title

Data Science Course | Data Science Courses | Intellipaat

description

🔥Intellipaat Data Science course: https://intellipaat.com/data-scientist-course-training/
Data Science tutorial will help you learn what is Data Science and master the foundations of data science, data sourcing, coding, mathematics, and statistics. In this complete Data Science courses video, you will get to understand the concepts of Data Science like Supervised Learning which includes data manipulation, Data Visualization, Linear Regression, Logistic Regression, Decision Tree, Random Forest. You will also learn about Unsupervised Learning which includes k-means clustering, user-based & item-based filtering, association rule mining, etc among others. At the end of the data science tutorial video, there are some most commonly asked Data Science interview questions by top MNCs to make you job-ready.
#datasciencecourse #datasciencecourses #datasciencetutorial #datasciencetutorialforbeginners
📝Following topics are covered in this data science courses video:
00:00 - Data Science tutorial
01:43 - what is data science
05:09 - languages for data science
16:37 - what is linear regression
32:39 - what is regression
34:45 - logistic regression
53:29 - performance metrics
01:15:09 - decision tree
03:56:02 - k means clustering
05:45:42 - recommendation engine
05:45:53 - collaborative filtering
06:06:35 - user based collaborative filtering
06:27:33 - item based collaborative filtering
07:29:24 - association rule mining
08:11:29 - Data Science Interview Questions
📰Interested to learn Data Science still more? Please check similar "what is Data Science" blog here: https://intellipaat.com/blog/what-is-data-science/
🔗Watch complete Data Science tutorials here:- https://goo.gl/BGTpv5
Are you looking for something more? Enroll in our Data Science course and become a certified Data Scientist (https://intellipaat.com/data-scientist-course-training/). It is a 40 hrs instructor led Data Science training provided by Intellipaat which is completely aligned with industry standards and certification bodies.
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Why should you watch this Data Science tutorial?
You can learn Data Science much faster than any other technology and this Introduction to Data Science tutorial helps you do just that. Data Science is one of the best technological advances that is finding increased applications for machine learning and in a lot of industry domains. We are offering the top Data Science tutorial to gain knowledge in Data Science. Our Data Science course has been created with extensive inputs from the industry experts so that you can learn Data Science training and apply it for real world scenarios.
Who should watch this Data Science tutorial video?
If you want to learn what is Data Science to become a Data Scientist then this Intellipaat Data Science tutorial is for you. The Intellipaat Data Science video is your first step to learn Data Science. Since this Data Science tutorial video can be taken by anybody, so if you are a beginner in technology then you can also enroll for Data Science training to take your skills to the next level.
Why Data Science is important?
Data Science is taking over each and every industry domain. Machine Learning and especially Deep Learning are the most important aspects of Data Science that are being deployed everywhere from search engines to online movie recommendations. Taking the Intellipaat Data Science training & Data Science Course can help professionals to build a solid career in a rising technology domain and get the best jobs in top organizations.
Why should you opt for a Data Science career?
If you want to fast-track your career then you should strongly consider Data Science. The reason for this is that it is one of the fastest growing technology. There is a huge demand for Data Scientist. The salaries for Data Scientist is fantastic.There is a huge growth opportunity in this domain as well. Hence this Intellipaat Data Science with r tutorial is your stepping stone to a successful career!
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{'title': 'Data Science Course | Data Science Courses | Intellipaat', 'heatmap': [{'end': 1349.466, 'start': 1011.814, 'weight': 1}, {'end': 3380.489, 'start': 3035.303, 'weight': 0.712}], 'summary': 'Covers various chapters on data science, including data science careers, logistic regression, decision trees, random forests, k-means clustering, missing value imputation, unsupervised learning, collaborative filtering, association rule mining, neural networks, pca, heart disease prediction, and building predictive models, with practical examples and achievements like 94.34% accuracy in random forest model optimization and 96% accuracy rate in predictive models.', 'chapters': [{'end': 288.221, 'segs': [{'end': 91.268, 'src': 'embed', 'start': 58.331, 'weight': 3, 'content': [{'end': 63.494, 'text': "Following which we'll start with our first machine learning algorithm, which is linear regression,", 'start': 58.331, 'duration': 5.163}, {'end': 68.456, 'text': "and then we'll learn how to do binary classification with the logistic regression algorithm.", 'start': 63.494, 'duration': 4.962}, {'end': 74.579, 'text': "Going ahead, we'll learn about decision tree and random forest, which are basically tree-based classifiers.", 'start': 68.836, 'duration': 5.743}, {'end': 78.501, 'text': "So, once we're done with all of these supervised learning algorithms,", 'start': 74.98, 'duration': 3.521}, {'end': 83.124, 'text': "we'll learn how to do unsupervised learning with the help of k-means clustering algorithm.", 'start': 78.501, 'duration': 4.623}, {'end': 91.268, 'text': "After that we'll learn about user-based collaborative filtering and item-based collaborative filtering, which are basically recommendation techniques.", 'start': 83.584, 'duration': 7.684}], 'summary': 'Introduces various machine learning algorithms including linear regression, logistic regression, decision tree, random forest, k-means clustering, and collaborative filtering.', 'duration': 32.937, 'max_score': 58.331, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ858331.jpg'}, {'end': 159.157, 'src': 'embed', 'start': 121.582, 'weight': 0, 'content': [{'end': 130.07, 'text': 'So data science is using some tools and techniques which help you to manipulate or wrangle the data so that you can find something new and meaningful.', 'start': 121.582, 'duration': 8.488}, {'end': 137.208, 'text': 'Now, what if I gave you a really long spreadsheet containing all the sales figures for the past three years??', 'start': 131.266, 'duration': 5.942}, {'end': 140.43, 'text': "It would be difficult for you to comprehend the data, wouldn't it??", 'start': 137.809, 'duration': 2.621}, {'end': 146.972, 'text': 'So, instead of the spreadsheet, what if I gave you some charts and graphs related to annual sales??', 'start': 141.07, 'duration': 5.902}, {'end': 150.714, 'text': 'You would obviously prefer the graphs over the spreadsheet, right?', 'start': 147.592, 'duration': 3.122}, {'end': 153.535, 'text': 'This again is data science, my friend.', 'start': 151.514, 'duration': 2.021}, {'end': 159.157, 'text': 'Visualizing the data helps you get a better perspective and understand it easily.', 'start': 154.155, 'duration': 5.002}], 'summary': 'Data science uses tools to manipulate data for meaningful insights, such as visualizing sales data for better understanding.', 'duration': 37.575, 'max_score': 121.582, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ8121582.jpg'}, {'end': 256.748, 'src': 'embed', 'start': 227.972, 'weight': 6, 'content': [{'end': 234.495, 'text': 'In such a scenario, how does a company retain its users? So this is where data science plays a pivotal role.', 'start': 227.972, 'duration': 6.523}, {'end': 240.399, 'text': 'The data scientists or the data analysts go through the data to understand customer behavior.', 'start': 235.156, 'duration': 5.243}, {'end': 247.103, 'text': 'They do a thorough analysis of data usage patterns, social media activity and voice call or SMS patterns.', 'start': 241.019, 'duration': 6.084}, {'end': 256.748, 'text': 'The data scientists also analyze customer demographics so that proper segregation can be done in terms of age, gender or geographic location.', 'start': 247.763, 'duration': 8.985}], 'summary': 'Data science helps retain users by analyzing usage patterns, social media activity, and demographics.', 'duration': 28.776, 'max_score': 227.972, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ8227972.jpg'}], 'start': 3.103, 'title': 'Data science careers & tools', 'summary': 'Discusses the high demand and salaries for data science jobs, citing a shortage of 1.5 million data scientists in the us and a 400 times growth in job postings in india. it also covers the applications of data science in decision making, predictive analysis, and industries like telecom, emphasizing customer retention and fraud detection.', 'chapters': [{'end': 97.071, 'start': 3.103, 'title': 'Data science: hot job & career growth', 'summary': 'Sheds light on the increasing demand and lucrative salary of data science jobs, with the united states facing a shortage of 1.5 million data scientists and india experiencing a 400 times growth in job postings for data science profile.', 'duration': 93.968, 'highlights': ['The United States faces a shortage of 1.5 million data scientists. According to McKinsey, the United States alone faces a job shortage of 1.5 million data scientists.', 'India has seen a 400 times growth in job postings for data science profile in the past one year. In India, according to Economic Times, the job postings for data science profile have grown over 400 times in the past one year.', 'Average salary of a data scientist is $123,000 per year. Data science is one of the hottest jobs of the 21st century, with an average salary of $123,000 per year.']}, {'end': 288.221, 'start': 97.511, 'title': 'Data science: tools and techniques', 'summary': 'Explores the concept of data science, its applications in decision making and predictive analysis, and its pivotal role in industries such as telecom, with a focus on customer retention and fraud detection.', 'duration': 190.71, 'highlights': ['Data science involves using tools and techniques to manipulate and visualize data, enabling meaningful insights. Data science enables manipulation and visualization of data to derive new and meaningful insights, making it a crucial aspect of decision-making processes.', 'Predictive analysis using data science concepts allows for the prediction of future events based on current data, aiding in decision-making processes. Data science concepts can be used to perform predictive analysis, offering the potential to predict future events based on current data, facilitating informed decision-making.', 'In the telecom industry, data science is utilized for customer retention through thorough analysis of data usage patterns, social media activity, demographics, and offering personalized solutions. Data science is instrumental in the telecom industry for customer retention by analyzing data usage patterns, social media activity, demographics, and tailoring personalized offers to retain customers.', 'Data science plays a crucial role in fraud detection by analyzing and identifying unusual transaction patterns, enabling proactive measures to prevent fraudulent activities. Data science is pivotal in fraud detection by analyzing and identifying unusual transaction patterns, allowing for proactive measures to prevent fraudulent activities and ensure customer security.']}], 'duration': 285.118, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ83103.jpg', 'highlights': ['The United States faces a shortage of 1.5 million data scientists. According to McKinsey, the United States alone faces a job shortage of 1.5 million data scientists.', 'India has seen a 400 times growth in job postings for data science profile in the past one year. In India, according to Economic Times, the job postings for data science profile have grown over 400 times in the past one year.', 'Average salary of a data scientist is $123,000 per year. Data science is one of the hottest jobs of the 21st century, with an average salary of $123,000 per year.', 'Data science involves using tools and techniques to manipulate and visualize data, enabling meaningful insights. Data science enables manipulation and visualization of data to derive new and meaningful insights, making it a crucial aspect of decision-making processes.', 'Predictive analysis using data science concepts allows for the prediction of future events based on current data, aiding in decision-making processes. Data science concepts can be used to perform predictive analysis, offering the potential to predict future events based on current data, facilitating informed decision-making.', 'In the telecom industry, data science is utilized for customer retention through thorough analysis of data usage patterns, social media activity, demographics, and offering personalized solutions. Data science is instrumental in the telecom industry for customer retention by analyzing data usage patterns, social media activity, demographics, and tailoring personalized offers to retain customers.', 'Data science plays a crucial role in fraud detection by analyzing and identifying unusual transaction patterns, enabling proactive measures to prevent fraudulent activities. Data science is pivotal in fraud detection by analyzing and identifying unusual transaction patterns, allowing for proactive measures to prevent fraudulent activities and ensure customer security.']}, {'end': 2220.161, 'segs': [{'end': 316.048, 'src': 'embed', 'start': 289.121, 'weight': 1, 'content': [{'end': 295.579, 'text': 'Now, how did the bank know that it was a fraudulent transaction? Well, data science again folks.', 'start': 289.121, 'duration': 6.458}, {'end': 301.125, 'text': "The bank keeps a check on your purchase pattern and whenever there's a deviance in that pattern,", 'start': 296.28, 'duration': 4.845}, {'end': 304.769, 'text': 'it flags it off as an anomaly and immediately notifies you.', 'start': 301.125, 'duration': 3.644}, {'end': 307.572, 'text': 'All of this, courtesy data science.', 'start': 305.47, 'duration': 2.102}, {'end': 312.317, 'text': "Let's look at some languages to implement data science concepts.", 'start': 309.494, 'duration': 2.823}, {'end': 316.048, 'text': 'First in the list is R.', 'start': 313.527, 'duration': 2.521}], 'summary': 'Bank uses data science to detect fraudulent transactions by monitoring purchase patterns and immediately flagging anomalies.', 'duration': 26.927, 'max_score': 289.121, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ8289121.jpg'}, {'end': 387.806, 'src': 'embed', 'start': 360.33, 'weight': 5, 'content': [{'end': 364.553, 'text': "And we'll be working with the Diamonds dataset to implement the data science concepts.", 'start': 360.33, 'duration': 4.223}, {'end': 366.635, 'text': "So let's head to RStudio.", 'start': 365.134, 'duration': 1.501}, {'end': 369.336, 'text': 'So this is RStudio, guys.', 'start': 367.896, 'duration': 1.44}, {'end': 370.978, 'text': 'This is how RStudio looks like.', 'start': 369.657, 'duration': 1.321}, {'end': 374.418, 'text': "So we'll start off by data manipulation.", 'start': 372.156, 'duration': 2.262}, {'end': 380.742, 'text': 'And since you would want to work with the diamonds data set, we would need to load the ggplot2 package.', 'start': 375.218, 'duration': 5.524}, {'end': 384.744, 'text': 'To load a package in R, all we have to use is the library function.', 'start': 381.302, 'duration': 3.442}, {'end': 387.806, 'text': "So I'll say library of ggplot2 to load the package.", 'start': 385.044, 'duration': 2.762}], 'summary': 'Implementing data science concepts using the diamonds dataset in rstudio.', 'duration': 27.476, 'max_score': 360.33, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ8360330.jpg'}, {'end': 477.208, 'src': 'embed', 'start': 450.97, 'weight': 6, 'content': [{'end': 455.272, 'text': "So now it's finally time to manipulate this data and get some insights from it.", 'start': 450.97, 'duration': 4.302}, {'end': 464.138, 'text': 'Now from this data set I would want to filter out only those diamonds where the cut is ideal.', 'start': 455.852, 'duration': 8.286}, {'end': 471.604, 'text': 'So over here we saw that the cut or the quality of the cut can be fair, good, very good, premium or ideal.', 'start': 464.679, 'duration': 6.925}, {'end': 475.046, 'text': 'Now I would want only those diamonds which have the ideal cut.', 'start': 472.064, 'duration': 2.982}, {'end': 477.208, 'text': 'So this is how I can do it.', 'start': 475.927, 'duration': 1.281}], 'summary': 'Filter diamonds by ideal cut for insights.', 'duration': 26.238, 'max_score': 450.97, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ8450970.jpg'}, {'end': 842.074, 'src': 'embed', 'start': 811.936, 'weight': 7, 'content': [{'end': 816.502, 'text': "So ggplot is the function where I'm giving the data set over here.", 'start': 811.936, 'duration': 4.566}, {'end': 823.992, 'text': 'The data set is diamonds and I would want to plot a bar plot with respect to the cut of the diamonds.', 'start': 816.522, 'duration': 7.47}, {'end': 830.426, 'text': 'This is the bar plot with respect to cut.', 'start': 827.864, 'duration': 2.562}, {'end': 834.989, 'text': 'So this function basically takes two arguments over here.', 'start': 831.447, 'duration': 3.542}, {'end': 836.17, 'text': 'First is the dataset.', 'start': 835.109, 'duration': 1.061}, {'end': 842.074, 'text': 'Next is the aesthetics onto which we are going to map the columns of the diamond dataset.', 'start': 836.67, 'duration': 5.404}], 'summary': 'Using ggplot to create a bar plot for diamond cuts.', 'duration': 30.138, 'max_score': 811.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ8811936.jpg'}, {'end': 1005.229, 'src': 'embed', 'start': 976.7, 'weight': 0, 'content': [{'end': 979.461, 'text': 'Now we are assigning the color medium orchid 4 to it.', 'start': 976.7, 'duration': 2.761}, {'end': 987.693, 'text': 'This is a similar plot to the previous one.', 'start': 985.751, 'duration': 1.942}, {'end': 994.799, 'text': 'So again what we can conclude is as the length of the diamond increases the price of the diamond would also increase.', 'start': 988.073, 'duration': 6.726}, {'end': 1005.229, 'text': 'So linear regression is a predictive modeling technique which is used whenever there is a linear relationship between the independent and dependent variables,', 'start': 996.401, 'duration': 8.828}], 'summary': 'Linear regression predicts diamond price based on length.', 'duration': 28.529, 'max_score': 976.7, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ8976700.jpg'}, {'end': 1349.466, 'src': 'heatmap', 'start': 1011.814, 'weight': 1, 'content': [{'end': 1018.319, 'text': "Like over here, we have a flower's sepal length mapped onto the x-axis and petal length mapped onto the y-axis.", 'start': 1011.814, 'duration': 6.505}, {'end': 1024.805, 'text': 'And we are trying to understand how does petal length change with respect to sepal length with the help of linear regression.', 'start': 1018.8, 'duration': 6.005}, {'end': 1028.988, 'text': "So let's have a better understanding of linear regression with this example over here.", 'start': 1025.726, 'duration': 3.262}, {'end': 1031.79, 'text': "So let's say there's a telecom network called as NEO.", 'start': 1029.509, 'duration': 2.281}, {'end': 1040.781, 'text': "And the delivery manager of the company wants to find out if there's a relationship between the monthly charges of the customer and the tenure of the customer.", 'start': 1032.375, 'duration': 8.406}, {'end': 1052.932, 'text': 'So he collects all of the customer data and implements the linear regression algorithm by taking monthly charges as the dependent variable and tenure as the independent variable.', 'start': 1041.943, 'duration': 10.989}, {'end': 1062.031, 'text': 'And after implementing the algorithm, what we understand is there is a linear relationship between the monthly charges and the tenure of the customer.', 'start': 1053.467, 'duration': 8.564}, {'end': 1066.873, 'text': 'So as the tenure of the customer increases, his monthly charges would also increase.', 'start': 1062.671, 'duration': 4.202}, {'end': 1072.876, 'text': 'Now the best fit line helps the delivery manager to find out interesting insights from the data.', 'start': 1067.554, 'duration': 5.322}, {'end': 1078.259, 'text': 'With this, he can predict the values of Y for every new value of X.', 'start': 1073.296, 'duration': 4.963}, {'end': 1080.18, 'text': "So let's say the tenure of the customer is 45 months.", 'start': 1078.259, 'duration': 1.921}, {'end': 1088.45, 'text': 'then with the help of the best fit line, he can predict that his monthly charges would be somewhere around $64.', 'start': 1081.047, 'duration': 7.403}, {'end': 1095.433, 'text': "Similarly, if the customer's tenure is 69 months, then his monthly charges would be around $110.", 'start': 1088.45, 'duration': 6.983}, {'end': 1097.594, 'text': 'So this is how linear regression works.', 'start': 1095.433, 'duration': 2.161}, {'end': 1104.616, 'text': "Now that you've understood what exactly is linear regression, let's go ahead and understand how can we find the best fit line.", 'start': 1098.534, 'duration': 6.082}, {'end': 1110.839, 'text': 'So this time we are trying to fit a linear line between the age of an employee and his salary.', 'start': 1105.497, 'duration': 5.342}, {'end': 1115.118, 'text': 'So the line could either be this, this, or this.', 'start': 1111.437, 'duration': 3.681}, {'end': 1118.4, 'text': 'So how do we know which of these is the best headline?', 'start': 1115.919, 'duration': 2.481}, {'end': 1121.02, 'text': 'There could be infinite possibilities, right?', 'start': 1118.88, 'duration': 2.14}, {'end': 1124.982, 'text': 'So this is where we need to have a look at the residual values.', 'start': 1121.941, 'duration': 3.041}, {'end': 1128.059, 'text': 'So this red line which you see over here.', 'start': 1125.717, 'duration': 2.342}, {'end': 1133.945, 'text': 'this denotes the residual value, which is nothing but the difference between the actual values and the predicted values.', 'start': 1128.059, 'duration': 5.886}, {'end': 1138.73, 'text': 'Now to find out the best fit line, we have something known as residual sum of squares.', 'start': 1134.606, 'duration': 4.124}, {'end': 1144.997, 'text': 'So in residual sum of squares, we take the square of all the residuals and then we sum them up.', 'start': 1139.351, 'duration': 5.646}, {'end': 1148.861, 'text': 'And this gives us the value of residual sum of squares.', 'start': 1145.537, 'duration': 3.324}, {'end': 1155.994, 'text': 'And whichever line has the lowest value of residual sum of squares, it would be considered as the best fit line.', 'start': 1149.568, 'duration': 6.426}, {'end': 1164.062, 'text': "So now we'll learn how the coefficient of x influences the relationship between independent variable and the dependent variable.", 'start': 1156.875, 'duration': 7.187}, {'end': 1170.032, 'text': 'So if it is simple, linear regression and value of coefficient of x is greater than zero,', 'start': 1164.83, 'duration': 5.202}, {'end': 1174.673, 'text': 'then the relationship between independent and dependent variables would be positive.', 'start': 1170.032, 'duration': 4.641}, {'end': 1179.235, 'text': 'That is, as the value of x increases, the value of y would also increase.', 'start': 1175.194, 'duration': 4.041}, {'end': 1186.958, 'text': 'And if the coefficient of x is lower than zero, then the relationship between independent and response variables would be negative.', 'start': 1179.935, 'duration': 7.023}, {'end': 1192.02, 'text': 'That is, as the value of x increases, the value of y would decrease.', 'start': 1187.398, 'duration': 4.622}, {'end': 1198.732, 'text': 'Right. So when multiple linear regression we have more than one independent variable and we try to determine,', 'start': 1192.92, 'duration': 5.812}, {'end': 1202.773, 'text': 'how do all of this independent variables together affect the dependent variable?', 'start': 1198.732, 'duration': 4.041}, {'end': 1212.596, 'text': 'Like over here we have a mapping between y, x1, x2 and x3 where y is the dependent variable and x1, x2 and x3 are the independent variables.', 'start': 1203.413, 'duration': 9.183}, {'end': 1217.357, 'text': "So let's take this example to have a better understanding of multiple linear regression.", 'start': 1213.396, 'duration': 3.961}, {'end': 1222.711, 'text': 'So over here we are trying to understand what factors affect the salary of an employee.', 'start': 1218.089, 'duration': 4.622}, {'end': 1229.153, 'text': 'Here salary is the dependent variable and gender, age and department are the independent variables.', 'start': 1223.311, 'duration': 5.842}, {'end': 1239.397, 'text': 'So this linear regression model helps us to determine the salary of an employee when specific values are given to age, gender and department.', 'start': 1229.833, 'duration': 9.564}, {'end': 1243.078, 'text': "So let's go to RStudio and implement multiple linear regression.", 'start': 1240.117, 'duration': 2.961}, {'end': 1250, 'text': 'right. we have our studio right in front of us and this is our same old customer churn data set all right.', 'start': 1243.956, 'duration': 6.044}, {'end': 1257.484, 'text': 'so now, since we already know that before building a linear regression model, we need to divide our data set into training and testing sets,', 'start': 1250, 'duration': 7.484}, {'end': 1260.707, 'text': 'and to do that we would require the CA tools package.', 'start': 1257.484, 'duration': 3.223}, {'end': 1263.728, 'text': "so I'll type library of CA tools.", 'start': 1260.707, 'duration': 3.021}, {'end': 1266.25, 'text': 'so we have loaded the CA tools package.', 'start': 1263.728, 'duration': 2.522}, {'end': 1269.172, 'text': 'so now to build a linear regression model,', 'start': 1266.25, 'duration': 2.922}, {'end': 1277.978, 'text': "I will take the tenure column as the dependent variable and I'll try to understand how do other columns affect the tenure of a customer right.", 'start': 1269.172, 'duration': 8.806}, {'end': 1281.24, 'text': "so I'll split this data set with respect to the tenure column.", 'start': 1277.978, 'duration': 3.262}, {'end': 1284.482, 'text': 'so I will use the sample dot split function.', 'start': 1281.24, 'duration': 3.242}, {'end': 1301.273, 'text': "now let me select this column customer churn dollar tenure right and the split ratio which I'll be giving is 0.65 and I will store this in an object called as split model.", 'start': 1284.482, 'duration': 16.791}, {'end': 1310.644, 'text': 'Okay, so basically, 65% of the observations would get true values and the rest 35 observations would get the false values.', 'start': 1302.719, 'duration': 7.925}, {'end': 1317.107, 'text': "So, now that we've stored this result in split model, I will divide the data set using the subset function right?", 'start': 1311.544, 'duration': 5.563}, {'end': 1320.829, 'text': 'Now this takes in the first parameter as the data set.', 'start': 1317.668, 'duration': 3.161}, {'end': 1332.136, 'text': 'Now from this data set, wherever the value of split model is equal to true, I will store all of those observations in the training set.', 'start': 1321.67, 'duration': 10.466}, {'end': 1337.157, 'text': 'Right. Similarly, from the entire customer churn data set.', 'start': 1332.833, 'duration': 4.324}, {'end': 1346.164, 'text': "wherever the value of split model is false, I'll select all of those observations and I will store those observations in a data set called as test.", 'start': 1337.157, 'duration': 9.007}, {'end': 1349.466, 'text': 'And thus we have our training and testing sets ready.', 'start': 1347.125, 'duration': 2.341}], 'summary': 'Linear regression used to predict customer charges based on tenure, with example of multiple linear regression using employee salary factors.', 'duration': 337.652, 'max_score': 1011.814, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ81011814.jpg'}, {'end': 1052.932, 'src': 'embed', 'start': 1025.726, 'weight': 8, 'content': [{'end': 1028.988, 'text': "So let's have a better understanding of linear regression with this example over here.", 'start': 1025.726, 'duration': 3.262}, {'end': 1031.79, 'text': "So let's say there's a telecom network called as NEO.", 'start': 1029.509, 'duration': 2.281}, {'end': 1040.781, 'text': "And the delivery manager of the company wants to find out if there's a relationship between the monthly charges of the customer and the tenure of the customer.", 'start': 1032.375, 'duration': 8.406}, {'end': 1052.932, 'text': 'So he collects all of the customer data and implements the linear regression algorithm by taking monthly charges as the dependent variable and tenure as the independent variable.', 'start': 1041.943, 'duration': 10.989}], 'summary': 'Telecom company uses linear regression to analyze monthly charges and customer tenure.', 'duration': 27.206, 'max_score': 1025.726, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ81025726.jpg'}, {'end': 1095.433, 'src': 'embed', 'start': 1062.671, 'weight': 9, 'content': [{'end': 1066.873, 'text': 'So as the tenure of the customer increases, his monthly charges would also increase.', 'start': 1062.671, 'duration': 4.202}, {'end': 1072.876, 'text': 'Now the best fit line helps the delivery manager to find out interesting insights from the data.', 'start': 1067.554, 'duration': 5.322}, {'end': 1078.259, 'text': 'With this, he can predict the values of Y for every new value of X.', 'start': 1073.296, 'duration': 4.963}, {'end': 1080.18, 'text': "So let's say the tenure of the customer is 45 months.", 'start': 1078.259, 'duration': 1.921}, {'end': 1088.45, 'text': 'then with the help of the best fit line, he can predict that his monthly charges would be somewhere around $64.', 'start': 1081.047, 'duration': 7.403}, {'end': 1095.433, 'text': "Similarly, if the customer's tenure is 69 months, then his monthly charges would be around $110.", 'start': 1088.45, 'duration': 6.983}], 'summary': 'As tenure increases, monthly charges rise; e.g. 45 months predicts $64, 69 months predicts $110.', 'duration': 32.762, 'max_score': 1062.671, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ81062671.jpg'}, {'end': 1174.673, 'src': 'embed', 'start': 1149.568, 'weight': 10, 'content': [{'end': 1155.994, 'text': 'And whichever line has the lowest value of residual sum of squares, it would be considered as the best fit line.', 'start': 1149.568, 'duration': 6.426}, {'end': 1164.062, 'text': "So now we'll learn how the coefficient of x influences the relationship between independent variable and the dependent variable.", 'start': 1156.875, 'duration': 7.187}, {'end': 1170.032, 'text': 'So if it is simple, linear regression and value of coefficient of x is greater than zero,', 'start': 1164.83, 'duration': 5.202}, {'end': 1174.673, 'text': 'then the relationship between independent and dependent variables would be positive.', 'start': 1170.032, 'duration': 4.641}], 'summary': 'Finding best fit line with lowest residual sum of squares and understanding how coefficient of x influences the relationship in simple linear regression.', 'duration': 25.105, 'max_score': 1149.568, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ81149568.jpg'}, {'end': 1834.183, 'src': 'embed', 'start': 1807.202, 'weight': 11, 'content': [{'end': 1810.545, 'text': "now. i'll bind this error back to the same data set.", 'start': 1807.202, 'duration': 3.343}, {'end': 1818.291, 'text': 'so c bind of final data 2, and i will bind the error back to the same data set.', 'start': 1810.545, 'duration': 7.746}, {'end': 1824.095, 'text': 'okay, and i will store it back to the same data set, which will be final two.', 'start': 1818.291, 'duration': 5.804}, {'end': 1827.978, 'text': "so, guys, these are the actual values of the customer's tenure.", 'start': 1824.095, 'duration': 3.883}, {'end': 1834.183, 'text': "these are the predicted values of the customer's tenure and this column gives us the error in prediction.", 'start': 1827.978, 'duration': 6.205}], 'summary': 'Binding error to data set, final data 2, and storing back as final two. analyzing actual vs predicted customer tenure values and error in prediction.', 'duration': 26.981, 'max_score': 1807.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ81807202.jpg'}, {'end': 2040.297, 'src': 'embed', 'start': 1996.409, 'weight': 13, 'content': [{'end': 2002.371, 'text': "We are trying to understand how does a person's age affect his salary based on the historical data.", 'start': 1996.409, 'duration': 5.962}, {'end': 2007.712, 'text': 'So over here, salary is the dependent variable and age is the independent variable.', 'start': 2003.011, 'duration': 4.701}, {'end': 2012.373, 'text': "That is, you're trying to ascertain the salary of the employee with respect to the age.", 'start': 2008.252, 'duration': 4.121}, {'end': 2014.713, 'text': "Let's look at the second scenario.", 'start': 2013.573, 'duration': 1.14}, {'end': 2018.39, 'text': 'Here we have two students, Rachel and Ross.', 'start': 2015.509, 'duration': 2.881}, {'end': 2023.852, 'text': 'They appear for an exam and Rachel manages to pass the exam while Ross fails.', 'start': 2019.01, 'duration': 4.842}, {'end': 2028.913, 'text': "Now, what if another student, let's say Monica, takes the same test?", 'start': 2024.612, 'duration': 4.301}, {'end': 2030.814, 'text': 'Would she be able to clear the exam??', 'start': 2029.433, 'duration': 1.381}, {'end': 2039.677, 'text': "Well, you'll again look at the data provided to you and see that Rachel, being a girl, was able to pass the exam, while Ross, being a guy,", 'start': 2031.334, 'duration': 8.343}, {'end': 2040.297, 'text': 'failed to clear it.', 'start': 2039.677, 'duration': 0.62}], 'summary': "Analyzing age's impact on salary; also comparing exam results by gender.", 'duration': 43.888, 'max_score': 1996.409, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ81996409.jpg'}], 'start': 289.121, 'title': 'Data science in banking', 'summary': 'Explores bank fraud detection using r, python, and java, data manipulation with the diamonds dataset, data analysis and visualization of diamond characteristics, linear regression for customer tenure and employee salary, building and evaluating linear regression models, and understanding regression and logistic regression with practical examples.', 'chapters': [{'end': 413.255, 'start': 289.121, 'title': 'Bank fraud detection and data science languages', 'summary': 'Discusses how banks use data science to detect fraudulent transactions, highlighting the role of r, python, and java in data science tasks and demonstrating data manipulation using r and the diamonds dataset.', 'duration': 124.134, 'highlights': ['Banks use data science to detect fraudulent transactions by monitoring purchase patterns and flagging deviations as anomalies, courtesy of data science. bank fraud detection, monitoring purchase patterns, flagging anomalies, data science', 'R is the most widely used language for data science tasks, providing over 10,000 packages for data visualization, manipulation, machine learning, and statistical analysis. R language, 10,000+ packages, data visualization, machine learning, statistical analysis', 'Python is in close competition with R, offering packages for deep learning like Keras and TensorFlow, facilitating the creation of deep neural networks. Python language, deep learning packages, Keras, TensorFlow, deep neural networks', 'Java is used for data science tasks due to its speed and scalability with big data. Java language, speed, scalability, big data', 'Demonstration of data manipulation in R using packages like ggplot2 and dplyr, with a focus on loading and viewing the Diamonds dataset in RStudio. data manipulation in R, ggplot2, dplyr, loading Diamonds dataset, RStudio']}, {'end': 952.419, 'start': 414.156, 'title': 'Data analysis and visualization', 'summary': 'Demonstrates data manipulation techniques on a dataset of 54,000 diamonds, including filtering by cut quality and price, selecting specific columns, and bivariate analysis with ggplot2, resulting in insights such as 21,551 diamonds with ideal cut, 531 diamonds priced over $15,000, and a scatter plot showing the relationship between carat size and diamond price.', 'duration': 538.263, 'highlights': ['21,551 diamonds are observed to have the ideal cut, showcasing a significant subset of the dataset based on cut quality. The chapter filters out 21,551 diamonds with the ideal cut from the dataset, demonstrating a significant subset based on cut quality.', '531 diamonds are identified with a price exceeding $15,000, providing insight into the high-end segment of the diamond market. By filtering out diamonds with a price exceeding $15,000, the chapter provides insight into the high-end segment of the diamond market.', 'The scatter plot reveals the relationship between carat size and diamond price, visually demonstrating the positive correlation between the two variables. The chapter utilizes a scatter plot to visually demonstrate the positive correlation between carat size and diamond price, providing a clear insight into the relationship between the two variables.']}, {'end': 1366.284, 'start': 953.44, 'title': 'Linear regression analysis', 'summary': 'Discusses the use of linear regression in analyzing relationships between variables, such as predicting the increase in monthly charges with customer tenure and finding the best fit line using residual sum of squares. it also covers multiple linear regression and its application to determine the factors affecting employee salary.', 'duration': 412.844, 'highlights': ["Linear regression predicts the increase in monthly charges with customer tenure, allowing the delivery manager to make predictions, such as estimating a customer's monthly charges at 45 months tenure to be around $64 and at 69 months tenure to be around $110. Linear regression is used to predict the increase in monthly charges with customer tenure, enabling predictions of specific values, e.g., monthly charges at 45 months tenure to be around $64 and at 69 months tenure to be around $110.", 'The use of residual sum of squares to find the best fit line, determining the line with the lowest residual sum of squares as the most suitable fit. The chapter explains the use of residual sum of squares to find the best fit line, selecting the line with the lowest residual sum of squares as the most suitable fit.', 'Introduction to multiple linear regression and its application in determining the factors affecting employee salary by considering independent variables such as gender, age, and department. The chapter introduces multiple linear regression and its application in determining the factors affecting employee salary, with consideration of independent variables including gender, age, and department.']}, {'end': 1942.016, 'start': 1366.284, 'title': 'Building linear regression models', 'summary': 'Covers building and evaluating two linear regression models to predict tenure, with the second model achieving a lower rmse of 12.80, indicating its superior accuracy compared to the first model with an rmse of 16.', 'duration': 575.732, 'highlights': ['The second linear regression model achieved a lower RMSE of 12.80, indicating its superior accuracy compared to the first model with an RMSE of 16.', 'The independent variables for the first model were monthly charges, gender, internet service, and contract, while for the second model, they were partner, phone service, total charges, and payment method.', 'The RMSE value for the second model was affected by NA values in the total charges column, which were handled using NA.RM equals true, resulting in an improved RMSE of 12.80.']}, {'end': 2220.161, 'start': 1942.837, 'title': 'Understanding regression and logistic regression', 'summary': 'Explains multiple linear regression, simple regression, and logistic regression through examples of predicting salary based on age, passing an exam based on gender, and determining the probability of rain. logistic regression introduces the concept of categorical dependent variables and the probability of an observation belonging to a particular category.', 'duration': 277.324, 'highlights': ['The chapter explains the concept of regression through examples of predicting salary based on age and passing an exam based on gender. The examples of predicting salary based on age and passing an exam based on gender demonstrate regression in action, showcasing the relationship between independent and dependent variables.', 'Logistic regression is introduced as a technique for determining the probability of an observation belonging to a particular category, with examples such as predicting rain based on temperature and humidity. The introduction of logistic regression as a technique for determining the probability of an observation belonging to a particular category is exemplified through the example of predicting rain based on temperature and humidity.', 'The distinction between linear regression and logistic regression is explained, highlighting the difference in the dependent variable and the nature of the relationship between variables. The explanation of the distinction between linear regression and logistic regression emphasizes the difference in the dependent variable and the nature of the relationship between variables, with linear regression involving a continuous dependent variable and a linear relationship, while logistic regression involves a categorical dependent variable with only two values.']}], 'duration': 1931.04, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ8289121.jpg', 'highlights': ['Banks use data science to detect fraudulent transactions by monitoring purchase patterns and flagging deviations as anomalies, courtesy of data science.', 'R is the most widely used language for data science tasks, providing over 10,000 packages for data visualization, manipulation, machine learning, and statistical analysis.', 'Python is in close competition with R, offering packages for deep learning like Keras and TensorFlow, facilitating the creation of deep neural networks.', 'Java is used for data science tasks due to its speed and scalability with big data.', 'Demonstration of data manipulation in R using packages like ggplot2 and dplyr, with a focus on loading and viewing the Diamonds dataset in RStudio.', '21,551 diamonds are observed to have the ideal cut, showcasing a significant subset of the dataset based on cut quality.', '531 diamonds are identified with a price exceeding $15,000, providing insight into the high-end segment of the diamond market.', 'The scatter plot reveals the relationship between carat size and diamond price, visually demonstrating the positive correlation between the two variables.', 'Linear regression predicts the increase in monthly charges with customer tenure, enabling predictions of specific values, e.g., monthly charges at 45 months tenure to be around $64 and at 69 months tenure to be around $110.', 'The use of residual sum of squares to find the best fit line, selecting the line with the lowest residual sum of squares as the most suitable fit.', 'Introduction to multiple linear regression and its application in determining the factors affecting employee salary, with consideration of independent variables including gender, age, and department.', 'The second linear regression model achieved a lower RMSE of 12.80, indicating its superior accuracy compared to the first model with an RMSE of 16.', 'The examples of predicting salary based on age and passing an exam based on gender demonstrate regression in action, showcasing the relationship between independent and dependent variables.', 'Logistic regression is introduced as a technique for determining the probability of an observation belonging to a particular category, exemplified through the example of predicting rain based on temperature and humidity.', 'The explanation of the distinction between linear regression and logistic regression emphasizes the difference in the dependent variable and the nature of the relationship between variables, with linear regression involving a continuous dependent variable and a linear relationship, while logistic regression involves a categorical dependent variable with only two values.']}, {'end': 3057.936, 'segs': [{'end': 2272.712, 'src': 'embed', 'start': 2244.296, 'weight': 0, 'content': [{'end': 2247.019, 'text': "So let's head to R studio.", 'start': 2244.296, 'duration': 2.723}, {'end': 2249.641, 'text': 'Right, so this is how R studio looks like.', 'start': 2247.94, 'duration': 1.701}, {'end': 2252.704, 'text': "So let's have a glance at the empty cars data set first.", 'start': 2250.102, 'duration': 2.602}, {'end': 2254.926, 'text': "So I'll say view of empty cars.", 'start': 2253.264, 'duration': 1.662}, {'end': 2256.948, 'text': 'Right, this is our data set.', 'start': 2255.887, 'duration': 1.061}, {'end': 2258.329, 'text': "Now let's understand this properly.", 'start': 2257.008, 'duration': 1.321}, {'end': 2267.489, 'text': 'So this is our data set which has 32 observations or in other words we have 32 cars and these are the variables.', 'start': 2260.083, 'duration': 7.406}, {'end': 2270.411, 'text': 'So mpg is the miles per gallon.', 'start': 2267.949, 'duration': 2.462}, {'end': 2272.712, 'text': 'sill is the number of cylinders in car.', 'start': 2270.411, 'duration': 2.301}], 'summary': 'R studio: 32 cars with miles per gallon and cylinder data.', 'duration': 28.416, 'max_score': 2244.296, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ82244296.jpg'}, {'end': 2494.442, 'src': 'embed', 'start': 2468.716, 'weight': 1, 'content': [{'end': 2473.037, 'text': 'so the number of observations in empty cars data set were 32.', 'start': 2468.716, 'duration': 4.321}, {'end': 2475.917, 'text': 'thus the number of degrees of freedom is 31.', 'start': 2473.037, 'duration': 2.88}, {'end': 2484.66, 'text': 'Now, when we include another variable over here, the decrease of freedom reduces by 1 again, and we get it to be 30..', 'start': 2475.917, 'duration': 8.743}, {'end': 2492.182, 'text': 'So, basically, the point that I am trying to say over here is initially the null deviance, that is, when we are not including any variable,', 'start': 2484.66, 'duration': 7.522}, {'end': 2494.442, 'text': 'then the null deviance is 43..', 'start': 2492.182, 'duration': 2.26}], 'summary': 'The dataset initially had 32 observations and 43 null deviance.', 'duration': 25.726, 'max_score': 2468.716, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ82468716.jpg'}, {'end': 2621.268, 'src': 'embed', 'start': 2599.465, 'weight': 3, 'content': [{'end': 2608.975, 'text': 'right, and since we see that mpg has two stars over here, then it is significant, with a confidence interval of 99, right.', 'start': 2599.465, 'duration': 9.51}, {'end': 2610.817, 'text': 'so we have built the model over here.', 'start': 2608.975, 'duration': 1.842}, {'end': 2613.941, 'text': "now it's time to check the accuracy of the model.", 'start': 2610.817, 'duration': 3.124}, {'end': 2618.786, 'text': "so what we'll do is we'll predict this on some other data set.", 'start': 2613.941, 'duration': 4.845}, {'end': 2621.268, 'text': "so let's go ahead and predict these values.", 'start': 2618.786, 'duration': 2.482}], 'summary': 'Built a significant model with 99% confidence interval, checking accuracy and predicting values.', 'duration': 21.803, 'max_score': 2599.465, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ82599465.jpg'}, {'end': 2711.011, 'src': 'embed', 'start': 2683.906, 'weight': 2, 'content': [{'end': 2689.068, 'text': 'the miles per gallon value would start from 20 and then will go like 21,, 22,, 23 to 30..', 'start': 2683.906, 'duration': 5.162}, {'end': 2696.992, 'text': 'Now let me determine what would be the probability of the engine being v-type with respect to these values.', 'start': 2689.068, 'duration': 7.924}, {'end': 2711.011, 'text': 'right. so what we see over here is, as the mpg value increases from 20 to 30, we also see that the probability of the engine being v type increases.', 'start': 2698.288, 'duration': 12.723}], 'summary': 'Probability of v-type engine increases as mpg value goes from 20 to 30.', 'duration': 27.105, 'max_score': 2683.906, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ82683906.jpg'}, {'end': 2832.579, 'src': 'embed', 'start': 2805.22, 'weight': 4, 'content': [{'end': 2808.543, 'text': 'And similarly if we look at the null deviants over here.', 'start': 2805.22, 'duration': 3.323}, {'end': 2813.247, 'text': 'So null deviants is same that is when we are not including any variable in the formula.', 'start': 2808.563, 'duration': 4.684}, {'end': 2815.929, 'text': 'But when we go ahead and include the variable.', 'start': 2813.727, 'duration': 2.202}, {'end': 2822.209, 'text': 'So over here in the model 1, we included MPG and in model 2, we included HP.', 'start': 2816.604, 'duration': 5.605}, {'end': 2826.914, 'text': 'So when we included horsepower, there is a greater reduction in residual deviant.', 'start': 2822.67, 'duration': 4.244}, {'end': 2832.579, 'text': 'Or in other terms, we can say that HP is more significant than miles per gallon.', 'start': 2827.555, 'duration': 5.024}], 'summary': 'Including horsepower in the model leads to greater reduction in residual deviant, making it more significant than miles per gallon.', 'duration': 27.359, 'max_score': 2805.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ82805220.jpg'}], 'start': 2220.161, 'title': 'Logistic regression in r for car engine type', 'summary': 'Covers implementing logistic regression with r, focusing on the s curve for probability. it uses the empty cars data set with 32 observations and variables such as mpg. additionally, it discusses using logistic regression to determine car engine type and compares three models for accuracy and significance.', 'chapters': [{'end': 2270.411, 'start': 2220.161, 'title': 'Implementing logistic regression with r', 'summary': 'Covers the concept of logistic regression, focusing on the s curve for probability, and implements logistic regression with r using the empty cars data set containing 32 observations and variables such as miles per gallon (mpg).', 'duration': 50.25, 'highlights': ['The chapter covers the concept of logistic regression, focusing on the S curve for probability. Concept of logistic regression, S curve for probability', 'Implements logistic regression with R using the empty cars data set containing 32 observations and variables such as miles per gallon (mpg). Implementation of logistic regression with R, empty cars data set details']}, {'end': 3057.936, 'start': 2270.411, 'title': 'Logistic regression for car engine type', 'summary': 'Discusses the use of logistic regression to determine whether a car has a v-shaped or straight engine based on variables like miles per gallon and horsepower, building and analyzing three models to compare their accuracy and significance.', 'duration': 787.525, 'highlights': ['The AIC values for the three models are 20, 22, and 29, with the second model having the best AIC value, indicating it is the most accurate. Comparing the AIC values of the three models demonstrates that the second model is the most accurate, with an AIC value of 20.', 'The probability of the engine being V-type increases from 44% to 98% as the miles per gallon value increases from 20 to 30. As the miles per gallon value increases from 20 to 30, the probability of the engine being V-type increases from 44% to 98%.', 'The probability of the engine being V-type is just 12% when the horsepower is 150 units, indicating a low likelihood. When the horsepower is 150 units, the probability of the engine being V-type is only 12%, indicating a low likelihood.', 'Including both horsepower and miles per gallon into the same formula does not improve the accuracy of the model, as miles per gallon does not add value to the model when combined with horsepower. Including both horsepower and miles per gallon into the same formula does not improve the accuracy of the model, as miles per gallon does not add value to the model when combined with horsepower.']}], 'duration': 837.775, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ82220161.jpg', 'highlights': ['Implements logistic regression with R using the empty cars data set containing 32 observations and variables such as miles per gallon (mpg).', 'Concept of logistic regression, S curve for probability', 'Comparing the AIC values of the three models demonstrates that the second model is the most accurate, with an AIC value of 20.', 'As the miles per gallon value increases from 20 to 30, the probability of the engine being V-type increases from 44% to 98%.', 'When the horsepower is 150 units, the probability of the engine being V-type is only 12%, indicating a low likelihood.']}, {'end': 4492.957, 'segs': [{'end': 3158.239, 'src': 'embed', 'start': 3126.693, 'weight': 3, 'content': [{'end': 3128.475, 'text': "So let's start with true positives.", 'start': 3126.693, 'duration': 1.782}, {'end': 3135.201, 'text': 'So these are the cases in which the actual value is true and the predicted value is also true.', 'start': 3129.055, 'duration': 6.146}, {'end': 3142.508, 'text': 'That is, the patient has been diagnosed with cancer and the model also predicted that the patient has cancer.', 'start': 3135.742, 'duration': 6.766}, {'end': 3151.594, 'text': 'So next we have true negatives and these are the cases in which the actual value is false and the predicted value is also false.', 'start': 3143.348, 'duration': 8.246}, {'end': 3158.239, 'text': "That is actually the patient doesn't have cancer and the model also predicted that the patient doesn't have cancer.", 'start': 3152.014, 'duration': 6.225}], 'summary': 'Explaining true positives and true negatives in cancer diagnosis.', 'duration': 31.546, 'max_score': 3126.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ83126693.jpg'}, {'end': 3297.136, 'src': 'embed', 'start': 3272.225, 'weight': 1, 'content': [{'end': 3278.829, 'text': 'So the precision value is 0.76 and the next performance metric, as recall,', 'start': 3272.225, 'duration': 6.604}, {'end': 3285.133, 'text': 'and this helps us to get the proportion of those actual positives which were identified correctly.', 'start': 3278.829, 'duration': 6.304}, {'end': 3292.853, 'text': 'and we can get recall by dividing true positives with the sum of true positives and false negatives.', 'start': 3285.948, 'duration': 6.905}, {'end': 3297.136, 'text': 'and over here we have 100 true positives and 25 false negatives.', 'start': 3292.853, 'duration': 4.283}], 'summary': 'Precision value is 0.76, with 100 true positives and 25 false negatives for recall.', 'duration': 24.911, 'max_score': 3272.225, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ83272225.jpg'}, {'end': 3698.859, 'src': 'embed', 'start': 3663.378, 'weight': 0, 'content': [{'end': 3676.408, 'text': 'okay, so this time, let me check the accuracy there will be 1459 plus 175, which is the left diagonal divided by all of the values,', 'start': 3663.378, 'duration': 13.03}, {'end': 3687.49, 'text': 'which will be 1459 plus 175 plus three, five, two plus four, seven, nine.', 'start': 3676.408, 'duration': 11.082}, {'end': 3698.859, 'text': 'So this time the accuracy is 66% guys, okay? So when the threshold value is 0.3, then the accuracy which we get is 61%.', 'start': 3689.252, 'duration': 9.607}], 'summary': 'Accuracy is 66% without threshold and 61% with 0.3 threshold.', 'duration': 35.481, 'max_score': 3663.378, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ83663378.jpg'}, {'end': 4066.259, 'src': 'embed', 'start': 4033.399, 'weight': 4, 'content': [{'end': 4037.381, 'text': 'Now after this, we need to give the type of performance metric which we want.', 'start': 4033.399, 'duration': 3.982}, {'end': 4039.503, 'text': "So I'd want to get the accuracy.", 'start': 4037.902, 'duration': 1.601}, {'end': 4045.106, 'text': "So I'll type ACC over here and I will store this in an object called as ACC.", 'start': 4039.623, 'duration': 5.483}, {'end': 4047.447, 'text': 'Okay, now let me plot this.', 'start': 4045.886, 'duration': 1.561}, {'end': 4050.429, 'text': "So plot of ACC, let's see what do we get.", 'start': 4047.727, 'duration': 2.702}, {'end': 4058.576, 'text': 'So yes, this is what we get over here, right? So this is basically a plot with respect to accuracy and the cutoff.', 'start': 4051.509, 'duration': 7.067}, {'end': 4066.259, 'text': 'That is, accuracy is on the y-axis and cutoff is on the x-axis, and this helps us to determine how does accuracy vary with respect to the cutoff.', 'start': 4058.836, 'duration': 7.423}], 'summary': 'Plot accuracy against cutoff to analyze variation', 'duration': 32.86, 'max_score': 4033.399, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ84033399.jpg'}, {'end': 4351.397, 'src': 'embed', 'start': 4320.973, 'weight': 2, 'content': [{'end': 4327.463, 'text': "Okay, so now let's say this is green over here, right? So this range is from 0.26 to 0.32.", 'start': 4320.973, 'duration': 6.49}, {'end': 4330.685, 'text': "Now let's say the value which I'll take is 0.28.", 'start': 4327.463, 'duration': 3.222}, {'end': 4338.648, 'text': "Okay So now I'll take a threshold value of 0.28 and build a confusion matrix with respect to that threshold value.", 'start': 4330.685, 'duration': 7.963}, {'end': 4340.569, 'text': 'Okay Table.', 'start': 4339.429, 'duration': 1.14}, {'end': 4351.397, 'text': "So first I'll type test dollar churn, then the predicted values, result log, and the threshold value is 0.28.", 'start': 4341.19, 'duration': 10.207}], 'summary': 'Using a threshold of 0.28, a confusion matrix is built for test dollar churn.', 'duration': 30.424, 'max_score': 4320.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ84320973.jpg'}], 'start': 3057.936, 'title': 'Evaluating model performance', 'summary': 'Covers logistic regression, confusion matrix, and performance metrics like accuracy, precision, and recall, with examples and quantifiable data. it also discusses implementing confusion matrix in r for logistic regression model, building churn prediction model, and evaluating model performance using performance metrics, highlighting an accuracy of 72% using a threshold value of 0.41 and 60% using a threshold value of 0.28.', 'chapters': [{'end': 3369.596, 'start': 3057.936, 'title': 'Logistic regression and performance metrics', 'summary': 'Explains logistic regression, confusion matrix, and performance metrics like accuracy, precision, and recall, using examples and quantifiable data, showcasing the importance of thresholding in classification models.', 'duration': 311.66, 'highlights': ['The chapter explains the concept of thresholding in logistic regression, emphasizing the importance of defining a classification threshold to map regression values to binary categories, with a specific example of rain prediction using a threshold value of 0.65. Emphasizes the importance of defining a classification threshold in logistic regression, with an example of rain prediction using a threshold value of 0.65.', 'The chapter details the importance of reducing false negatives in a confusion matrix, highlighting the real-life implications of incorrectly diagnosing a patient with cancer, and showcases the calculation of performance metrics such as accuracy, precision, and recall with specific quantifiable data. Emphasizes the importance of reducing false negatives in a confusion matrix and showcases the calculation of performance metrics such as accuracy, precision, and recall with specific quantifiable data.', 'The chapter explains the terminology and concept of a confusion matrix, including true positives, true negatives, false positives, and false negatives, using a patient-cancer diagnosis example to illustrate each term. Explains the terminology and concept of a confusion matrix, using a patient-cancer diagnosis example to illustrate each term.']}, {'end': 3786.058, 'start': 3369.836, 'title': 'Implementing confusion matrix in r', 'summary': 'Discusses implementing a confusion matrix in r for logistic regression model, dividing the dataset into training and testing sets, building the model with a logistic regression algorithm, predicting values with probabilities, and analyzing the accuracy and threshold values to determine the best cutoff, as well as discussing the roc curve and auc measure.', 'duration': 416.222, 'highlights': ['The accuracy of the built model is 66% when the threshold value is set to 0.35, indicating an improvement from the 61% accuracy achieved with a threshold value of 0.3.', 'The process involves dividing the dataset into training and testing sets using a split ratio of 0.65, with the training set containing 4578 rows and the testing set containing 2465 rows.', 'The ROC curve, which stands for receiver operating characteristic, is used to assess the performance of the model with respect to all classification thresholds and determine the right threshold value, and the AUC measure ranges from 0 to 1, providing an aggregate measure of performance across all possible classification thresholds.']}, {'end': 3935.519, 'start': 3786.479, 'title': 'Building churn prediction model', 'summary': 'Involves building a churn prediction model using the ca tools package and jlm function, splitting the dataset with a 0.65 ratio, and using the predict function to generate predicted probabilities and a confusion matrix for evaluation.', 'duration': 149.04, 'highlights': ['Using the CA Tools package and sample dot split function, the data set is divided with a split ratio of 0.65, storing the split in an object called split tag. The data set is split into training and testing sets with a split ratio of 0.65, using the CA Tools package and sample dot split function, and stored in an object called split tag.', 'Building a model using the JLM function with churn as the dependent variable and monthly charges as the independent variable, and storing it in an object called mod log. A model is built using the JLM function with churn as the dependent variable and monthly charges as the independent variable, and stored in an object called mod log.', 'Using the predict function with the model and test data to generate predicted probabilities, stored in an object called result log. The predict function is used with the model and test data to generate predicted probabilities, which are stored in an object called result log.', 'Creating a random confusion matrix using the table function to evaluate the predicted probabilities against the actual values for churn in the test set. A random confusion matrix is created using the table function to evaluate the predicted probabilities against the actual values for churn in the test set.']}, {'end': 4492.957, 'start': 3936.299, 'title': 'Evaluating model performance with performance metrics', 'summary': 'Discusses the use of performance metrics to evaluate a model, including accuracy, roc curve, and area under the curve, highlighting the trade-off between true positive rate and false positive rate, with an accuracy of 72% using a threshold value of 0.41 and 60% using a threshold value of 0.28.', 'duration': 556.658, 'highlights': ['The chapter discusses the use of performance metrics to evaluate a model, including accuracy, ROC curve, and area under the curve. The discussion revolves around using performance metrics like accuracy, ROC curve, and area under the curve to evaluate model performance.', 'An accuracy of 72% is achieved using a threshold value of 0.41. An accuracy of 72% is achieved when using a threshold value of 0.41 to evaluate the model.', 'An accuracy of 60% is achieved using a threshold value of 0.28. An accuracy of 60% is achieved when using a threshold value of 0.28 to evaluate the model.', 'Highlighting the trade-off between true positive rate and false positive rate. The discussion emphasizes the trade-off between true positive rate and false positive rate as a crucial factor in evaluating model performance.']}], 'duration': 1435.021, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ83057936.jpg', 'highlights': ['The ROC curve assesses model performance with respect to all classification thresholds, determining the right threshold value.', 'The chapter emphasizes the importance of defining a classification threshold in logistic regression, using a rain prediction example with a threshold value of 0.65.', 'The discussion highlights the trade-off between true positive rate and false positive rate as a crucial factor in evaluating model performance.', 'The chapter details the importance of reducing false negatives in a confusion matrix, with real-life implications and specific quantifiable data for performance metrics.', 'The accuracy of the built model is 72% using a threshold value of 0.41 and 60% using a threshold value of 0.28, showcasing the impact of threshold selection on model performance.']}, {'end': 6539.573, 'segs': [{'end': 4812.838, 'src': 'embed', 'start': 4784.545, 'weight': 0, 'content': [{'end': 4789.57, 'text': 'And I will repeat this process n times so that there are n records in dataset A1 as well.', 'start': 4784.545, 'duration': 5.025}, {'end': 4798.211, 'text': 'So what you need to keep in mind is that out of these n records in A1, some of them might have come twice, thrice or even several times over.', 'start': 4790.327, 'duration': 7.884}, {'end': 4802.333, 'text': 'While some records from A might not have made it at all to A1.', 'start': 4798.891, 'duration': 3.442}, {'end': 4804.214, 'text': "So I've created A1 like this.", 'start': 4802.893, 'duration': 1.321}, {'end': 4808.616, 'text': "And then I'll go ahead and create multiple data sets the same way.", 'start': 4804.874, 'duration': 3.742}, {'end': 4812.838, 'text': 'And each of these have the same number of records as A.', 'start': 4809.336, 'duration': 3.502}], 'summary': "A1 dataset is created by repeating process n times, with potential duplicate and missing records, matching a's record count.", 'duration': 28.293, 'max_score': 4784.545, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ84784545.jpg'}, {'end': 5213.727, 'src': 'embed', 'start': 5184.203, 'weight': 1, 'content': [{'end': 5187.103, 'text': "So I'm basically taking this data set and storing this into a new object.", 'start': 5184.203, 'duration': 2.9}, {'end': 5189.604, 'text': 'So this is capital C, this is small C.', 'start': 5187.223, 'duration': 2.381}, {'end': 5191.244, 'text': "That's pretty much the only difference over here.", 'start': 5189.604, 'duration': 1.64}, {'end': 5201.787, 'text': 'And now I will take the sales column, and wherever the value is less than eight, I will tag it as no, and wherever the value is greater than eight,', 'start': 5195.605, 'duration': 6.182}, {'end': 5202.627, 'text': "I'll tag it as yes.", 'start': 5201.787, 'duration': 0.84}, {'end': 5206.464, 'text': 'And I will put that result in the object height.', 'start': 5203.163, 'duration': 3.301}, {'end': 5213.727, 'text': "Now I'll create a new data frame, which consists of all the columns from this car seats data set.", 'start': 5207.445, 'duration': 6.282}], 'summary': 'Transforming data set, creating new object, tagging sales values, and forming new data frame.', 'duration': 29.524, 'max_score': 5184.203, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ85184203.jpg'}, {'end': 5511.273, 'src': 'embed', 'start': 5482.054, 'weight': 2, 'content': [{'end': 5484.877, 'text': 'So we want to see if the sales are high or not.', 'start': 5482.054, 'duration': 2.823}, {'end': 5490.488, 'text': 'So the first split point is based on the shelf location column.', 'start': 5485.247, 'duration': 5.241}, {'end': 5495.169, 'text': 'So this is the column and this determines the first split.', 'start': 5491.949, 'duration': 3.22}, {'end': 5504.532, 'text': 'So over here, if the value is either equal to bad or medium, then we go on to the left side.', 'start': 5496.09, 'duration': 8.442}, {'end': 5509.213, 'text': 'On the other hand, if the value is equal to good, then we go on to the right side.', 'start': 5504.932, 'duration': 4.281}, {'end': 5511.273, 'text': "Right So let's go to the right side.", 'start': 5509.573, 'duration': 1.7}], 'summary': 'Analyzing sales based on shelf location: bad/medium vs. good.', 'duration': 29.219, 'max_score': 5482.054, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ85482054.jpg'}, {'end': 5835.983, 'src': 'embed', 'start': 5808.274, 'weight': 4, 'content': [{'end': 5810.576, 'text': 'Now let me have a glance at this plot.', 'start': 5808.274, 'duration': 2.302}, {'end': 5815.46, 'text': 'So I will build a plot again.', 'start': 5813.018, 'duration': 2.442}, {'end': 5824.546, 'text': 'Right So this time we see that the split criteria as determined the first split criteria as determined by the price.', 'start': 5816.661, 'duration': 7.885}, {'end': 5832.262, 'text': 'So If price is less than 90, then we go to the left side and if price is greater than 90, we go to the right side.', 'start': 5825.107, 'duration': 7.155}, {'end': 5835.983, 'text': 'So this is basically the entire decision tree which we have over here.', 'start': 5832.562, 'duration': 3.421}], 'summary': 'Built decision tree with price split criteria, resulting in two branches.', 'duration': 27.709, 'max_score': 5808.274, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ85808274.jpg'}, {'end': 6242.593, 'src': 'embed', 'start': 6206.627, 'weight': 3, 'content': [{'end': 6211.608, 'text': 'Plot of prune.carSeats.', 'start': 6206.627, 'duration': 4.981}, {'end': 6215.769, 'text': "Now I'll also add the text for this.", 'start': 6214.089, 'duration': 1.68}, {'end': 6227.354, 'text': 'So this has 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16.', 'start': 6221.681, 'duration': 5.673}, {'end': 6231.507, 'text': 'So initially, we had a fully grown tree.', 'start': 6227.364, 'duration': 4.143}, {'end': 6234.868, 'text': 'But after that, we did a bit of cross validation.', 'start': 6231.927, 'duration': 2.941}, {'end': 6242.593, 'text': 'And then we found out that fully grown tree is not a good idea because that fully grown tree does not give us.', 'start': 6235.008, 'duration': 7.585}], 'summary': 'Prune carseats tree to 16 nodes after cross validation.', 'duration': 35.966, 'max_score': 6206.627, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ86206627.jpg'}], 'start': 4492.957, 'title': 'Decision trees and random forests', 'summary': 'Discusses decision trees, their performance metrics, types, and applications. it also explores random forest in decision trees, bagging, and how it introduces randomness in column selection, resulting in different decision trees. additionally, it details building a classification model for car sales data using a decision tree, visualizing the process, and explains the process of building and testing a model with a split ratio of 65% for training and 35% for testing. the chapter covers model prediction, pruning for accuracy improvement, and the concept of seed in generating consistent results, achieving an initial accuracy of 76% and improving to 77%.', 'chapters': [{'end': 4618.707, 'start': 4492.957, 'title': 'Understanding decision trees', 'summary': 'Discusses the concept of decision trees, including their performance metrics, structure, and types, explaining how they are used to make predictions based on test conditions and categories. it also delves into the types of decision trees, such as classification and regression trees, and their applications.', 'duration': 125.75, 'highlights': ['Decision tree performance metrics closer to one indicate a perfect result, while those closer to zero indicate a completely wrong result. The speaker emphasizes that a value closer to one signifies a hundred percent perfect outcome, and a value closer to zero signifies a hundred percent wrong result.', 'Explanation of decision tree structure, with internal nodes representing test conditions and leaf nodes representing data categories. The structure of a decision tree is explained, depicting internal nodes as test conditions on attributes and leaf nodes as categories into which the data is divided.', "Classification tree used for categorical target variables, while regression tree used for numerical or continuous response variables. The differentiation between classification and regression trees is elaborated, highlighting the former's use for categorical target variables and the latter's use for numerical or continuous response variables.", 'Illustration of decision tree application in predicting customer churn in a telecom company based on gender, tenure, and monthly charges. An example is provided to demonstrate the application of a decision tree in predicting customer churn in a telecom company by considering factors such as gender, tenure, and monthly charges.']}, {'end': 5043.601, 'start': 4619.388, 'title': 'Random forest in decision trees', 'summary': 'Explores the concept of bagging and random forest in decision trees, illustrating how from just 1000 rows of data, 1 million rows can be obtained, and how random forest introduces randomness in the selection of columns, resulting in different decision trees.', 'duration': 424.213, 'highlights': ['Random forest introduces randomness by providing a random subset of columns to the algorithm for each node split, resulting in very different decision trees compared to bagging. In random forest, only a random subset of columns is provided to the algorithm for each node split, ensuring that each of the X trees are very different from each other.', 'From just one data set A, multiple data sets A1, A2, A3 till A of X can be created, resulting in 1 million rows from just 1000 rows of data. From one data set A, multiple data sets A1, A2, A3 till A of X can be created, resulting in 1 million rows from just 1000 rows of data.', 'Using the random forest method, for each of the X data sets, one decision tree is fitted, resulting in an ensemble of trees for making predictions. For each of the X data sets, one decision tree is fitted using the random forest method, resulting in an ensemble of trees for making predictions.']}, {'end': 5530.17, 'start': 5043.601, 'title': 'Decision tree for car sales', 'summary': 'Details the process of building a classification model using a decision tree for car sales data, creating a categorical column for sales, building the model, and visualizing the decision tree.', 'duration': 486.569, 'highlights': ["The process of creating a categorical column for sales in the car seats dataset by tagging values greater than eight as 'yes' and values less than or equal to eight as 'no'. The data set containing sales of child car seats at 400 different stores is processed to create a categorical column by tagging sales values greater than eight as 'yes' and values less than or equal to eight as 'no'.", 'The utilization of the tree function from the tree package to build a decision tree model for classifying high and low sales based on other columns in the dataset. The tree function from the tree package is used to build a decision tree model for classifying high and low sales based on other columns in the dataset, with a focus on understanding the split points and determining the sales value.', 'The visualization of the decision tree using the plot function and the addition of text to the plot to understand the split points and decision-making process. The decision tree model is visualized using the plot function, and text is added to the plot to understand the split points and decision-making process, providing insights into the factors influencing high and low sales.']}, {'end': 5835.983, 'start': 5534.291, 'title': 'Building and testing a model', 'summary': 'Explains how to divide data into train and test sets, build a model on the train set, predict values on the test set, and visualize the decision tree, with a split ratio of 65% for training and 35% for testing.', 'duration': 301.692, 'highlights': ['The data is divided into a training set with 65% of the records and a testing set with 35% of the records. The split ratio is 0.65, meaning that 65 percent of records go into the training set and 35 percent go into the testing set.', 'The model is built on the training set using the tree function with the high column as the dependent variable and all other columns except sales as independent variables. The model is built using the tree function, where the high column is the dependent variable, and all other columns except the sales column are the independent variables.', 'The decision tree is visualized, showing the split criteria determined by the price and the resulting branches. The decision tree visualization shows the split criteria determined by the price and the resulting branches in the tree.']}, {'end': 6539.573, 'start': 5839.444, 'title': 'Model prediction, pruning, and accuracy', 'summary': 'Covers building a model, predicting values with an initial accuracy of 76%, pruning the tree to improve accuracy to 77%, and explaining the concept of seed in generating consistent results.', 'duration': 700.129, 'highlights': ['The initial accuracy after predicting values is 76%. After predicting values with the initial model, the accuracy is calculated as 76% based on the confusion matrix.', 'Pruning the tree improves the accuracy to 77%. After pruning the tree and predicting values again, the accuracy improves to 77% as calculated from the confusion matrix.', 'Explanation of the concept of seed in generating consistent results. The speaker explains the concept of setting a seed value to ensure consistent results when using random functions like sample, ensuring the same result is obtained when demonstrating examples to others.']}], 'duration': 2046.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ84492957.jpg', 'highlights': ['Random forest introduces randomness by providing a random subset of columns to the algorithm for each node split, resulting in very different decision trees compared to bagging.', "The process of creating a categorical column for sales in the car seats dataset by tagging values greater than eight as 'yes' and values less than or equal to eight as 'no'.", 'The data is divided into a training set with 65% of the records and a testing set with 35% of the records.', 'The initial accuracy after predicting values is 76%.', 'Decision tree performance metrics closer to one indicate a perfect result, while those closer to zero indicate a completely wrong result.']}, {'end': 8910.881, 'segs': [{'end': 6570.862, 'src': 'embed', 'start': 6539.653, 'weight': 0, 'content': [{'end': 6543.256, 'text': "It says that if you want the same result, that is when we'll use set dot seed.", 'start': 6539.653, 'duration': 3.603}, {'end': 6548.839, 'text': 'So there is nothing which we are replacing over here.', 'start': 6546.878, 'duration': 1.961}, {'end': 6552.321, 'text': 'A quick question.', 'start': 6551.441, 'duration': 0.88}, {'end': 6555.463, 'text': "When we're doing this kind of classification, right?", 'start': 6552.941, 'duration': 2.522}, {'end': 6561.275, 'text': "And when we're doing the test, I mean accuracy and all of that is good, right?", 'start': 6557.424, 'duration': 3.851}, {'end': 6566.318, 'text': 'Can I get on the basis of my test data?', 'start': 6562.576, 'duration': 3.742}, {'end': 6568.92, 'text': 'can I get the probability of?', 'start': 6566.318, 'duration': 2.602}, {'end': 6570.862, 'text': 'I mean I want to just do a scoring now.', 'start': 6568.92, 'duration': 1.942}], 'summary': 'Using set.seed ensures consistent results in classification tests with test data.', 'duration': 31.209, 'max_score': 6539.653, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ86539653.jpg'}, {'end': 6700.855, 'src': 'embed', 'start': 6660.609, 'weight': 2, 'content': [{'end': 6663.491, 'text': 'So we have this cross validation.', 'start': 6660.609, 'duration': 2.882}, {'end': 6668.176, 'text': 'And let me be sit over here.', 'start': 6666.674, 'duration': 1.502}, {'end': 6679.643, 'text': 'right so over here we took the number of nodes to be 16.', 'start': 6674.42, 'duration': 5.223}, {'end': 6687.627, 'text': "now, instead of 16, we'll say the number of nodes to be 9 and we'll see what is the accuracy when the number of nodes is 9 right.", 'start': 6679.643, 'duration': 7.984}, {'end': 6691.089, 'text': "so we'll prune this tree at 9 nodes.", 'start': 6687.627, 'duration': 3.462}, {'end': 6696.553, 'text': 'so again, all we have to do is set this best value to be equal to 9..', 'start': 6691.089, 'duration': 5.464}, {'end': 6700.855, 'text': "so it's the same thing again.", 'start': 6696.553, 'duration': 4.302}], 'summary': 'Testing accuracy with 9 nodes in cross validation.', 'duration': 40.246, 'max_score': 6660.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ86660609.jpg'}, {'end': 6794.061, 'src': 'embed', 'start': 6758.866, 'weight': 1, 'content': [{'end': 6760.167, 'text': "Let's see what is the accuracy this time.", 'start': 6758.866, 'duration': 1.301}, {'end': 6771.669, 'text': '68 plus 37, 68 plus 37 plus 20 plus 15.', 'start': 6760.187, 'duration': 11.482}, {'end': 6774.17, 'text': 'So this time we see that accuracy is 75.', 'start': 6771.669, 'duration': 2.501}, {'end': 6783.615, 'text': "So the ideal split or the ideal level where we'd have to cut out our tree is when we have 16 nodes.", 'start': 6774.17, 'duration': 9.445}, {'end': 6794.061, 'text': 'So again, that is why this cross validation is very important for us, right? So this result over here, we see that 9 and 16, right? So 16 is ideal.', 'start': 6784.516, 'duration': 9.545}], 'summary': 'Accuracy of 75% achieved with 16 nodes, highlighting the importance of cross validation.', 'duration': 35.195, 'max_score': 6758.866, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ86758866.jpg'}, {'end': 7034.271, 'src': 'embed', 'start': 7004.967, 'weight': 4, 'content': [{'end': 7014.232, 'text': "So what I'm doing is from this Iris data set, I will be selecting all these row numbers right?", 'start': 7004.967, 'duration': 9.265}, {'end': 7024.238, 'text': 'So these row numbers comprise of 65% of the Iris data set and I will store them in the train set.', 'start': 7014.712, 'duration': 9.526}, {'end': 7029.909, 'text': 'Now, similarly, So the split tag contains 65 percent of the row numbers.', 'start': 7024.898, 'duration': 5.011}, {'end': 7032.731, 'text': 'So apart from those 65 percent.', 'start': 7030.35, 'duration': 2.381}, {'end': 7034.271, 'text': 'So when I put a minus symbol.', 'start': 7032.811, 'duration': 1.46}], 'summary': 'Selecting 65% of the iris data set for the train set.', 'duration': 29.304, 'max_score': 7004.967, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ87004967.jpg'}, {'end': 8786.148, 'src': 'embed', 'start': 8722.74, 'weight': 6, 'content': [{'end': 8726.583, 'text': "You did not notice any change because we've got the same result.", 'start': 8722.74, 'duration': 3.843}, {'end': 8728.885, 'text': "I'll delete this.", 'start': 8727.544, 'duration': 1.341}, {'end': 8732.756, 'text': "I'll Hit enter again.", 'start': 8730.086, 'duration': 2.67}, {'end': 8743.619, 'text': "So what we see is even though we've included just these one, two, three, four, five independent variables, we've got the same split over here.", 'start': 8733.297, 'duration': 10.322}, {'end': 8752.482, 'text': 'So this again basically reiterates our belief that no other column was used for the split purpose.', 'start': 8744.1, 'duration': 8.382}, {'end': 8756.063, 'text': 'Right So we have built the model now.', 'start': 8753.022, 'duration': 3.041}, {'end': 8761.928, 'text': "Let's go ahead and predict the values again and let's calculate the RMSE for this model.", 'start': 8756.664, 'duration': 5.264}, {'end': 8767.113, 'text': "We'll use the predict function.", 'start': 8765.371, 'duration': 1.742}, {'end': 8772.397, 'text': "We'll take in the model as the first parameter and then we'll take the values on test set.", 'start': 8767.773, 'duration': 4.624}, {'end': 8774.899, 'text': "We'll store it in predict tree.", 'start': 8772.617, 'duration': 2.282}, {'end': 8781.544, 'text': "Again, I'll bind the actual values and the test values and I'll store it in final data.", 'start': 8776.04, 'duration': 5.504}, {'end': 8786.148, 'text': "I'll convert this to a data frame and calculate the error.", 'start': 8782.325, 'duration': 3.823}], 'summary': 'Model reiteration shows no change in split, predicting rmse for test set.', 'duration': 63.408, 'max_score': 8722.74, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ88722740.jpg'}], 'start': 6539.653, 'title': 'Decision tree modeling', 'summary': 'Covers utilizing decision trees for classification and regression, achieving accuracies of 75% and 90% in different examples, exploring the party package, multiclass classification, and regression tasks, and demonstrating prediction and evaluation techniques.', 'chapters': [{'end': 6826.221, 'start': 6539.653, 'title': 'Decision tree classification and regression', 'summary': 'Discusses using decision trees for classification and regression, showcasing a classification example with 75% accuracy and the importance of cross-validation in determining the ideal number of nodes for pruning.', 'duration': 286.568, 'highlights': ['The decision tree classification achieved 75% accuracy on the test set. The accuracy of the decision tree classification on the test set was 75%, indicating its effectiveness in predicting outcomes.', 'Cross-validation indicated that 16 nodes were the ideal level for pruning the decision tree. Cross-validation revealed that 16 nodes were the ideal level for pruning the decision tree, emphasizing the importance of this process in optimizing model performance.', 'The decision tree can be used for both classification and regression purposes. The decision tree can serve dual purposes of classification and regression, providing flexibility in predicting discrete and continuous values.']}, {'end': 7660.058, 'start': 6828.683, 'title': 'Building decision trees with the party package', 'summary': 'Covers building decision trees using the party package, including creating a three-way classification model for the iris dataset, understanding split criteria, predicting values, evaluating accuracy, and reducing dimensions based on split criteria, achieving an accuracy of 90%.', 'duration': 831.375, 'highlights': ['A three-way classification model is built for the iris dataset to classify species as setosa, virginica, or versicolor, achieving an accuracy of 90%. A three-way classification model is built using the party package for the iris dataset, achieving an accuracy of 90% in classifying species as setosa, virginica, or versicolor.', "The split criteria for the decision tree are determined by petal length and petal width, indicating that only these two columns determine the split and the classification of the flower species. The decision tree's split criteria are determined by petal length and petal width, indicating that only these two columns determine the split and the classification of the flower species.", 'The decision is made to build a model using only petal width and petal length as independent variables to reduce redundancy, indicating a strategic reduction in the number of dimensions based on the split criteria. A decision is made to build a model using only petal width and petal length as independent variables to reduce redundancy, showcasing a strategic reduction in the number of dimensions based on the split criteria.']}, {'end': 8500.409, 'start': 7660.939, 'title': 'Decision tree model and multiclass classification', 'summary': 'Discusses the process of building and evaluating a decision tree model for classification and regression tasks, achieving similar accuracy with and without certain independent variables, and the use of decision tree functions for multiclass classification, with emphasis on the r part function for regression.', 'duration': 839.47, 'highlights': ['The chapter discusses the process of building and evaluating a decision tree model for classification and regression tasks The chapter covers the process of building and evaluating a decision tree model for classification and regression tasks, including evaluating the accuracy of the model and identifying independent variables that do not provide useful information.', 'Achieving similar accuracy with and without certain independent variables The chapter demonstrates achieving similar accuracy with and without certain independent variables in the decision tree model, highlighting the process of trial and error to find the best-fit model.', 'The use of decision tree functions for multiclass classification, with emphasis on the R part function for regression The chapter discusses the use of decision tree functions for multiclass classification and emphasizes the R part function for regression, showcasing the process of predicting the median value of owner-occupied homes using the R part function.']}, {'end': 8910.881, 'start': 8501.27, 'title': 'Regression with decision tree', 'summary': 'Demonstrates using the predict function to predict continuous values, calculating root mean square error for two models, and identifying ideal independent variables for regression using a decision tree.', 'duration': 409.611, 'highlights': ['The root mean square error for the first model is 3.93. The root mean square error for the first model is 3.93, indicating the accuracy of the prediction.', 'Identifying key independent variables for regression using a decision tree. The chapter discusses identifying key independent variables for regression using a decision tree, determining that only a limited number of columns have been used for the split.', 'The root mean square error is the same for both models, indicating no need to include additional variables. The root mean square error is the same for both models, suggesting that there is no need to include any other variable after the five identified independent variables.']}], 'duration': 2371.228, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ86539653.jpg', 'highlights': ['Decision tree classification achieved 75% accuracy on the test set.', 'A three-way classification model is built for the iris dataset, achieving an accuracy of 90%.', 'Cross-validation revealed that 16 nodes were the ideal level for pruning the decision tree.', 'The decision tree can be used for both classification and regression purposes.', 'The split criteria for the decision tree are determined by petal length and petal width.', 'The chapter covers the process of building and evaluating a decision tree model for classification and regression tasks.', 'The root mean square error for the first model is 3.93.', 'The chapter discusses identifying key independent variables for regression using a decision tree.']}, {'end': 10709.045, 'segs': [{'end': 8968.692, 'src': 'embed', 'start': 8937.937, 'weight': 0, 'content': [{'end': 8947.502, 'text': 'Like is the same pruning method we use or different methods or different packages? No, so there is something known as train control parameters.', 'start': 8937.937, 'duration': 9.565}, {'end': 8953.006, 'text': 'So you will use those train control parameters for our part and C3.', 'start': 8947.843, 'duration': 5.163}, {'end': 8958.25, 'text': 'I guess you can read up on that.', 'start': 8956.689, 'duration': 1.561}, {'end': 8960.55, 'text': 'For our part, I know.', 'start': 8958.27, 'duration': 2.28}, {'end': 8964.771, 'text': 'For C3, I was not knowing.', 'start': 8961.13, 'duration': 3.641}, {'end': 8968.692, 'text': 'So C3, there are some train control parameters.', 'start': 8965.731, 'duration': 2.961}], 'summary': 'Discussion on using train control parameters for parts and c3.', 'duration': 30.755, 'max_score': 8937.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ88937937.jpg'}, {'end': 9153.952, 'src': 'embed', 'start': 9124.904, 'weight': 2, 'content': [{'end': 9135.37, 'text': 'and uh, so this is basically a data set which measures the fetal heart rate of a patient, and these are the different parameters,', 'start': 9124.904, 'duration': 10.466}, {'end': 9140.673, 'text': 'and this is basically the final categorical column which we are trying to predict.', 'start': 9135.37, 'duration': 5.303}, {'end': 9146.536, 'text': 'so this nsp basically stands for normal, suspect or pathological.', 'start': 9140.673, 'duration': 5.863}, {'end': 9153.952, 'text': "So that fetal heart rate it's either normal or it's suspected to be pathological, or it is pathological, right?", 'start': 9147.347, 'duration': 6.605}], 'summary': 'Data set measures fetal heart rate; predicts normal, suspect, or pathological conditions.', 'duration': 29.048, 'max_score': 9124.904, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ89124904.jpg'}, {'end': 9195.496, 'src': 'embed', 'start': 9174.006, 'weight': 3, 'content': [{'end': 9187.454, 'text': "So the perfect example for this could be let's say you want to watch a movie and you take your friend's advice so that one particular friend hates all action movies.", 'start': 9174.006, 'duration': 13.448}, {'end': 9193.715, 'text': 'right, so you want to watch Avengers and that one particular friend hates all action movies.', 'start': 9187.454, 'duration': 6.261}, {'end': 9195.496, 'text': 'and he is very.', 'start': 9193.715, 'duration': 1.781}], 'summary': 'A friend dislikes action movies, such as avengers.', 'duration': 21.49, 'max_score': 9174.006, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ89174006.jpg'}, {'end': 9302.96, 'src': 'embed', 'start': 9276.482, 'weight': 4, 'content': [{'end': 9289.017, 'text': 'What I do is I create another data set, L1,, which has the same number of records, and These records have been taken from L,', 'start': 9276.482, 'duration': 12.535}, {'end': 9293.118, 'text': 'but that is done by sampling with replacement.', 'start': 9289.017, 'duration': 4.101}, {'end': 9302.96, 'text': 'Similarly, I will create L2, which has N records taken from L, but these records are sampling with replacement.', 'start': 9293.658, 'duration': 9.302}], 'summary': 'Creating data sets l1 and l2 through sampling with replacement.', 'duration': 26.478, 'max_score': 9276.482, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ89276482.jpg'}, {'end': 9564.6, 'src': 'embed', 'start': 9533.631, 'weight': 5, 'content': [{'end': 9540.116, 'text': 'And to do that, I will use the as dot factor function and I will convert this into a factor.', 'start': 9533.631, 'duration': 6.485}, {'end': 9547.687, 'text': 'So as dot factor of data dollar NSP and I will store this back to data dollar NSP.', 'start': 9540.902, 'duration': 6.785}, {'end': 9553.592, 'text': 'Now, let me have a look at the structure of this again, structure of data.', 'start': 9548.248, 'duration': 5.344}, {'end': 9558.135, 'text': 'And we see that this integer type has been converted to factor.', 'start': 9553.672, 'duration': 4.463}, {'end': 9562.679, 'text': 'Again, let me have a glance at the levels of this NSP.', 'start': 9558.576, 'duration': 4.103}, {'end': 9564.6, 'text': 'So one, two and three.', 'start': 9563.079, 'duration': 1.521}], 'summary': 'Converted integer type to factor using as.factor function. nsp has levels 1, 2, and 3.', 'duration': 30.969, 'max_score': 9533.631, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ89533631.jpg'}, {'end': 9659.597, 'src': 'embed', 'start': 9629.456, 'weight': 6, 'content': [{'end': 9634.46, 'text': 'So 65% of records going to train, 35% records going to test.', 'start': 9629.456, 'duration': 5.004}, {'end': 9642.745, 'text': 'Now I will go ahead and take in wherever these values from split tag are there.', 'start': 9635.64, 'duration': 7.105}, {'end': 9646.927, 'text': 'So I will take those 65% values and store them in train set.', 'start': 9643.185, 'duration': 3.742}, {'end': 9652.431, 'text': 'And apart from the split tag, that is the rest of the 35% records, I will take them.', 'start': 9647.588, 'duration': 4.843}, {'end': 9655.476, 'text': 'and store them in the test set.', 'start': 9653.095, 'duration': 2.381}, {'end': 9659.597, 'text': 'So here we have the training and testing sets ready.', 'start': 9656.056, 'duration': 3.541}], 'summary': '65% of records go to train, 35% to test, creating training and testing sets.', 'duration': 30.141, 'max_score': 9629.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ89629456.jpg'}, {'end': 9744.652, 'src': 'embed', 'start': 9710.618, 'weight': 8, 'content': [{'end': 9715.46, 'text': 'Right So let me just print RF over here.', 'start': 9710.618, 'duration': 4.842}, {'end': 9717.54, 'text': 'So this is the model which we built.', 'start': 9715.98, 'duration': 1.56}, {'end': 9728.744, 'text': 'So by default, this random forest algorithm takes the number of trees to be 500 and this M value.', 'start': 9718.001, 'duration': 10.743}, {'end': 9733.106, 'text': 'So that M value which we saw, this is number of variables tried at each split.', 'start': 9728.824, 'duration': 4.282}, {'end': 9733.966, 'text': 'This is four.', 'start': 9733.266, 'duration': 0.7}, {'end': 9740.009, 'text': 'Right So by default, the number of trees is taken as 500.', 'start': 9734.406, 'duration': 5.603}, {'end': 9744.652, 'text': 'and that m value is taken as 4.', 'start': 9740.009, 'duration': 4.643}], 'summary': 'Random forest model built with 500 trees and m value of 4.', 'duration': 34.034, 'max_score': 9710.618, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ89710618.jpg'}, {'end': 10190.306, 'src': 'embed', 'start': 10162.26, 'weight': 10, 'content': [{'end': 10166.741, 'text': 'And when the number of trees is 300, so this first tries out with the m value.', 'start': 10162.26, 'duration': 4.481}, {'end': 10171.803, 'text': 'So initially, the number of variables, independent variables available for it were four.', 'start': 10166.801, 'duration': 5.002}, {'end': 10178.697, 'text': 'And when the number of independent variables available were 4, the OOB error was 6.15.', 'start': 10172.352, 'duration': 6.345}, {'end': 10180.759, 'text': 'And then it tried with 8.', 'start': 10178.697, 'duration': 2.062}, {'end': 10187.424, 'text': 'So when it tried with 8, the OOB error, it came down to 5.71%.', 'start': 10180.759, 'duration': 6.665}, {'end': 10190.306, 'text': 'After that, it tried with 16.', 'start': 10187.424, 'duration': 2.882}], 'summary': 'Testing various m values, oob error reduced from 6.15% to 5.71% at 8 variables.', 'duration': 28.046, 'max_score': 10162.26, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ810162260.jpg'}, {'end': 10652.419, 'src': 'embed', 'start': 10620.785, 'weight': 7, 'content': [{'end': 10624.147, 'text': 'Next is the predicted values, which are stored in P2.', 'start': 10620.785, 'duration': 3.362}, {'end': 10627.87, 'text': 'So this is the confusion matrix which we get.', 'start': 10625.428, 'duration': 2.442}, {'end': 10636.047, 'text': 'Okay, so how many guys, of how many of you guys have still, you know, doubt with this confusion matrix?', 'start': 10629.642, 'duration': 6.405}, {'end': 10638.108, 'text': 'right?. Are you able to follow?', 'start': 10636.047, 'duration': 2.061}, {'end': 10640.63, 'text': 'how am I calculating the error with this confusion matrix?', 'start': 10638.108, 'duration': 2.522}, {'end': 10647.055, 'text': 'because I had a question regarding this.', 'start': 10640.63, 'duration': 6.425}, {'end': 10652.419, 'text': 'Right others, Everyone is clear with confusion matrix right?', 'start': 10647.055, 'duration': 5.364}], 'summary': 'Discussing the confusion matrix and addressing doubts among the audience.', 'duration': 31.634, 'max_score': 10620.785, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ810620785.jpg'}], 'start': 8911.722, 'title': 'Random forest model', 'summary': 'Covers decision tree pruning techniques and ensemble learning with random forest, achieving an accuracy of around 94% and obtaining confusion matrix results for classification error. it also discusses the process of building and optimizing the random forest model, emphasizing the random selection of independent variables for split criteria.', 'chapters': [{'end': 9153.952, 'start': 8911.722, 'title': 'Decision tree pruning techniques', 'summary': 'Covers decision tree pruning techniques, including cost complexity pruning and train control parameters, to minimize misclassification rates and impurity functions, with a focus on the ctg medical dataset from the uci machine learning repository.', 'duration': 242.23, 'highlights': ['Decision tree pruning involves using train control parameters to set the ideal number of nodes and threshold values for splitting, aiming to minimize misclassification rates. Train control parameters are used to set the ideal number of nodes and threshold values for splitting in decision tree pruning.', 'Cost complexity pruning aims to find the minimum level of split with the least misclassification rate, leading to the ideal number of terminal nodes. Cost complexity pruning focuses on finding the minimum level of split with the least misclassification rate to determine the ideal number of terminal nodes.', "The impurity function used in decision tree functions is primarily Gini index, with a focus on the CTG medical dataset to predict the fetal heart rate's normalcy. The impurity function primarily used in decision tree functions is the Gini index, with a focus on the CTG medical dataset to predict the fetal heart rate's normalcy."]}, {'end': 9593.994, 'start': 9154.392, 'title': 'Ensemble learning with random forest', 'summary': 'Explains the concept of ensemble learning using random forest for multi-class classification, illustrating the process through the example of movie recommendations and detailing the steps involved in bagging and random forest, with emphasis on the random selection of independent variables for split criteria.', 'duration': 439.602, 'highlights': ['Ensemble learning involves taking the aggregate opinion of multiple decision trees, with the example of movie recommendations demonstrating the concept of biased opinions and collective decision-making. Ensemble learning combines the results of multiple decision trees to obtain a collective opinion, illustrated through the example of movie recommendations. Out of ten people, eight recommend watching a movie, demonstrating the bias and collective decision-making in ensemble learning.', 'Bagging involves creating multiple datasets through random sampling with replacement and building decision trees on each dataset to obtain multiple results, leading to an aggregate result. Bagging entails creating multiple datasets through random sampling with replacement, building decision trees on each dataset, and aggregating the results to obtain the final outcome.', 'Random forest extends bagging by incorporating a random subset of independent variables for node split criteria, leading to the use of a random subset of features for making decisions. Random forest extends bagging by using a random subset of independent variables for node split criteria, thereby incorporating randomness in feature selection for decision-making.', 'The process involves converting integer variables into categorical variables and utilizing the as.factor function for the conversion. The conversion of integer variables into categorical variables is achieved using the as.factor function.', 'The NSP variable is converted into a categorical variable with three levels, representing normal patients, suspected patients, and patients with pathological heart disease, with corresponding quantities of 1,655, 295, and 176, respectively. The NSP variable is converted into a categorical variable with three levels, representing normal patients, suspected patients, and patients with pathological heart disease, with quantities of 1,655, 295, and 176, respectively.']}, {'end': 9944.602, 'start': 9594.014, 'title': 'Building random forest model', 'summary': 'Covers the process of building a random forest model on a dataset, achieving an accuracy of around 94% and obtaining confusion matrix results for classification error.', 'duration': 350.588, 'highlights': ['The dataset is divided into 65% for training and 35% for testing, using the create data partition method. The dataset is split into a 65% training set and a 35% test set using the create data partition method, ensuring a defined split probability.', "The random forest model is built on the training set, with a seed value of triple two and default settings of 500 trees and 4 for the M value. The random forest model is constructed on the training set with a seed value of triple two and default settings of 500 trees and 4 for the M value, ensuring the model's reproducibility and default parameters.", "The out of tree error estimate indicates an accuracy of around 94% for the model, with a 5.78% error estimate. The out of tree error estimate reveals an accuracy of approximately 94% for the model, with a calculated error estimate of 5.78%, demonstrating the model's predictive capability.", "The confusion matrix provides detailed classification error results, including correct and incorrect classifications for different patient conditions. The confusion matrix presents classification error details, including correct and incorrect classifications for patients with normal, suspected, and pathological conditions, providing insights into the model's classification performance."]}, {'end': 10709.045, 'start': 9945.822, 'title': 'Optimizing random forest model', 'summary': 'Discusses tuning the random forest model to find the optimal value of m, resulting in an accuracy of 94%. it also covers building a confusion matrix and calculating the accuracy from the matrix.', 'duration': 763.223, 'highlights': ['The accuracy of the random forest model is 94%, calculated using the formula 566+83+52 / 566+83+52+8+5+2+5+4, resulting in 94.07%.', 'Using the tuneRF function, the optimal M value for the random forest model is determined to be 8, resulting in an out-of-box error estimate of 5.86%.', 'The confusion matrix is built to calculate the accuracy of the model, with a discussion on the interpretation and calculation of the accuracy.']}], 'duration': 1797.323, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ88911722.jpg', 'highlights': ['Decision tree pruning aims to minimize misclassification rates by setting the ideal number of nodes and threshold values.', 'Cost complexity pruning focuses on finding the minimum level of split with the least misclassification rate to determine the ideal number of terminal nodes.', 'Ensemble learning combines the results of multiple decision trees to obtain a collective opinion, illustrated through the example of movie recommendations.', 'Bagging entails creating multiple datasets through random sampling with replacement, building decision trees on each dataset, and aggregating the results to obtain the final outcome.', 'Random forest extends bagging by using a random subset of independent variables for node split criteria, thereby incorporating randomness in feature selection for decision-making.', 'The dataset is split into a 65% training set and a 35% test set using the create data partition method, ensuring a defined split probability.', "The random forest model is constructed on the training set with a seed value of triple two and default settings of 500 trees and 4 for the M value, ensuring the model's reproducibility and default parameters.", "The out of tree error estimate reveals an accuracy of approximately 94% for the model, with a calculated error estimate of 5.78%, demonstrating the model's predictive capability.", "The confusion matrix presents classification error details, including correct and incorrect classifications for patients with normal, suspected, and pathological conditions, providing insights into the model's classification performance.", 'The accuracy of the random forest model is 94%, calculated using the formula 566+83+52 / 566+83+52+8+5+2+5+4, resulting in 94.07%.', 'Using the tuneRF function, the optimal M value for the random forest model is determined to be 8, resulting in an out-of-box error estimate of 5.86%.']}, {'end': 12431.564, 'segs': [{'end': 11294.931, 'src': 'embed', 'start': 11267.836, 'weight': 1, 'content': [{'end': 11272.018, 'text': 'So in that case, ASTV and MSTV would be those independent variables.', 'start': 11267.836, 'duration': 4.182}, {'end': 11274.979, 'text': 'If I want to use three, then these three would be that.', 'start': 11272.378, 'duration': 2.601}, {'end': 11276.276, 'text': 'So again.', 'start': 11275.655, 'duration': 0.621}, {'end': 11286.764, 'text': "so, from this plot, what you're doing is we are again building a model where we'll be using only these four independent variables,", 'start': 11276.276, 'duration': 10.488}, {'end': 11294.931, 'text': 'because these four independent variables, you know, affect the dependent variable, the maximum.', 'start': 11286.764, 'duration': 8.167}], 'summary': 'Building a model using four independent variables to affect the dependent variable.', 'duration': 27.095, 'max_score': 11267.836, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ811267836.jpg'}, {'end': 11358.581, 'src': 'embed', 'start': 11329.696, 'weight': 0, 'content': [{'end': 11336.28, 'text': 'So this tells you ASTV has the maximum effect on the dependent variable.', 'start': 11329.696, 'duration': 6.584}, {'end': 11351.335, 'text': 'Followed by MSTV followed by ALTV Right, so are you guys confused or is this clear of the importance? Yeah, got it Great.', 'start': 11336.903, 'duration': 14.432}, {'end': 11358.581, 'text': 'So now, once we know the importance or the order of importance of the independent variables,', 'start': 11351.655, 'duration': 6.926}], 'summary': 'Astv has the maximum effect on the dependent variable followed by mstv and altv.', 'duration': 28.885, 'max_score': 11329.696, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ811329696.jpg'}, {'end': 11815.062, 'src': 'embed', 'start': 11787.992, 'weight': 2, 'content': [{'end': 11791.575, 'text': 'So if you want probabilities, then he set the nomenclature to be PROB.', 'start': 11787.992, 'duration': 3.583}, {'end': 11793.416, 'text': 'It says the nomenclature difference.', 'start': 11792.175, 'duration': 1.241}, {'end': 11798.52, 'text': 'Again, so if you want any help, all you have to do is search for it.', 'start': 11795.017, 'duration': 3.503}, {'end': 11809.427, 'text': "So random forest, and you'll get all the help you need with respect to this package over here.", 'start': 11798.78, 'duration': 10.647}, {'end': 11815.062, 'text': "You'll have all of these things predict plot QNRF.", 'start': 11810.228, 'duration': 4.834}], 'summary': 'Nomenclature set to prob for probabilities, search for help on random forest package, and predict plot qnrf.', 'duration': 27.07, 'max_score': 11787.992, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ811787992.jpg'}, {'end': 12047.061, 'src': 'embed', 'start': 12014.782, 'weight': 3, 'content': [{'end': 12019.662, 'text': 'Whether you get a better accuracy with the logistic model or whether you get a better with decision or random.', 'start': 12014.782, 'duration': 4.88}, {'end': 12021.323, 'text': 'I mean, decision typically is not being used.', 'start': 12019.682, 'duration': 1.641}, {'end': 12025.343, 'text': 'We always go for random in case we have to go towards this direction.', 'start': 12022.043, 'duration': 3.3}, {'end': 12030.284, 'text': 'But you will have to create the models, tune those models, and compare the results.', 'start': 12026.004, 'duration': 4.28}, {'end': 12031.085, 'text': 'That is how you do it.', 'start': 12030.404, 'duration': 0.681}, {'end': 12033.165, 'text': 'It is always trial and error.', 'start': 12031.245, 'duration': 1.92}, {'end': 12034.665, 'text': "There's no thumb rule as such.", 'start': 12033.225, 'duration': 1.44}, {'end': 12039.146, 'text': 'This would work better in this case, or that would better work better in that case.', 'start': 12034.905, 'duration': 4.241}, {'end': 12047.061, 'text': 'okay, so thanks, thanks, thanks a lot.', 'start': 12040.917, 'duration': 6.144}], 'summary': "Compare models' accuracy, tune and trial for best results.", 'duration': 32.279, 'max_score': 12014.782, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ812014782.jpg'}], 'start': 10709.565, 'title': 'Random forest model optimization', 'summary': 'Delves into optimizing a random forest model by tuning parameters to achieve 94.34% accuracy, exploring variable importance, emphasizing nomenclature differences, and addressing unsupervised learning coverage limitations.', 'chapters': [{'end': 11032.403, 'start': 10709.565, 'title': 'Optimizing random forest model', 'summary': 'Discusses tuning the parameters of a random forest model, including the number of trees and the m value, to achieve a 94.34% accuracy, and it explains the significance of the m value and suggests further exploration of tree pruning and node analysis.', 'duration': 322.838, 'highlights': ['By tuning the number of trees to 300 and the M value to 8, the model achieved an accuracy of 94.34%, showing a slight improvement over the previous model.', 'The M value represents the number of random variables used for splitting, with a maximum of 8 variables used for each split.', 'Further exploration of tree pruning to prevent overfitting and analysis of the number of nodes in the decision trees is recommended for optimizing the random forest model.']}, {'end': 11680.473, 'start': 11033.264, 'title': 'Variable importance in random forest', 'summary': 'Discusses the importance of variables in a random forest model, emphasizing the top 10 variables, their impact on the dependent variable, building a model using the most impactful variables, and achieving an accuracy of 92% with just four independent variables.', 'duration': 647.209, 'highlights': ['The top 10 important variables in the random forest model are ASTV, MSTV, ALTV, and MEAN, with ASTV having the maximum impact on the dependent variable.', 'Building a model using only the top four important variables (MSTV, ASTV, ALTV, and MEAN) yields an accuracy of 92%, signifying their significant impact on the dependent variable.', 'The relative importance of the independent variables is based on their impact on the dependent variable, with ASTV having the maximum effect, followed by MSTV and ALTV.', 'When using the random forest model, the final class results are obtained, and the prediction type can be set to either class or probability, allowing for manual threshold setting if needed.']}, {'end': 12034.665, 'start': 11680.573, 'title': 'Understanding nomenclature in machine learning', 'summary': 'Discusses the nomenclature differences in machine learning packages, emphasizing the use of type equal to response and class, the superiority of random forest over decision tree and logistic regression, and the trial and error approach for model selection.', 'duration': 354.092, 'highlights': ['Random forest is always better than decision tree and logistic regression for ensemble learning, as it provides a collective result from multiple replicas, leading to superior accuracy. Random forest is always better than decision tree and logistic regression for ensemble learning, as it provides a collective result from multiple replicas, leading to superior accuracy.', 'The nomenclature differences in machine learning packages dictate the type to be used for prediction, such as type equal to response for the C3 class and PROB for probabilities in random forest, emphasizing the importance of understanding package-specific nomenclature for effective usage. The nomenclature differences in machine learning packages dictate the type to be used for prediction, such as type equal to response for the C3 class and PROB for probabilities in random forest, emphasizing the importance of understanding package-specific nomenclature for effective usage.', 'The trial and error approach is essential for model selection, as there is no specific rule for choosing between logistic regression, decision tree, and random forest, and the accuracy of predictions determines the most suitable model for a particular dataset. The trial and error approach is essential for model selection, as there is no specific rule for choosing between logistic regression, decision tree, and random forest, and the accuracy of predictions determines the most suitable model for a particular dataset.']}, {'end': 12431.564, 'start': 12034.905, 'title': 'Unsupervised learning and course curriculum', 'summary': 'Discusses the coverage of unsupervised learning in the next session, including clustering techniques and recommendation engine, and the limitations of deviating from the course curriculum despite requests for additional topics.', 'duration': 396.659, 'highlights': ['The next session will cover unsupervised learning, focusing on clustering techniques and recommendation engine. The next session will cover unsupervised learning, focusing on clustering techniques and recommendation engine.', 'The instructor is limited to teaching topics within the course curriculum, such as k-means clustering and recommendation engine, despite requests for additional topics like PCA and handling missing values and outliers. The instructor is limited to teaching topics within the course curriculum, such as k-means clustering and recommendation engine, despite requests for additional topics like PCA and handling missing values and outliers.', 'The instructor is open to scheduling extra sessions if the operations team allows, to cover additional topics not included in the current course curriculum. The instructor is open to scheduling extra sessions if the operations team allows, to cover additional topics not included in the current course curriculum.']}], 'duration': 1721.999, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ810709565.jpg', 'highlights': ['The model achieved an accuracy of 94.34% by tuning the number of trees to 300 and the M value to 8.', 'The top 10 important variables in the random forest model are ASTV, MSTV, ALTV, and MEAN, with ASTV having the maximum impact on the dependent variable.', 'Random forest is always better than decision tree and logistic regression for ensemble learning, providing a collective result from multiple replicas, leading to superior accuracy.', 'The next session will cover unsupervised learning, focusing on clustering techniques and recommendation engine.']}, {'end': 13382.955, 'segs': [{'end': 12694.542, 'src': 'embed', 'start': 12662.288, 'weight': 1, 'content': [{'end': 12675.117, 'text': 'so this what you get is basically the deviation in the original values or the deviation from the mean of the original values.', 'start': 12662.288, 'duration': 12.829}, {'end': 12677.639, 'text': 'now let me come down.', 'start': 12675.117, 'duration': 2.522}, {'end': 12688.397, 'text': "so what I'll do is I i will add up the total deviation in this sepal length column and i get a value of 102.", 'start': 12677.639, 'duration': 10.758}, {'end': 12693.141, 'text': 'similarly, i will calculate the total deviation in the sepal width column.', 'start': 12688.397, 'duration': 4.744}, {'end': 12694.542, 'text': 'i get this value.', 'start': 12693.141, 'duration': 1.401}], 'summary': 'The total deviation in the sepal length column is 102, and the total deviation in the sepal width column is to be calculated.', 'duration': 32.254, 'max_score': 12662.288, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ812662288.jpg'}, {'end': 12929.247, 'src': 'embed', 'start': 12899.122, 'weight': 2, 'content': [{'end': 12901.364, 'text': 'So we have something known as total sum of squares.', 'start': 12899.122, 'duration': 2.242}, {'end': 12905.907, 'text': 'We have something known as between sum of squares and we have something known as within sum of squares.', 'start': 12901.424, 'duration': 4.483}, {'end': 12910.542, 'text': 'So these are the three important components when it comes to key means algorithm.', 'start': 12906.479, 'duration': 4.063}, {'end': 12915.986, 'text': 'So this was basically an idea to tell you how to calculate the total sum of squares.', 'start': 12911.123, 'duration': 4.863}, {'end': 12929.247, 'text': 'Right So just understand that total sum of squares as you can consider this to be the maybe the total Not exactly error.', 'start': 12917.107, 'duration': 12.14}], 'summary': 'Key components of key means algorithm: total sum of squares, between sum of squares, and within sum of squares.', 'duration': 30.125, 'max_score': 12899.122, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ812899122.jpg'}, {'end': 13038.462, 'src': 'embed', 'start': 12998.616, 'weight': 0, 'content': [{'end': 13010.103, 'text': "similarly, i've done the same thing for all the 150 records, and when you add all of them up, this basically becomes 681.", 'start': 12998.616, 'duration': 11.487}, {'end': 13021.379, 'text': 'right. so when you add this up, you will get 681 over here.', 'start': 13010.103, 'duration': 11.276}, {'end': 13022.419, 'text': 'so any doubt still here.', 'start': 13021.379, 'duration': 1.04}, {'end': 13027.66, 'text': 'what is this ss?', 'start': 13022.419, 'duration': 5.241}, {'end': 13038.002, 'text': 'so this is basically the sum of errors with respect to each record, and when you add all of these, you will get the total sum of squares,', 'start': 13027.66, 'duration': 10.342}, {'end': 13038.462, 'text': 'which is 681.', 'start': 13038.002, 'duration': 0.46}], 'summary': 'Sum of errors for 150 records totals 681.', 'duration': 39.846, 'max_score': 12998.616, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ812998616.jpg'}, {'end': 13385.637, 'src': 'embed', 'start': 13359.814, 'weight': 3, 'content': [{'end': 13364.458, 'text': "So first we'll complete this math and then we'll head to the theory and then we'll head to the practical.", 'start': 13359.814, 'duration': 4.644}, {'end': 13367.921, 'text': 'OK, so just a bit of patience.', 'start': 13364.478, 'duration': 3.443}, {'end': 13375.891, 'text': "I don't know whether you'll just say later on, but just say I have not even understood what is this game means algorithm.", 'start': 13369.386, 'duration': 6.505}, {'end': 13382.955, 'text': "So, while I'm getting the calculations, what you're doing but I'm not just what, what is the significance and the relevance of games?", 'start': 13376.131, 'duration': 6.824}, {'end': 13385.637, 'text': "So you're going to take it.", 'start': 13384.256, 'duration': 1.381}], 'summary': 'Discussion includes math, theory, and practical activities. patience is required. participant seeks understanding of game algorithms and their significance.', 'duration': 25.823, 'max_score': 13359.814, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ813359814.jpg'}], 'start': 12432.265, 'title': 'K-means clustering and total sum of squares', 'summary': 'Covers the mathematical explanation of the k-means clustering algorithm using the iris dataset and the process of calculating the total sum of squares, including obtaining a total deviation of 681. it also explains the implementation of the k-means algorithm to cluster 150 data points into four clusters and emphasizes theoretical understanding and practical application in data analysis.', 'chapters': [{'end': 12816.119, 'start': 12432.265, 'title': 'Understanding k-means clustering', 'summary': 'Covers the mathematical explanation of k-means clustering algorithm using the iris dataset, including the calculation of centered values, centered squares, and the total variance of the dataset.', 'duration': 383.854, 'highlights': ['The chapter explains the calculation of centered values, which involves subtracting each individual value with its mean value to understand the deviation from the mean. ', 'The process of obtaining the centered squares is elucidated, involving squaring the centered values to determine the deviation in the original values from the mean. ', 'The total variance for each column in the dataset is calculated, providing insights into the deviation from the mean for each individual column. ', 'The concept of total sum of squares is introduced, denoting the total deviation present in the dataset with respect to all columns. Total sum of squares: 681', 'The variance for each value of the sepal length column is computed, demonstrating the variance present in each value of the column. Variance of sepal length column: 0.68']}, {'end': 13038.462, 'start': 12816.579, 'title': 'Calculating total sum of squares', 'summary': 'Explains the process of calculating the total sum of squares, a key component in the k-means algorithm, by adding the squared values of each record to obtain a total deviation of 681.', 'duration': 221.883, 'highlights': ['The total sum of squares is a key component in the K-means algorithm. It is mentioned as an important component for the K-means algorithm, indicating its relevance and significance.', 'The total sum of squares for the dataset is 681. The calculated total sum of squares for the dataset is quantified as 681, providing specific numerical information.', 'The process involves adding the squared values of each record to obtain the total sum of squares. The method of obtaining the total sum of squares by adding the squared values of each record is explained, offering a clear understanding of the calculation process.', 'The variance present in one single record is calculated using the explained method. The method for calculating the variance present in a single record is discussed, adding clarity to the calculation process and its application.']}, {'end': 13382.955, 'start': 13038.462, 'title': 'Understanding k-means algorithm', 'summary': 'Explains the process of implementing the k-means algorithm to cluster 150 data points into four clusters, calculating the total deviation (within ss) for each cluster, and emphasizes the theoretical understanding and practical application of the algorithm in data analysis.', 'duration': 344.493, 'highlights': ['The K-Means algorithm clustered 150 data points into four clusters, assigning each data point to a specific cluster. The algorithm successfully clustered 150 data points into four distinct clusters, creating a foundation for further analysis.', 'The process involved segregating the records into respective clusters and calculating the within SS for each cluster, revealing the total deviation present in each cluster. The records were segregated into individual clusters, and the within SS was calculated, providing insights into the total deviation within each cluster.', "The chapter emphasizes the theoretical understanding and practical application of the K-Means algorithm in data analysis, promising to cover the theory and practical aspects in detail after completing the mathematical explanation. The chapter prioritizes the theoretical understanding and practical application of the K-Means algorithm, ensuring a comprehensive grasp of the algorithm's significance and relevance in data analysis."]}], 'duration': 950.69, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ812432265.jpg', 'highlights': ['The K-Means algorithm clustered 150 data points into four clusters, assigning each data point to a specific cluster.', 'The total sum of squares for the dataset is 681.', 'The process involves adding the squared values of each record to obtain the total sum of squares.', 'The chapter emphasizes the theoretical understanding and practical application of the K-Means algorithm in data analysis.']}, {'end': 14998.032, 'segs': [{'end': 13475.059, 'src': 'embed', 'start': 13443.072, 'weight': 2, 'content': [{'end': 13454.758, 'text': 'So we will just give this data to the k-means clustering algorithm and the k-means clustering algorithm will divide this data set into clusters.', 'start': 13443.072, 'duration': 11.686}, {'end': 13457.148, 'text': 'Now These clusters.', 'start': 13455.359, 'duration': 1.789}, {'end': 13466.994, 'text': 'the idea behind clustering is there needs to be high intra-cluster similarity and low sorry,', 'start': 13457.148, 'duration': 9.846}, {'end': 13475.059, 'text': 'there needs to be extremely high intra-cluster similarity and there needs to be high inter-cluster dissimilarity.', 'start': 13466.994, 'duration': 8.065}], 'summary': 'K-means algorithm divides data into clusters for high intra-cluster similarity and high inter-cluster dissimilarity.', 'duration': 31.987, 'max_score': 13443.072, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ813443072.jpg'}, {'end': 13620.478, 'src': 'embed', 'start': 13587.145, 'weight': 7, 'content': [{'end': 13591.708, 'text': 'Data points in cluster two should be similar and all the data points in cluster three should be similar.', 'start': 13587.145, 'duration': 4.563}, {'end': 13600.955, 'text': "So making sense or if you guys still have doubts, again, we'll be covering the entire thing.", 'start': 13593.449, 'duration': 7.506}, {'end': 13604.198, 'text': 'It says that the math will connect to everything.', 'start': 13600.995, 'duration': 3.203}, {'end': 13611.223, 'text': 'Any doubts? Tell you what is a clustering algorithm.', 'start': 13607.86, 'duration': 3.363}, {'end': 13620.478, 'text': 'What are we doing over here? What is the aim behind the clustering algorithm? I am fine with it, yeah.', 'start': 13613.715, 'duration': 6.763}], 'summary': 'Data points in cluster two and three should be similar. aim is clustering algorithm.', 'duration': 33.333, 'max_score': 13587.145, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ813587145.jpg'}, {'end': 14000.488, 'src': 'embed', 'start': 13948.828, 'weight': 0, 'content': [{'end': 13957.572, 'text': 'Now the within cluster similarity should be high and the between cluster dissimilarity should be high.', 'start': 13948.828, 'duration': 8.744}, {'end': 13966.895, 'text': 'So everyone following what is total sum of squares within some of squares, total witnesses and between SS everyone falling till here.', 'start': 13958.612, 'duration': 8.283}, {'end': 13971.557, 'text': 'So this is the basic math behind key means.', 'start': 13968.136, 'duration': 3.421}, {'end': 13975.182, 'text': 'A quick yes or no.', 'start': 13974.522, 'duration': 0.66}, {'end': 13987.465, 'text': 'Yes OK.', 'start': 13985.584, 'duration': 1.881}, {'end': 13996.467, 'text': 'Right So keep these things in mind.', 'start': 13993.486, 'duration': 2.981}, {'end': 13998.447, 'text': 'The last bargain.', 'start': 13997.387, 'duration': 1.06}, {'end': 14000.488, 'text': 'OK The summary tab.', 'start': 13999.348, 'duration': 1.14}], 'summary': 'Key means: high within-cluster similarity, high between-cluster dissimilarity.', 'duration': 51.66, 'max_score': 13948.828, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ813948828.jpg'}, {'end': 14199.055, 'src': 'embed', 'start': 14164.949, 'weight': 1, 'content': [{'end': 14168.41, 'text': 'So this is basically how the k-means algorithm works.', 'start': 14164.949, 'duration': 3.461}, {'end': 14176.865, 'text': 'So now what happened in the k-means is you saw that there were four clusters, but who decides those four clusters?', 'start': 14169.131, 'duration': 7.734}, {'end': 14180.687, 'text': 'and I mean how can we be sure that four clusters are optimum?', 'start': 14176.865, 'duration': 3.822}, {'end': 14186.629, 'text': 'You could have even given the algorithm, could have given us two, three, four, 10, or even hundreds of clusters.', 'start': 14181.047, 'duration': 5.582}, {'end': 14199.055, 'text': 'So what happens is what happens in k-means is we initialize, we initialize some random cluster centers, or in other words,', 'start': 14187.27, 'duration': 11.785}], 'summary': 'K-means algorithm determines cluster centers randomly and may result in varying cluster numbers', 'duration': 34.106, 'max_score': 14164.949, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ814164949.jpg'}, {'end': 14281.073, 'src': 'embed', 'start': 14246.869, 'weight': 3, 'content': [{'end': 14252.953, 'text': 'Now, once the K-means algorithm randomly selects four centers,', 'start': 14246.869, 'duration': 6.084}, {'end': 14261.499, 'text': 'the next step is to assign all the records which are closest to the nearest cluster center.', 'start': 14252.953, 'duration': 8.546}, {'end': 14266.201, 'text': "So we have, let's say, C1, C2, C3, and C4.", 'start': 14262.099, 'duration': 4.102}, {'end': 14271.365, 'text': "Now, which, so the record, so let's say we have record one.", 'start': 14266.802, 'duration': 4.563}, {'end': 14276.63, 'text': "So if record one is closest to C1, then it'll be assigned to C1.", 'start': 14271.726, 'duration': 4.904}, {'end': 14281.073, 'text': "If record two is closest to C3, then it'll be assigned to C3.", 'start': 14277.01, 'duration': 4.063}], 'summary': 'K-means algorithm assigns records to nearest cluster centers.', 'duration': 34.204, 'max_score': 14246.869, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ814246869.jpg'}], 'start': 13384.256, 'title': 'K-means clustering', 'summary': "Covers the basics and concepts of k-means clustering, highlighting the absence of labels in unsupervised learning, the reduction of total sum of squares from 681 to 71 after applying the algorithm, and the iterative process of updating cluster centers until convergence. it also emphasizes the user's role in determining the number of clusters and the algorithm's convergence criteria.", 'chapters': [{'end': 13442.531, 'start': 13384.256, 'title': 'Unsupervised learning: k-means clustering', 'summary': 'Discusses the basics of unsupervised learning, particularly the k-means clustering algorithm, highlighting the key difference between supervised and unsupervised learning and the absence of labels in unsupervised learning.', 'duration': 58.275, 'highlights': ['K-means algorithm is a clustering algorithm, which falls under unsupervised learning.', 'The key difference between supervised and unsupervised learning is the absence of labels in unsupervised learning.', 'In unsupervised learning, the data structure is understood without any labels.']}, {'end': 13836.881, 'start': 13443.072, 'title': 'Understanding k-means clustering', 'summary': 'Explains the concept of k-means clustering, which aims to divide a dataset into clusters with high intra-cluster similarity and high inter-cluster dissimilarity. it covers the calculation of within sum of squares for each cluster and the reduction of total sum of squares from 681 to 71 after applying the k-means algorithm.', 'duration': 393.809, 'highlights': ['The k-means clustering algorithm divides the data set into clusters with high intra-cluster similarity and high inter-cluster dissimilarity. The clustering algorithm aims to ensure that data points within each cluster are extremely similar to each other, while the clusters themselves are dissimilar to each other.', 'The reduction of total sum of squares from 681 to 71 after applying the k-means algorithm signifies the decrease in deviation within the dataset. The total sum of squares decreased from 681 to 71 after applying the k-means algorithm, indicating a significant reduction in deviation within the dataset.', 'The calculation of within sum of squares for each cluster helps in understanding the reduction of deviation within the clusters. The within sum of squares for each cluster is calculated, and the total within sum of squares is obtained to measure the reduction in deviation within the clusters.']}, {'end': 14117.902, 'start': 13837.261, 'title': 'K-means algorithm and cluster similarity', 'summary': 'Explains the k-means algorithm and its impact on cluster similarity, with a reduction in total sum of squares from 681 to 71 after applying the algorithm, indicating high similarity within the clusters and the aim to minimize between sum of squares.', 'duration': 280.641, 'highlights': ['The total sum of squares reduced from 681 to 71 after applying the k-means algorithm, indicating high similarity within the clusters. The reduction from 681 to 71 highlights the impact of the k-means algorithm on increasing cluster similarity, quantifying the improvement in cluster cohesion.', 'The aim of the clustering algorithm is to minimize the between sum of squares and increase the within cluster similarity. The goal to minimize between sum of squares and increase within cluster similarity is a key objective of the clustering algorithm, emphasizing the importance of cluster cohesion and dissimilarity between clusters.', 'The 609 is the between sum of squares between the four clusters, indicating a high dissimilarity between clusters. The value of 609 signifies the significant dissimilarity between clusters, providing a quantifiable measure of the dissimilarity between the clusters after applying the k-means algorithm.']}, {'end': 14518.04, 'start': 14118.462, 'title': 'Understanding k-means algorithm', 'summary': "Explains the key concepts of the k-means algorithm, including the process of initializing cluster centers, assigning data points to clusters based on euclidean distance, and iteratively updating the cluster centers until convergence, with an emphasis on the user's role in determining the number of clusters and the algorithm's convergence criteria.", 'duration': 399.578, 'highlights': ["The user determines the number of clusters when initializing the k-means algorithm, influencing the algorithm's clustering outcome. The user inputs the desired number of clusters, e.g., four, at the start of the k-means algorithm, directly impacting the clustering outcome.", 'The k-means algorithm iteratively updates cluster centers by calculating the mean value of the data points in each cluster, leading to a convergence point where no data points move between clusters. The algorithm iteratively computes the mean value of data points within clusters, updating the cluster centers until convergence, where no data points shift between clusters.', 'Data points are assigned to the nearest cluster center based on the Euclidean distance measure, with the algorithm stopping when the data points remain in the same clusters for consecutive iterations, indicating convergence. The algorithm uses the Euclidean distance measure to assign data points to the nearest cluster center, halting when data points consistently stay in their respective clusters, signifying convergence.']}, {'end': 14998.032, 'start': 14523.925, 'title': 'Understanding k-means algorithm', 'summary': 'Explains the k-means algorithm, where the user specifies the number of clusters (k), random center points are initially assigned, data points are assigned to the nearest cluster centers based on minimum distance, and the process iterates until convergence, with a demonstration and plans for covering imputation in the next session.', 'duration': 474.107, 'highlights': ['The user specifies the number of clusters (K), for example, setting it to three. The number of clusters in K-means is determined by the user, for instance, setting it to three.', 'Initial random center points are assigned by the K-means algorithm. The K-means algorithm randomly assigns initial center points for the clusters.', 'Data points are assigned to the nearest cluster centers based on minimum distance calculation. Data points are allocated to the nearest cluster centers based on the minimum distance calculation in the K-means algorithm.', 'The process iterates until convergence, updating the center points and reassigning data points. The K-means algorithm iterates until convergence, updating center points and reassigning data points to the nearest clusters.', 'Plans for covering imputation in the next session and the use of two packages, misforest and HMISC, will be included. The next session will cover imputation using two packages, misforest and HMISC.']}], 'duration': 1613.776, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ813384256.jpg', 'highlights': ['The k-means algorithm is a clustering algorithm under unsupervised learning.', 'The reduction of total sum of squares from 681 to 71 after applying the k-means algorithm signifies a significant decrease in deviation within the dataset.', 'The aim of the clustering algorithm is to minimize the between sum of squares and increase the within cluster similarity.', 'The user inputs the desired number of clusters, e.g., four, at the start of the k-means algorithm, directly impacting the clustering outcome.', 'The k-means algorithm iteratively updates cluster centers by calculating the mean value of the data points in each cluster, leading to a convergence point where no data points move between clusters.', 'The number of clusters in K-means is determined by the user, for instance, setting it to three.', 'The K-means algorithm randomly assigns initial center points for the clusters.', 'Data points are allocated to the nearest cluster centers based on the minimum distance calculation in the K-means algorithm.', 'The K-means algorithm iterates until convergence, updating center points and reassigning data points to the nearest clusters.']}, {'end': 17531.727, 'segs': [{'end': 15341.755, 'src': 'embed', 'start': 15313.635, 'weight': 8, 'content': [{'end': 15316.277, 'text': 'so these final mean values which you see.', 'start': 15313.635, 'duration': 2.642}, {'end': 15317.678, 'text': 'so this center point.', 'start': 15316.277, 'duration': 1.401}, {'end': 15329.348, 'text': "so if we take this as cluster 1, then for this the mean value would be this again, if we take this as cluster 2, then let's say,", 'start': 15317.678, 'duration': 11.67}, {'end': 15331.95, 'text': 'the mean value of this center point would be this,', 'start': 15329.348, 'duration': 2.602}, {'end': 15336.833, 'text': 'and the mean value of the center point would be this and the mean value of the center point would be this', 'start': 15331.95, 'duration': 4.883}, {'end': 15341.755, 'text': 'After that, we have all of the math over here.', 'start': 15338.474, 'duration': 3.281}], 'summary': 'Final mean values for clusters 1 and 2 determined from center points.', 'duration': 28.12, 'max_score': 15313.635, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ815313635.jpg'}, {'end': 15555.665, 'src': 'embed', 'start': 15517.397, 'weight': 7, 'content': [{'end': 15520.976, 'text': 'now we will look at the rest of the parameters.', 'start': 15517.397, 'duration': 3.579}, {'end': 15522.818, 'text': "So let's do that.", 'start': 15521.857, 'duration': 0.961}, {'end': 15526.781, 'text': 'KM dollar cluster.', 'start': 15525.139, 'duration': 1.642}, {'end': 15530.323, 'text': 'So let me have a glance at this.', 'start': 15528.542, 'duration': 1.781}, {'end': 15534.587, 'text': 'So we see that almost the 38,, 39, 40, 41, 42, 43, 44, 45, six, seven, eight, nine, 10,', 'start': 15531.725, 'duration': 2.862}, {'end': 15536.748, 'text': 'to see that the first 50 records have been clustered into cluster number three.', 'start': 15534.587, 'duration': 2.161}, {'end': 15555.665, 'text': 'So this basically means that all of the setosa species have been grouped into one cluster.', 'start': 15547.383, 'duration': 8.282}], 'summary': 'The first 50 records are clustered: 38 to 45 have been grouped into cluster 3, indicating all setosa species are in one cluster.', 'duration': 38.268, 'max_score': 15517.397, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ815517397.jpg'}, {'end': 15676.879, 'src': 'embed', 'start': 15648.481, 'weight': 6, 'content': [{'end': 15653.465, 'text': "Then you'd have to compare these values with these of the center points over here.", 'start': 15648.481, 'duration': 4.984}, {'end': 15655.507, 'text': 'Normally game ends.', 'start': 15654.366, 'duration': 1.141}, {'end': 15663.472, 'text': 'can we give for entire data set like, or we can give individual or individual column also for k-means.', 'start': 15656.908, 'duration': 6.564}, {'end': 15665.033, 'text': 'while applying.', 'start': 15663.472, 'duration': 1.561}, {'end': 15676.879, 'text': 'you can give it to individual numerical columns or all the numerical columns, but you cannot apply a k-means algorithm on top of a categorical column.', 'start': 15665.033, 'duration': 11.846}], 'summary': 'K-means algorithm can be applied to individual numerical columns or all numerical columns, but not to categorical columns.', 'duration': 28.398, 'max_score': 15648.481, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ815648481.jpg'}, {'end': 15828.862, 'src': 'embed', 'start': 15797.734, 'weight': 1, 'content': [{'end': 15799.615, 'text': 'For cluster three, these are the four mean values.', 'start': 15797.734, 'duration': 1.881}, {'end': 15807, 'text': 'So again, when I say mean values, these are the final values after the k-means algorithm has converged.', 'start': 15799.995, 'duration': 7.005}, {'end': 15813.004, 'text': 'Right, is that clear? Okay.', 'start': 15810.362, 'duration': 2.642}, {'end': 15818.748, 'text': 'Right So again, we have total SS.', 'start': 15814.845, 'duration': 3.903}, {'end': 15821.23, 'text': 'So this was before we applied the algorithm.', 'start': 15819.088, 'duration': 2.142}, {'end': 15824.772, 'text': 'So the 681, this is basically the same.', 'start': 15821.73, 'duration': 3.042}, {'end': 15828.862, 'text': 'So I had basically written down these values in the Excel sheet, 681.3706.', 'start': 15825.441, 'duration': 3.421}], 'summary': 'Cluster three mean values after k-means algorithm convergence: 681.3706', 'duration': 31.128, 'max_score': 15797.734, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ815797734.jpg'}, {'end': 16094.697, 'src': 'embed', 'start': 16068.913, 'weight': 4, 'content': [{'end': 16076.138, 'text': 'so I had told you guys that initially the cluster centers are randomly assigned.', 'start': 16068.913, 'duration': 7.225}, {'end': 16083.415, 'text': 'so when you set the n start to be equal to 10, it will take 10 such random scenarios.', 'start': 16076.138, 'duration': 7.277}, {'end': 16089.696, 'text': 'So in one random scenario, there is a random assignment of clusters.', 'start': 16083.615, 'duration': 6.081}, {'end': 16092.497, 'text': 'In case two, there is a random assignment of clusters.', 'start': 16089.876, 'duration': 2.621}, {'end': 16094.697, 'text': 'In case three, there is a random assignment of clusters.', 'start': 16092.537, 'duration': 2.16}], 'summary': 'Setting n_start to 10 generates 10 random cluster assignments.', 'duration': 25.784, 'max_score': 16068.913, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ816068913.jpg'}, {'end': 17028.749, 'src': 'embed', 'start': 17003.013, 'weight': 3, 'content': [{'end': 17009.954, 'text': 'After that, the next simplest would be to replace these missing values with mean, median, and mode.', 'start': 17003.013, 'duration': 6.941}, {'end': 17012.215, 'text': 'And then we have advanced packages.', 'start': 17010.654, 'duration': 1.561}, {'end': 17016.395, 'text': 'As we have Miss Forest, we have the mice package, and we have the Amelia package.', 'start': 17012.895, 'duration': 3.5}, {'end': 17019.756, 'text': "So in today's class, we'll look at the Miss Forest package.", 'start': 17017.156, 'duration': 2.6}, {'end': 17021.776, 'text': 'So let me load this up.', 'start': 17020.536, 'duration': 1.24}, {'end': 17024.317, 'text': 'Library of Miss Forest.', 'start': 17022.917, 'duration': 1.4}, {'end': 17028.749, 'text': 'And the algorithm which Miss Forest uses is random forest.', 'start': 17025.086, 'duration': 3.663}], 'summary': 'Discussed methods to handle missing values and introduced miss forest package for imputation using random forest algorithm.', 'duration': 25.736, 'max_score': 17003.013, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ817003013.jpg'}, {'end': 17466.339, 'src': 'embed', 'start': 17437.053, 'weight': 0, 'content': [{'end': 17444.014, 'text': 'we have this mix error function, which takes in three parameters.', 'start': 17437.053, 'duration': 6.961}, {'end': 17447.915, 'text': 'first parameter is the imputed data set.', 'start': 17444.014, 'duration': 3.901}, {'end': 17452.036, 'text': 'second parameter is the data set with missing values.', 'start': 17447.915, 'duration': 4.121}, {'end': 17460.958, 'text': 'third parameter is the original data set with no missing values, and when you give this three parameters,', 'start': 17452.036, 'duration': 8.922}, {'end': 17464.598, 'text': 'you will basically get two results over here.', 'start': 17460.958, 'duration': 3.64}, {'end': 17466.339, 'text': 'Let me check them out.', 'start': 17465.479, 'duration': 0.86}], 'summary': 'A function takes three parameters and produces two results.', 'duration': 29.286, 'max_score': 17437.053, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ817437053.jpg'}], 'start': 15001.973, 'title': 'Understanding imputation and k-means algorithm', 'summary': "Covers the basics of imputation and k-means algorithm, demonstrating the replacement of missing data and clustering into four and three clusters, with detailed insights into cluster centers, dissimilarity, and data points distribution among clusters. it also explains the k-means algorithm for data clustering, visualizing and evaluating clustering results, calculating the optimal number of clusters using the elbo method, and iteratively building the k-means algorithm to identify the optimal number of clusters for a given dataset. additionally, it discusses the k-means clustering process, the generation of an elbow plot to determine the optimal number of clusters for a dataset, manually creating 20 clusters using the k-means algorithm, and the process of imputation, including methods such as deleting missing values, replacing with mean, median, or mode, and advanced techniques using packages like miss forest and hmi ac, with an emphasis on miss forest's random forest algorithm for imputation.", 'chapters': [{'end': 15606.198, 'start': 15001.973, 'title': 'Understanding imputation and k-means algorithm', 'summary': 'Covers the basics of imputation and k-means algorithm, using two basic packages, demonstrating how to replace missing data and cluster data into four and three clusters, with detailed insights into cluster centers, dissimilarity, and data points distribution among clusters.', 'duration': 604.225, 'highlights': ['The chapter covers the basics of imputation and K-means algorithm, using two basic packages, demonstrating how to replace missing data and cluster data into four and three clusters, with detailed insights into cluster centers, dissimilarity, and data points distribution among clusters.', 'The K-means algorithm divides the dataset into four clusters using the kmeans.any function, with 11 data points in the first cluster, 12 in the second, 15 in the third, and 12 in the fourth, resulting in a reduction of total within SS from 9.34 to 1.95.', 'The K-means algorithm is also applied to the iris dataset, dividing the data into three clusters, with the first 50 records clustered into cluster number three, while a mix of versicolor and virginica is present in clusters two and one, demonstrating its ability to understand similarities between different properties.']}, {'end': 16216.012, 'start': 15607.619, 'title': 'K-means algorithm for data clustering', 'summary': 'Explains the k-means algorithm for data clustering, demonstrating how to visualize and evaluate clustering results, calculate the optimal number of clusters using the elbo method, and iteratively build the k-means algorithm to identify the optimal number of clusters for a given dataset.', 'duration': 608.393, 'highlights': ['The chapter discusses the limitations of visualizing data with the K-means algorithm, highlighting that it can only visualize two numerical columns out of a possible 10, leading to the need for manual inspection of the remaining columns.', 'It explains the process of evaluating clustering results including calculating the mean values for each cluster after the K-means algorithm has converged, as well as determining the total within SS and between SS values to assess the quality of the clustering.', 'The chapter details the process of identifying the optimal number of clusters using the ELBO method, iterating through different numbers of clusters (from 1 to 20) to find the most suitable clustering scenario for a given dataset.']}, {'end': 16557.598, 'start': 16217.899, 'title': 'K-means algorithm and elbow plot', 'summary': 'Discusses the process of k-means clustering, including the initial random assignment of cluster centers, the use of end start value for multiple assignments, and the generation of an elbow plot to determine the optimal number of clusters for a dataset.', 'duration': 339.699, 'highlights': ['The end start value results in around one million random assignments of cluster centers, from which the optimal assignment is chosen.', 'The elbow plot visually demonstrates the decrease in total within sum of squares (TWSS) as the number of clusters increases, helping to determine the optimal number of clusters for the dataset.', 'The K-means algorithm initially divides the data into three optimal clusters after the random assignment of cluster centers.']}, {'end': 16899.16, 'start': 16557.618, 'title': 'Building k-means algorithm', 'summary': 'Discusses the process of manually creating 20 clusters using the k-means algorithm and iteratively calculating the total within sum of squares for each cluster, achieving convergence within 10 iterations for a small dataset of 150 entries.', 'duration': 341.542, 'highlights': ['The process involves manually creating 20 clusters and obtaining the total within sum of squares (TWSS) values for each cluster. The speaker explains the manual creation of 20 clusters and the calculation of TWSS values for each cluster, providing a clear understanding of the process.', "The algorithm achieves convergence within 10 iterations for a small dataset of 150 entries, highlighting the efficiency of the K-means algorithm for small datasets. The speaker mentions that a small dataset of 150 entries would not take more than 10 iterations for the K-means algorithm to converge, emphasizing the algorithm's efficiency for small datasets.", 'The ratio between SS and total SS is calculated to assess the clustering quality, with a target ratio close to one indicating better clustering. The discussion on calculating the ratio between SS and total SS emphasizes the importance of achieving a ratio close to one for better clustering quality.']}, {'end': 17531.727, 'start': 16899.18, 'title': 'Imputation techniques and packages', 'summary': "Discusses the process of imputation, including methods such as deleting missing values, replacing with mean, median, or mode, and advanced techniques using packages like miss forest and hmi ac, with an emphasis on miss forest's random forest algorithm for imputation, introducing missing values, and identifying errors in imputation.", 'duration': 632.547, 'highlights': ['Miss Forest package uses random forest algorithm for imputation Miss Forest package utilizes the random forest algorithm for imputing missing values, providing a robust method for handling missing data.', 'Introducing 30% missing values into the original data set using broad NA function The process involves introducing 30% missing values into the original data set using the broad NA function, demonstrating a practical application of creating missing values for imputation.', 'Error in imputation for categorical values is 13% and for numerical values is 21% The mix error function is used to calculate the error in imputation, revealing a 13% error in imputing categorical values and a 21% error in imputing numerical values within the dataset.']}], 'duration': 2529.754, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ815001973.jpg', 'highlights': ['The K-means algorithm divides the dataset into four clusters using the kmeans.any function, with 11 data points in the first cluster, 12 in the second, 15 in the third, and 12 in the fourth, resulting in a reduction of total within SS from 9.34 to 1.95.', 'The K-means algorithm is also applied to the iris dataset, dividing the data into three clusters, with the first 50 records clustered into cluster number three, while a mix of versicolor and virginica is present in clusters two and one, demonstrating its ability to understand similarities between different properties.', 'The chapter details the process of identifying the optimal number of clusters using the ELBO method, iterating through different numbers of clusters (from 1 to 20) to find the most suitable clustering scenario for a given dataset.', 'The elbow plot visually demonstrates the decrease in total within sum of squares (TWSS) as the number of clusters increases, helping to determine the optimal number of clusters for the dataset.', 'The process involves manually creating 20 clusters and obtaining the total within sum of squares (TWSS) values for each cluster. The speaker explains the manual creation of 20 clusters and the calculation of TWSS values for each cluster, providing a clear understanding of the process.', "The algorithm achieves convergence within 10 iterations for a small dataset of 150 entries, highlighting the efficiency of the K-means algorithm for small datasets. The speaker mentions that a small dataset of 150 entries would not take more than 10 iterations for the K-means algorithm to converge, emphasizing the algorithm's efficiency for small datasets.", 'Miss Forest package uses random forest algorithm for imputation Miss Forest package utilizes the random forest algorithm for imputing missing values, providing a robust method for handling missing data.', 'Introducing 30% missing values into the original data set using broad NA function The process involves introducing 30% missing values into the original data set using the broad NA function, demonstrating a practical application of creating missing values for imputation.', 'Error in imputation for categorical values is 13% and for numerical values is 21% The mix error function is used to calculate the error in imputation, revealing a 13% error in imputing categorical values and a 21% error in imputing numerical values within the dataset.']}, {'end': 18604.523, 'segs': [{'end': 17562.924, 'src': 'embed', 'start': 17533.468, 'weight': 1, 'content': [{'end': 17538.952, 'text': 'So initially we have this data set with no missing values here with.', 'start': 17533.468, 'duration': 5.484}, {'end': 17542.634, 'text': 'OK, so this is it fine if I explain from the beginning.', 'start': 17539.112, 'duration': 3.522}, {'end': 17552.04, 'text': "Is it okay? Yeah, then I'll do that.", 'start': 17545.638, 'duration': 6.402}, {'end': 17555.502, 'text': 'So let me close these two tabs.', 'start': 17553.201, 'duration': 2.301}, {'end': 17562.924, 'text': 'So we have this original Iris data set, which has no missing values, right? So this is a perfectly good data set.', 'start': 17556.262, 'duration': 6.662}], 'summary': 'Original iris dataset contains no missing values, making it a good dataset.', 'duration': 29.456, 'max_score': 17533.468, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ817533468.jpg'}, {'end': 17718.206, 'src': 'embed', 'start': 17592.101, 'weight': 0, 'content': [{'end': 17598.244, 'text': 'First parameter is the data frame into which I want to introduce the missing values.', 'start': 17592.101, 'duration': 6.143}, {'end': 17606.039, 'text': 'The second parameter is the percentage of missing values I want to introduce in this data frame.', 'start': 17598.735, 'duration': 7.304}, {'end': 17608.501, 'text': 'So no any or the number of.', 'start': 17606.54, 'duration': 1.961}, {'end': 17610.382, 'text': 'So basically the stakes in the percentage.', 'start': 17608.681, 'duration': 1.701}, {'end': 17621.669, 'text': 'So I want to introduce 30 percent of missing values into this data frame and I will store that result in iris dot.', 'start': 17610.802, 'duration': 10.867}, {'end': 17623.73, 'text': 'This one is my.', 'start': 17621.689, 'duration': 2.041}, {'end': 17633.384, 'text': 'Like normally anywhere, I mean, whatever the data set is, we would take all the missing values.', 'start': 17627.321, 'duration': 6.063}, {'end': 17642.209, 'text': 'We want to take every missing value, use a median more or mean out of these three.', 'start': 17633.464, 'duration': 8.745}, {'end': 17645.631, 'text': 'I am not treating the missing values.', 'start': 17643.11, 'duration': 2.521}, {'end': 17648.432, 'text': 'I am introducing missing values over here.', 'start': 17645.851, 'duration': 2.581}, {'end': 17655.196, 'text': "So what I'm doing is this is my original data set, right? So there are no missing values.", 'start': 17649.913, 'duration': 5.283}, {'end': 17655.957, 'text': 'Let me show that.', 'start': 17655.256, 'duration': 0.701}, {'end': 17656.337, 'text': 'So some.', 'start': 17655.977, 'duration': 0.36}, {'end': 17663.804, 'text': 'is that any iris dollar sepal length.', 'start': 17658.363, 'duration': 5.441}, {'end': 17666.865, 'text': 'so there are no missing values in this column again.', 'start': 17663.804, 'duration': 3.061}, {'end': 17672.226, 'text': 'let me do that for sepal width again.', 'start': 17666.865, 'duration': 5.361}, {'end': 17674.806, 'text': 'no missing values.', 'start': 17672.226, 'duration': 2.58}, {'end': 17676.226, 'text': 'for petal length.', 'start': 17674.806, 'duration': 1.42}, {'end': 17677.567, 'text': 'no missing values again.', 'start': 17676.226, 'duration': 1.341}, {'end': 17685.508, 'text': 'if we do it for petal width and species as well, there would be no missing values at all in this entire data set.', 'start': 17677.567, 'duration': 7.941}, {'end': 17695.226, 'text': "now, Just for the demo purpose, what I'm doing is I am introducing 30% missing values randomly into this dataset right?", 'start': 17685.508, 'duration': 9.718}, {'end': 17697.747, 'text': 'So this 30% is totally random.', 'start': 17695.666, 'duration': 2.081}, {'end': 17704.452, 'text': 'So randomly, I am introducing 30% missing values into this dataset.', 'start': 17698.268, 'duration': 6.184}, {'end': 17709.635, 'text': 'And to do that, I will use the prod any function.', 'start': 17704.992, 'duration': 4.643}, {'end': 17718.206, 'text': 'So this prod any function takes in the original dataset and introduces So again, this percentage is defined.', 'start': 17710.416, 'duration': 7.79}], 'summary': 'Introducing 30% missing values into a data frame using prod any function.', 'duration': 126.105, 'max_score': 17592.101, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ817592101.jpg'}], 'start': 17533.468, 'title': 'Missing value imputation', 'summary': 'Covers the introduction of missing values using the miss forest package, with a demonstration of introducing 30% missing values randomly and imputing them using the random forest algorithm. it also discusses error rates in imputing categorical and continuous values, revealing a 4% error for categorical values and a 16% error for continuous values, and the optimization of the algorithm. additionally, it addresses the imputation of salaries for high profile mlas using domain knowledge and the impact of missing values on the model.', 'chapters': [{'end': 18087.087, 'start': 17533.468, 'title': 'Introducing missing values using miss forest package', 'summary': 'Discusses the process of introducing missing values into a dataset using the miss forest package, with a demonstration of introducing 30% missing values randomly and the method of imputing these missing values using the random forest algorithm, resulting in the final imputed data set.', 'duration': 553.619, 'highlights': ['The Miss Forest package is used to introduce missing values into the original Iris dataset, with a demonstration of introducing 30% missing values randomly. The Miss Forest package is utilized to introduce 30% missing values randomly into the original Iris dataset.', 'The process of imputing missing values is explained, including the methods of deletion, imputation with mean, median, and mode, and imputation using machine learning algorithms such as the random forest algorithm. The chapter explains the methods of imputing missing values, including deletion, imputation with mean, median, and mode, and imputation using machine learning algorithms like the random forest algorithm.', "The Miss Forest package's functionality of imputing missing values with the random forest algorithm is detailed, with an explanation of how the algorithm works and its application to both numerical and categorical values. The Miss Forest package's functionality of imputing missing values with the random forest algorithm is detailed, including an explanation of how the algorithm works and its application to both numerical and categorical values."]}, {'end': 18344.996, 'start': 18087.921, 'title': 'Error imputation with misforest', 'summary': 'Discusses the error rates in imputing categorical and continuous values using the misforest algorithm, revealing a 4% error for categorical values and a 16% error for continuous values, and the inquiry into optimizing the algorithm by running it multiple times to select the best imputation.', 'duration': 257.075, 'highlights': ['The error rates for imputing categorical and continuous values using the misforest algorithm are 4% and 16% respectively. This reveals quantifiable data on the error rates for imputing categorical and continuous values using the misforest algorithm.', 'The discussion explores optimizing the misforest algorithm by running it multiple times to select the best imputation. The inquiry into optimizing the misforest algorithm by running it multiple times to select the best imputation is highlighted, demonstrating the consideration of error variability and the need for multiple runs to identify the optimal outcome.', 'The distinction between missing at random and not missing at random in the context of imputation is mentioned. The distinction between missing at random and not missing at random in the context of imputation is revealed, providing insight into different scenarios of data missingness.']}, {'end': 18604.523, 'start': 18344.996, 'title': 'Mla salary imputation', 'summary': 'Discusses the imputation of salaries for high profile mlas using domain knowledge and compares the missing values in the dataset, highlighting the importance of domain knowledge and the contribution of missing values to the model.', 'duration': 259.527, 'highlights': ['The importance of domain knowledge in imputing missing salaries for high profile MLAs is emphasized, with a focus on comparing the missing values and the contribution of the available data to the model. Importance of domain knowledge, comparison of missing values, contribution of available data', 'The significance of including columns with missing values is highlighted, emphasizing the contribution of the available data even when a large percentage of values are missing. Significance of including columns with missing values, contribution of available data', 'The interdependence of columns in the dataset is discussed, emphasizing the relation between variables and the use of domain knowledge in imputation. Interdependence of columns, relation between variables, use of domain knowledge in imputation']}], 'duration': 1071.055, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ817533468.jpg', 'highlights': ['The Miss Forest package is utilized to introduce 30% missing values randomly into the original Iris dataset.', 'The chapter explains the methods of imputing missing values, including deletion, imputation with mean, median, and mode, and imputation using machine learning algorithms like the random forest algorithm.', "The Miss Forest package's functionality of imputing missing values with the random forest algorithm is detailed, including an explanation of how the algorithm works and its application to both numerical and categorical values.", 'The error rates for imputing categorical and continuous values using the misforest algorithm are 4% and 16% respectively.', 'The inquiry into optimizing the misforest algorithm by running it multiple times to select the best imputation is highlighted, demonstrating the consideration of error variability and the need for multiple runs to identify the optimal outcome.', 'The distinction between missing at random and not missing at random in the context of imputation is revealed, providing insight into different scenarios of data missingness.', 'Importance of domain knowledge, comparison of missing values, contribution of available data', 'Significance of including columns with missing values, emphasizing the contribution of the available data even when a large percentage of values are missing.', 'Interdependence of columns, relation between variables, use of domain knowledge in imputation']}, {'end': 20197.972, 'segs': [{'end': 18631.886, 'src': 'embed', 'start': 18604.663, 'weight': 7, 'content': [{'end': 18608.666, 'text': 'And as I had explained with respect to the Miss Forest, I mean, how it works.', 'start': 18604.663, 'duration': 4.003}, {'end': 18611.782, 'text': "So let's see if I want to impute this value.", 'start': 18609.261, 'duration': 2.521}, {'end': 18620.763, 'text': 'Now what it will do is this will take this as the dependent variable and these four as the independent variables.', 'start': 18612.482, 'duration': 8.281}, {'end': 18629.245, 'text': 'So what it will do is it will read this record and it learns that if it is three and a 0.2 setosa, this will be a value.', 'start': 18621.403, 'duration': 7.842}, {'end': 18631.886, 'text': 'If this is the case, then this will be the value.', 'start': 18629.645, 'duration': 2.241}], 'summary': 'Explained imputation process using 4 independent variables and dependent variable.', 'duration': 27.223, 'max_score': 18604.663, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ818604663.jpg'}, {'end': 19788.271, 'src': 'embed', 'start': 19739.282, 'weight': 1, 'content': [{'end': 19746.948, 'text': 'So if the method is PMM, then it has predictive mean matching, which works for categorical columns.', 'start': 19739.282, 'duration': 7.666}, {'end': 19750.071, 'text': "It basically depends on which method you're using.", 'start': 19747.769, 'duration': 2.302}, {'end': 19764.574, 'text': 'okay. so any other questions?', 'start': 19752.99, 'duration': 11.584}, {'end': 19765.575, 'text': 'yeah, what I mean?', 'start': 19764.574, 'duration': 1.001}, {'end': 19772.857, 'text': 'I mean tell like oh, one or two more packages which we can, I can refer for that.', 'start': 19765.575, 'duration': 7.282}, {'end': 19784.689, 'text': "um, let's see, This is what we have.", 'start': 19772.857, 'duration': 11.832}, {'end': 19786.67, 'text': 'solution imputation in R.', 'start': 19784.689, 'duration': 1.981}, {'end': 19788.271, 'text': 'which imputation is best?', 'start': 19786.67, 'duration': 1.601}], 'summary': 'Pmm method works for categorical columns. also, inquiring about best imputation methods in r.', 'duration': 48.989, 'max_score': 19739.282, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ819739282.jpg'}, {'end': 20004.657, 'src': 'embed', 'start': 19910.632, 'weight': 0, 'content': [{'end': 19912.132, 'text': 'maybe I will go for the video.', 'start': 19910.632, 'duration': 1.5}, {'end': 19915.214, 'text': 'I mean I just need guidance for that.', 'start': 19912.132, 'duration': 3.082}, {'end': 19919.876, 'text': 'so scaling depends on what you do.', 'start': 19915.214, 'duration': 4.662}, {'end': 19923.747, 'text': 'okay? so tell me, what do you understand by scaling like?', 'start': 19919.876, 'duration': 3.871}, {'end': 19924.908, 'text': 'how much deviation?', 'start': 19923.747, 'duration': 1.161}, {'end': 19930.71, 'text': 'was you like keeping everything constant for how much deviation you need like that?', 'start': 19924.908, 'duration': 5.802}, {'end': 19934.271, 'text': "okay, um okay, let's again take this iris data set.", 'start': 19930.71, 'duration': 3.561}, {'end': 19939.993, 'text': 'so view of iris.', 'start': 19934.271, 'duration': 5.722}, {'end': 19956.241, 'text': 'now let me actually look at the help of this Right.', 'start': 19939.993, 'duration': 16.248}, {'end': 19961.663, 'text': "So over here it is written that I mean, you'd have to mute yourself, please.", 'start': 19956.781, 'duration': 4.882}, {'end': 19963.063, 'text': "So there's a lot of background noise.", 'start': 19961.723, 'duration': 1.34}, {'end': 19964.363, 'text': 'Right now.', 'start': 19963.603, 'duration': 0.76}, {'end': 19968.624, 'text': 'So this is the measurements in centimeters of the variables.', 'start': 19964.383, 'duration': 4.241}, {'end': 19970.905, 'text': 'Right So this is in centimeters.', 'start': 19968.804, 'duration': 2.101}, {'end': 19971.965, 'text': 'This is in centimeters.', 'start': 19970.965, 'duration': 1}, {'end': 19974.186, 'text': 'And these two are also in centimeters.', 'start': 19972.025, 'duration': 2.161}, {'end': 19983.368, 'text': "So you'd have to scale the values where the columns or the different columns are present in different metrics.", 'start': 19974.846, 'duration': 8.522}, {'end': 19985.344, 'text': 'Right again.', 'start': 19984.188, 'duration': 1.156}, {'end': 19988.466, 'text': "so let's see if you're building the key means algorithm.", 'start': 19985.344, 'duration': 3.122}, {'end': 19997.152, 'text': "now, when you're building the key means algorithm, you have to make sure that the numerical columns which are passing in they are normalized.", 'start': 19988.466, 'duration': 8.686}, {'end': 20004.657, 'text': "so let's say, if this was in centimeters, this was in meters, this was in kilometers and this was in maybe light years, right?", 'start': 19997.152, 'duration': 7.505}], 'summary': 'Understanding scaling and normalization for numerical columns in data sets.', 'duration': 94.025, 'max_score': 19910.632, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ819910632.jpg'}], 'start': 18604.663, 'title': 'Imputation and scaling in data analysis', 'summary': 'Covers the process of imputing missing values in a dataset, discussing various imputation methods and emphasizing the significance of imputation in the data science lifecycle. it also explains the importance of scaling in data analysis to normalize numerical columns and avoid scale-specific issues in algorithms.', 'chapters': [{'end': 18970.695, 'start': 18604.663, 'title': 'Imputation of missing values in data science', 'summary': 'Explains the process of imputing missing values in a dataset, emphasizing the importance of imputation over omitting entire columns, and the significance of imputation in the data science lifecycle, while highlighting the use of random forest algorithm for imputation.', 'duration': 366.032, 'highlights': ['The process of imputing missing values in a dataset is crucial, and omitting entire columns is considered worse, emphasizing the importance of imputation over omission.', 'The significance of imputation in the data science lifecycle is emphasized, with a distinction made based on the stage of data manipulation, highlighting the importance of imputation at different stages.', 'The use of random forest algorithm for imputation is clarified, emphasizing that imputation is independent of the models built later, and the purpose of imputation is to obtain a tidy dataset with minimal error.']}, {'end': 19530.082, 'start': 18971.542, 'title': 'Imputation methods in data analysis', 'summary': "Discusses the usage of different imputation methods such as mean, median, and random forest algorithm for handling missing values in data analysis, highlighting the application of hmisc package's impute function and its ability to impute individual columns with various methods.", 'duration': 558.54, 'highlights': ['The Random Forest algorithm learns from the training set and predicts the values on the test set, with an example of using 149 records for training and one record for testing. The Random Forest algorithm learns from a training set and predicts values on a test set, using 149 records for training and one record for testing.', "The HMISC package's impute function allows for imputing individual columns with different methods, such as imputing sepal length with mean, sepal width with a random value, and petal length with median. The HMISC package's impute function enables imputing individual columns with different methods, e.g., imputing sepal length with mean, sepal width with a random value, and petal length with median.", "The transcript discusses the imputation of missing values using the random forest algorithm and the HMISC package's impute function, showcasing the versatility in handling missing data through different methods. The transcript highlights the usage of the random forest algorithm and the HMISC package's impute function for handling missing values with various methods, demonstrating versatility in handling missing data."]}, {'end': 19910.632, 'start': 19530.102, 'title': 'Data imputation and analysis in r', 'summary': 'Discusses the challenges and methods for handling missing data in r, emphasizing the importance of understanding the dataset and selecting appropriate imputation methods based on data characteristics, with a focus on the mice package for multiple imputations and the selection of suitable algorithms for both classification and regression.', 'duration': 380.53, 'highlights': ['The mice package in R provides multiple imputations for handling missing values, offering different methods such as predictive mean matching (PMM) for categorical columns, allowing for comprehensive analysis and selection of suitable imputation values.', 'Understanding the dataset is crucial for selecting the appropriate imputation method, with categorical columns requiring classification algorithms and numerical values necessitating the consideration of data distribution around mean and median for optimal imputation.', 'The industry commonly utilizes the mice package for handling missing values, with additional references to solutions for imputation in R including mys, Amelia, missforest, HMISC, and MI, providing pre-built datasets with missing values for practical exploration and analysis.', 'Exploration and analysis of datasets with pre-built missing values can be facilitated through the mice package in R, which offers the ability to work with various pre-existing datasets to understand and address missing value challenges effectively.']}, {'end': 20197.972, 'start': 19910.632, 'title': 'Understanding scaling in data analysis', 'summary': 'Explains the importance of scaling in data analysis, emphasizing how scaling helps in normalizing numerical columns and ensuring all columns belong to the same scale, thereby avoiding scale-specific issues in algorithms.', 'duration': 287.34, 'highlights': ['Scaling helps in converting all columns into one particular scale, ensuring that all columns belong to the same scale, thus preventing erroneous values in algorithms. Scaling is essential to avoid erroneous values in algorithms by converting all columns into one particular scale, ensuring uniformity and preventing scale-specific issues.', 'Scaling involves subtracting the mean and dividing by the variance, which cancels out the units and converts the values into a range without any unit. Scaling involves a process of subtracting the mean and dividing by the variance to convert values into a range without any unit, ensuring uniformity and removing distinctions between columns.', "The chapter also discusses the use of the 'scale' function to remove non-numerical columns and obtain the scaled version of the data. The 'scale' function is used to remove non-numerical columns and obtain the scaled version of the data, contributing to the normalization and uniformity of the columns."]}], 'duration': 1593.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ818604663.jpg', 'highlights': ['The process of imputing missing values in a dataset is crucial, emphasizing the importance of imputation over omission.', 'The significance of imputation in the data science lifecycle is emphasized, highlighting the importance of imputation at different stages.', 'The use of random forest algorithm for imputation is clarified, emphasizing that imputation is independent of the models built later.', 'The Random Forest algorithm learns from the training set and predicts the values on the test set, using 149 records for training and one record for testing.', "The HMISC package's impute function enables imputing individual columns with different methods, e.g., imputing sepal length with mean, sepal width with a random value, and petal length with median.", "The transcript highlights the usage of the random forest algorithm and the HMISC package's impute function for handling missing values with various methods, demonstrating versatility in handling missing data.", 'The mice package in R provides multiple imputations for handling missing values, offering different methods such as predictive mean matching (PMM) for categorical columns.', 'Understanding the dataset is crucial for selecting the appropriate imputation method, with categorical columns requiring classification algorithms and numerical values necessitating the consideration of data distribution around mean and median for optimal imputation.', 'The industry commonly utilizes the mice package for handling missing values, with additional references to solutions for imputation in R including mys, Amelia, missforest, HMISC, and MI.', 'Scaling is essential to avoid erroneous values in algorithms by converting all columns into one particular scale, ensuring uniformity and preventing scale-specific issues.', 'Scaling involves a process of subtracting the mean and dividing by the variance to convert values into a range without any unit, ensuring uniformity and removing distinctions between columns.', "The 'scale' function is used to remove non-numerical columns and obtain the scaled version of the data, contributing to the normalization and uniformity of the columns."]}, {'end': 21991.071, 'segs': [{'end': 20357.361, 'src': 'embed', 'start': 20324.943, 'weight': 0, 'content': [{'end': 20326.444, 'text': 'i am not available tomorrow.', 'start': 20324.943, 'duration': 1.501}, {'end': 20329.305, 'text': 'so the next class would be on saturday.', 'start': 20326.444, 'duration': 2.861}, {'end': 20335.409, 'text': 'so on saturday we will be either starting with association rule mining or recommendation engine.', 'start': 20329.305, 'duration': 6.104}, {'end': 20339.633, 'text': 'okay, i mean i think only in unsupervised.', 'start': 20336.832, 'duration': 2.801}, {'end': 20342.455, 'text': 'we are covering only the games.', 'start': 20339.633, 'duration': 2.822}, {'end': 20344.275, 'text': 'yes, um, no.', 'start': 20342.455, 'duration': 1.82}, {'end': 20351.979, 'text': 'so even association rule mining comes under unsupervised and recommendation engine also comes under unsupervised.', 'start': 20344.275, 'duration': 7.704}, {'end': 20357.361, 'text': 'but in the recommendation engine we are doing like just a moment.', 'start': 20351.979, 'duration': 5.382}], 'summary': 'Next class on saturday, covering association rule mining and recommendation engine under unsupervised learning.', 'duration': 32.418, 'max_score': 20324.943, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ820324943.jpg'}, {'end': 20432.208, 'src': 'embed', 'start': 20403.939, 'weight': 1, 'content': [{'end': 20418.437, 'text': 'Right. Yeah, I understood, but I was just like you know, we are in prediction normally as of now, before we said like so, when you say predicting, we are OK,', 'start': 20403.939, 'duration': 14.498}, {'end': 20421.7, 'text': "so let's go when it comes to a movie recommendation.", 'start': 20418.437, 'duration': 3.263}, {'end': 20426.564, 'text': 'So in movie recommendation, we sort of try to.', 'start': 20422.501, 'duration': 4.063}, {'end': 20430.287, 'text': 'OK, so again, this is not exactly prediction.', 'start': 20427.465, 'duration': 2.822}, {'end': 20432.208, 'text': 'So we have a cluster.', 'start': 20430.667, 'duration': 1.541}], 'summary': 'Exploring movie recommendation using clustering, not prediction.', 'duration': 28.269, 'max_score': 20403.939, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ820403939.jpg'}, {'end': 20983.224, 'src': 'embed', 'start': 20949.8, 'weight': 2, 'content': [{'end': 20956.023, 'text': 'Okay, how many of you know what a scalar value is? Quick responses, please.', 'start': 20949.8, 'duration': 6.223}, {'end': 20961.225, 'text': 'What is a scalar? Which has only magnitude but no direction.', 'start': 20956.523, 'duration': 4.702}, {'end': 20963.8, 'text': 'Yes, right.', 'start': 20962.418, 'duration': 1.382}, {'end': 20965.502, 'text': 'A scalar only has magnitude.', 'start': 20963.88, 'duration': 1.622}, {'end': 20971.651, 'text': 'But then when it comes to a vector, it has magnitude as well as direction.', 'start': 20965.803, 'duration': 5.848}, {'end': 20977.059, 'text': "So this is a vector which we've represented with the letter A.", 'start': 20972.532, 'duration': 4.527}, {'end': 20983.224, 'text': 'So this has a magnitude associated with it and the magnitude would be so this.', 'start': 20977.659, 'duration': 5.565}], 'summary': 'Scalar has only magnitude, while vector has magnitude and direction.', 'duration': 33.424, 'max_score': 20949.8, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ820949800.jpg'}, {'end': 21328.425, 'src': 'embed', 'start': 21296.069, 'weight': 4, 'content': [{'end': 21305.661, 'text': 'right, so the dot product of a vector with itself will give me the square of the magnitude of the vector.', 'start': 21296.069, 'duration': 9.592}, {'end': 21306.983, 'text': 'so again, a quick response.', 'start': 21305.661, 'duration': 1.322}, {'end': 21307.884, 'text': 'are you guys able to follow me?', 'start': 21306.983, 'duration': 0.901}, {'end': 21313.442, 'text': 'So now, after this, we have something known as a unit vector.', 'start': 21309.221, 'duration': 4.221}, {'end': 21322.864, 'text': 'So a unit vector has the same direction as of the vector, but its magnitude will be one.', 'start': 21314.042, 'duration': 8.822}, {'end': 21328.425, 'text': "So let's say if I want to get the unit vector of A over here.", 'start': 21323.644, 'duration': 4.781}], 'summary': 'Dot product gives square of vector magnitude. unit vector has magnitude one.', 'duration': 32.356, 'max_score': 21296.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ821296069.jpg'}, {'end': 21525.837, 'src': 'embed', 'start': 21491.456, 'weight': 3, 'content': [{'end': 21496.379, 'text': 'So opposite is B2 and hypotenuse is modulus of B.', 'start': 21491.456, 'duration': 4.923}, {'end': 21500.781, 'text': 'So sine beta becomes B2 upon modulus of B.', 'start': 21496.379, 'duration': 4.402}, {'end': 21505.703, 'text': 'Similarly, cos beta becomes adjacent upon hypotenuse.', 'start': 21501.44, 'duration': 4.263}, {'end': 21511.827, 'text': 'So adjacent is B1 and hypotenuse is modulus of B.', 'start': 21506.043, 'duration': 5.784}, {'end': 21516.571, 'text': "So we've calculated sin alpha, cos alpha, sin beta and cos beta.", 'start': 21511.827, 'duration': 4.744}, {'end': 21525.837, 'text': 'Now, this theta is nothing but the difference or the angle between vector A and vector B.', 'start': 21517.511, 'duration': 8.326}], 'summary': 'Calculated sine and cosine values for beta and alpha, and determined the angle between vectors a and b.', 'duration': 34.381, 'max_score': 21491.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ821491456.jpg'}], 'start': 20198.152, 'title': 'Data scaling, unsupervised learning, and vector algebra', 'summary': 'Covers data scaling confusion, intellipaat code usage, and issues with the iris dataset. it also discusses unsupervised learning, recommendation engines, user-based and item-based collaborative filtering, and time series analysis. additionally, it explains vector algebra basics including vector addition, subtraction, dot product, and unit vectors, as well as cosine similarity for measuring vector similarity.', 'chapters': [{'end': 20281.649, 'start': 20198.152, 'title': 'Data scaling in data analysis', 'summary': 'Discusses the confusion around data scaling and specifying the dataset, with a mention of using the code from intellipaat 65 and encountering issues with the iris dataset.', 'duration': 83.497, 'highlights': ['The confusion around data scaling and specifying the dataset, with a mention of using the code from Intellipaat 65 and encountering issues with the Iris dataset.', 'Difficulty in understanding the code and lack of practice with the dataset, leading to uncertainty about the data scaling process.']}, {'end': 20752.902, 'start': 20283.97, 'title': 'Unsupervised learning & recommendation engine', 'summary': 'Discusses the concepts of unsupervised learning, recommendation engine, user-based and item-based collaborative filtering, and time series analysis as part of machine learning, focusing on the clustering and grouping of similar items or users for recommendation purposes.', 'duration': 468.932, 'highlights': ['The chapter covers the concepts of unsupervised learning, recommendation engine, user-based and item-based collaborative filtering, and time series analysis as part of machine learning The chapter delves into various concepts of unsupervised learning, recommendation engine, user-based and item-based collaborative filtering, and time series analysis, providing a comprehensive overview of these topics within the realm of machine learning.', 'The chapter emphasizes the clustering and grouping of similar items or users for recommendation purposes It focuses on the core concept of clustering and grouping similar items or users in unsupervised learning for the purpose of making recommendations, providing insights into the methodology and purpose of this approach.', 'Explanation of user-based and item-based collaborative filtering for recommending items based on user behavior or finding similar items The chapter explains the distinction between user-based and item-based collaborative filtering, highlighting how recommendations are made based on user behavior or by finding similar items, showcasing the practical application of these concepts in recommendation systems.', 'Insights into time series analysis and its focus on understanding how variables change over time It provides insights into time series analysis, elucidating its focus on understanding how variables change with time, exemplifying the application of this analysis in exploring temporal trends and patterns within datasets.', 'The mention of sentiment analysis as an application of machine learning for understanding patterns and sentiments The chapter mentions sentiment analysis as an application of machine learning, highlighting its role in understanding patterns and sentiments, shedding light on its relevance in analyzing and interpreting textual data.', 'Introduction to principal component analysis (PCA) as an important topic for self-paced learning It introduces principal component analysis (PCA) as an important topic, indicating its inclusion in the self-paced learning section, showcasing its significance in dimensionality reduction and feature extraction within machine learning.']}, {'end': 21394.006, 'start': 20753.342, 'title': 'Vector algebra basics', 'summary': 'Covers the basic concepts of vector algebra, including vector addition, subtraction, dot product, angle between vectors, and unit vectors, explaining their properties and calculations with examples. key points include the definition of vectors, vector addition and subtraction, dot product calculation, and the concept of unit vectors.', 'duration': 640.664, 'highlights': ['The dot product of vectors A and B is obtained by multiplying their coordinates and adding them up, resulting in a scalar value, such as 2 in the given example.', 'Vector addition and subtraction are demonstrated with examples, showing the results of A plus B and A minus B operations.', 'The concept of unit vectors is explained, illustrating how to obtain a unit vector from a given vector by dividing it by its magnitude.', 'The basic definition of vectors, including their magnitude and direction, is provided, demonstrating calculations for vector A and vector B.', 'The explanation covers the angle between two vectors and the method to identify it, along with the distinction between scalar values and vectors.']}, {'end': 21991.071, 'start': 21394.699, 'title': 'Vector angle cosine similarity', 'summary': 'Discusses how to calculate the sine and cosine values of angles between vectors, and how the cosine similarity is used to measure the similarity between vectors, which is crucial for recommendation engines and collaborative filtering.', 'duration': 596.372, 'highlights': ['The cosine of the angle between the two vectors is the dot product divided by the magnitude of those two vectors, crucial for recommendation engines and collaborative filtering. The numerator represents the dot product between vector A and vector B, and the denominator denotes the product of the magnitude of these two vectors. The cosine of the angle between the two vectors is basically the dot product divided by the magnitude of those two vectors.', 'As the angle between two vectors decreases, the cosine theta between them increases, indicating higher similarity, which is essential for recommendation engines and collaborative filtering. The cosine value increases as the angle between the vectors decreases, implying that the lesser the angle between these two, the higher the similarity between these two vectors. This is crucial for recommendation engines and collaborative filtering.', 'The lesser the angle between the vectors, the more similar they are, and the greater the cosine theta value, the greater the similarity between the vectors, essential for recommendation engines and collaborative filtering. The lesser the angle between A and B, the more similar those vectors are, and the greater the value of cos theta, the more similarity between these two vectors. As the angle between these two vectors decreases, the cos theta value increases, which is crucial for recommendation engines and collaborative filtering.']}], 'duration': 1792.919, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ820198152.jpg', 'highlights': ['The chapter covers unsupervised learning, recommendation engines, collaborative filtering, and time series analysis.', 'It explains vector algebra basics including addition, subtraction, dot product, and unit vectors.', 'The cosine similarity is crucial for recommendation engines and collaborative filtering.', 'The confusion around data scaling and using Intellipaat code, encountering issues with the Iris dataset.', 'Difficulty in understanding the code and lack of practice with the dataset leads to uncertainty about data scaling.']}, {'end': 23875.151, 'segs': [{'end': 23344.882, 'src': 'embed', 'start': 23314.26, 'weight': 4, 'content': [{'end': 23320.923, 'text': 'So this time I want to recommend this item number one to user number five.', 'start': 23314.26, 'duration': 6.663}, {'end': 23330.351, 'text': 'And if I have to do that, I would have to find out the similarity of this item number one with respect to other items.', 'start': 23321.383, 'duration': 8.968}, {'end': 23337.196, 'text': 'So if I have to calculate the similarity, then what I will do is I will calculate the cosine values.', 'start': 23330.971, 'duration': 6.225}, {'end': 23344.882, 'text': "So I'll calculate the cosine value between item one and item two, between item one and item three, between item one and item four.", 'start': 23337.616, 'duration': 7.266}], 'summary': 'Recommend item 1 to user 5 based on cosine similarity with other items.', 'duration': 30.622, 'max_score': 23314.26, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ823314260.jpg'}, {'end': 23815.084, 'src': 'embed', 'start': 23786.216, 'weight': 0, 'content': [{'end': 23796.08, 'text': 'so minus one point three into the weight which is zero point four, plus minus zero point three into the weight which is zero point nine,', 'start': 23786.216, 'duration': 9.864}, {'end': 23803.224, 'text': 'divided by the sum of the weights, which is zero point four plus zero point nine, which gives us one point three.', 'start': 23796.08, 'duration': 7.144}, {'end': 23806.545, 'text': 'And in total, we get a value of minus zero point six.', 'start': 23803.884, 'duration': 2.661}, {'end': 23815.084, 'text': 'We should compare the values of 10 and 1, right, to do the, I mean, recommend to user 1.', 'start': 23810.323, 'duration': 4.761}], 'summary': 'Weighted calculation yields -0.6, recommend to user 1.', 'duration': 28.868, 'max_score': 23786.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ823786216.jpg'}], 'start': 21993.905, 'title': 'Collaborative filtering in recommendation engines', 'summary': 'Delves into user-based and item-based collaborative filtering in recommendation engines, discussing the process, calculations, and examples, such as using cosine similarity to recommend items and normalizing values to address user biases.', 'chapters': [{'end': 22239.986, 'start': 21993.905, 'title': 'Collaborative filtering in recommendation engines', 'summary': 'Discusses user-based collaborative filtering in recommendation engines, where data is collaborated between similar entities to recommend values to users based on their similar tastes, as seen in the example of user 1 and user 2 watching similar channels and receiving recommendations.', 'duration': 246.081, 'highlights': ['Collaborative filtering involves collaborating data between similar entities and recommending values, as shown in the example of User 1 and User 2 watching similar channels and receiving recommendations.', 'The concept of collaborative filtering is illustrated using the example of recommending comedy sitcoms to User 1 based on the similar taste observed in User 2, thus demonstrating the application of collaborative filtering in recommendation engines.', 'The explanation of collaborative filtering emphasizes the recommendation of values from similar entities to users with similar tastes, as exemplified by recommending channels to User 1 based on the common channels watched by User 2.']}, {'end': 22692.561, 'start': 22239.986, 'title': 'User-based collaborative filtering', 'summary': 'Explains user-based collaborative filtering, involving a matrix with six users and eight items, recommending items to a new user based on cosine similarity, and identifying the most similar users with a cosine value of 0.62.', 'duration': 452.575, 'highlights': ['The chapter explains the concept of user-based collaborative filtering, which involves creating a matrix with users and items and filling in missing values based on the preference of other users, supported by the use of cosine similarity.', 'It illustrates the process of calculating cosine similarity between a new user and existing users, with the most similar user having a cosine value of 0.62, indicating a high level of similarity.', 'The discussion also touches on different types of recommendation engines, such as item-based collaborative filtering and content-based collaborative filtering, each utilizing different functions like Pearson coefficient for recommendations.', 'The speaker also mentions the use of cosine similarity in user-based and item-based collaborative filtering, emphasizing its importance in understanding the similarity between users.']}, {'end': 23059.161, 'start': 22692.902, 'title': 'User-based collaborative filtering', 'summary': 'Explains the concept of user-based collaborative filtering and demonstrates the calculation of cosine values to determine the similarity of users, resulting in the recommendation of items based on ratings.', 'duration': 366.259, 'highlights': ['The chapter explains the concept of user-based collaborative filtering and demonstrates the calculation of cosine values to determine the similarity of users, resulting in the recommendation of items based on ratings. It covers the calculation of cosine values to determine user similarity and the recommendation of items based on ratings.', 'The process involves selecting the most similar users, predicting ratings for new users, and recommending items based on user-based collaborative filtering. The process involves selecting the most similar users, predicting ratings for new users, and recommending items based on user-based collaborative filtering.', 'The ranking of items is determined based on their ratings, and recommendations are made accordingly. The ranking of items is determined based on their ratings, and recommendations are made accordingly.', 'The concept of item-based collaborative filtering is briefly introduced, along with the consideration of user rating tendencies. The concept of item-based collaborative filtering is briefly introduced, along with the consideration of user rating tendencies.']}, {'end': 23390.465, 'start': 23059.861, 'title': 'Item-based collaborative filtering', 'summary': 'Explains the process of item-based collaborative filtering, calculating cosine values between items, and normalizing values to address biased user ratings in order to recommend items based on their similarity, with examples of cosine values and normalization calculations.', 'duration': 330.604, 'highlights': ['The chapter explains the process of item-based collaborative filtering It provides an overview of the main topic of the chapter.', 'calculating cosine values between items Describes the specific method used to determine similarity between items.', 'normalizing values to address biased user ratings Details the approach to handle biased user ratings, ensuring fair comparison.', 'recommend items based on their similarity Emphasizes the ultimate purpose of the collaborative filtering process.', 'with examples of cosine values and normalization calculations Illustrates the concepts with practical examples and calculations.']}, {'end': 23551.694, 'start': 23390.465, 'title': 'Data normalization and similarity calculation', 'summary': 'Discusses the process of normalizing values by subtracting the mean, calculating cosine values between items, and determining the most similar items based on cosine values, with a specific example of item number one being most similar to item number 10 with a cosine value of 0.9.', 'duration': 161.229, 'highlights': ['The process of normalizing values by subtracting the mean is explained. The speaker explains the process of normalizing values by subtracting the mean from the original values.', 'Calculation of cosine values between items and determining the most similar items based on cosine values. The chapter discusses calculating cosine values between items and determining the most similar items based on these values, with a specific example of item number one being most similar to item number 10 with a cosine value of 0.9.', "Explanation of determining the new user's value based on the nearest neighbors and the average calculation. The speaker explains determining the new user's value based on the nearest neighbors, emphasizing the difference between the average and the actual calculation, and asks for clarification on how to obtain a specific value."]}, {'end': 23875.151, 'start': 23554.216, 'title': 'Normalization and weighted average', 'summary': 'Discusses the importance of normalizing data and calculating weighted average in collaborative filtering, emphasizing the need to consider variance and standard deviation for accurate results.', 'duration': 320.935, 'highlights': ['The importance of considering variance and standard deviation for accurate normalization is discussed, highlighting the need for uniformity in data scaling. The speaker emphasizes the need to consider variance and standard deviation for accurate normalization, stressing the importance of uniformity in data scaling.', 'The process of calculating a weighted average in collaborative filtering is explained, demonstrating the use of weighted values to derive the final result. The method of calculating a weighted average in collaborative filtering is explained, showcasing the use of weighted values to compute the final result.', 'The differences between user-based and item-based collaborative filtering are outlined, clarifying that UBCF focuses on users while IBCF centers around items. The distinction between user-based and item-based collaborative filtering is outlined, emphasizing that UBCF revolves around users, whereas IBCF centers around items.']}], 'duration': 1881.246, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ821993905.jpg', 'highlights': ["Collaborative filtering involves recommending values from similar entities, demonstrated by recommending comedy sitcoms to User 1 based on User 2's similar taste.", "User-based collaborative filtering creates a matrix with users and items, filling in missing values based on other users' preferences, using cosine similarity.", 'The chapter explains the process of calculating cosine similarity between users and items, emphasizing the importance of cosine similarity in understanding user similarity.', 'Item-based collaborative filtering involves calculating cosine values between items, normalizing values to address biased user ratings, and recommending items based on their similarity.', 'The process of normalizing values by subtracting the mean is explained, along with calculating cosine values between items and determining the most similar items based on these values.', 'The importance of considering variance and standard deviation for accurate normalization is discussed, along with the process of calculating a weighted average in collaborative filtering.']}, {'end': 25477.568, 'segs': [{'end': 23959.627, 'src': 'embed', 'start': 23931.215, 'weight': 8, 'content': [{'end': 23934.336, 'text': 'Now, let me also have a glance at the data of it.', 'start': 23931.215, 'duration': 3.121}, {'end': 23937.578, 'text': 'So movie lens at the rate data.', 'start': 23934.696, 'duration': 2.882}, {'end': 23942.48, 'text': 'So this is basically the metrics which we saw in the PPTO here.', 'start': 23938.398, 'duration': 4.082}, {'end': 23946.962, 'text': 'So these are all the users and these are this are basically represent all the movies.', 'start': 23942.5, 'duration': 4.462}, {'end': 23949.023, 'text': 'And this is the ratings for those movies.', 'start': 23947.002, 'duration': 2.021}, {'end': 23953.324, 'text': 'Right So user one has rated movie one with five.', 'start': 23949.243, 'duration': 4.081}, {'end': 23956.126, 'text': 'User one has rated movie two with a rating of three.', 'start': 23953.584, 'duration': 2.542}, {'end': 23959.627, 'text': 'User one has rated movie three with a rating of four.', 'start': 23956.626, 'duration': 3.001}], 'summary': 'Data analysis shows user one rated movies with 5, 3, and 4 stars.', 'duration': 28.412, 'max_score': 23931.215, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ823931215.jpg'}, {'end': 24006.704, 'src': 'embed', 'start': 23982.768, 'weight': 5, 'content': [{'end': 23989.712, 'text': 'So I will convert this using the ASDOT vector function and I will store it in vector ratings.', 'start': 23982.768, 'duration': 6.944}, {'end': 23994.196, 'text': 'Now, let me have a look at all of the unique ratings.', 'start': 23990.393, 'duration': 3.803}, {'end': 23999.059, 'text': "So I'll use the unique function and I will pass in this object over here.", 'start': 23994.796, 'duration': 4.263}, {'end': 24004.803, 'text': 'So these are the different ratings which could be provided by the user.', 'start': 24000.08, 'duration': 4.723}, {'end': 24006.704, 'text': 'So zero, one, two, three, four, five.', 'start': 24004.883, 'duration': 1.821}], 'summary': 'Converted using asdot vector function, stored in vector ratings, with unique ratings zero to five.', 'duration': 23.936, 'max_score': 23982.768, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ823982768.jpg'}, {'end': 24181.219, 'src': 'embed', 'start': 24151.509, 'weight': 3, 'content': [{'end': 24153.149, 'text': 'This is not the one.', 'start': 24151.509, 'duration': 1.64}, {'end': 24154.91, 'text': 'Let me see.', 'start': 24154.19, 'duration': 0.72}, {'end': 24156.791, 'text': 'This gives me the same thing.', 'start': 24155.15, 'duration': 1.641}, {'end': 24172.154, 'text': 'Again, What you can maybe take it as is we are not actually removing the missing values over here.', 'start': 24156.811, 'duration': 15.343}, {'end': 24181.219, 'text': 'So there are actually cases where some users have not seen certain movies and we will go ahead and recommend them.', 'start': 24172.214, 'duration': 9.005}], 'summary': 'Users not seeing certain movies, missing values not removed, hence recommending them.', 'duration': 29.71, 'max_score': 24151.509, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ824151509.jpg'}, {'end': 24413.257, 'src': 'embed', 'start': 24376.803, 'weight': 2, 'content': [{'end': 24379.083, 'text': 'They mentioned they explained it to you.', 'start': 24376.803, 'duration': 2.28}, {'end': 24380.944, 'text': 'OK, the same the same example.', 'start': 24379.163, 'duration': 1.781}, {'end': 24383.084, 'text': 'OK, well, then.', 'start': 24381.984, 'duration': 1.1}, {'end': 24399.914, 'text': "Okay, so then in that case, so is the user allowed to enter zero? No, you can't enter zero.", 'start': 24389.471, 'duration': 10.443}, {'end': 24407.316, 'text': 'Okay, so let me actually have a glance at the original data set.', 'start': 24402.915, 'duration': 4.401}, {'end': 24409.536, 'text': 'Movie lens at the rate data.', 'start': 24407.556, 'duration': 1.98}, {'end': 24410.737, 'text': 'This is what we have.', 'start': 24409.596, 'duration': 1.141}, {'end': 24413.257, 'text': 'Class of this.', 'start': 24411.917, 'duration': 1.34}], 'summary': "Discussion about data and user input, with mention of 'movie lens' data set.", 'duration': 36.454, 'max_score': 24376.803, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ824376803.jpg'}, {'end': 24574.283, 'src': 'embed', 'start': 24519.879, 'weight': 0, 'content': [{'end': 24526.841, 'text': 'So as we already know, that columns represent the movies and the rules represent the users.', 'start': 24519.879, 'duration': 6.962}, {'end': 24535.669, 'text': "So now If we get a count of the columns or the column values, then we'll get the number of views of each movie.", 'start': 24527.481, 'duration': 8.188}, {'end': 24539.773, 'text': 'So we have this call counts function, which is a part of the recommender lab package.', 'start': 24536.15, 'duration': 3.623}, {'end': 24545.137, 'text': 'I will pass in this data set and I will store it in views per movie.', 'start': 24540.013, 'duration': 5.124}, {'end': 24550.582, 'text': 'Now let me have a glance at this view of use per movie.', 'start': 24546.018, 'duration': 4.564}, {'end': 24554.185, 'text': 'So see that Toy Story has been seen 452 times.', 'start': 24552.043, 'duration': 2.142}, {'end': 24558.292, 'text': 'GoldenEye has been seen 131 times.', 'start': 24555.95, 'duration': 2.342}, {'end': 24561.374, 'text': 'Similarly, this movie Four Rooms has been seen 90 times.', 'start': 24558.432, 'duration': 2.942}, {'end': 24567.878, 'text': 'So we have got the number of times each movie has been seen over here.', 'start': 24562.074, 'duration': 5.804}, {'end': 24574.283, 'text': "Now, what we'll do is we will go ahead and create a data frame out of this.", 'start': 24567.898, 'duration': 6.385}], 'summary': 'Using recommender lab package to count movie views: toy story-452, goldeneye-131, four rooms-90.', 'duration': 54.404, 'max_score': 24519.879, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ824519879.jpg'}, {'end': 24661.115, 'src': 'embed', 'start': 24636.815, 'weight': 10, 'content': [{'end': 24644.321, 'text': 'like we have heard, the movie lens said the data is storing in ratings how the movies are, how the data is doing exactly.', 'start': 24636.815, 'duration': 7.506}, {'end': 24653.188, 'text': "i didn't get even after learning i mean i have done ubc ibcf, but i had a doubt always this one like how the data is coming before.", 'start': 24644.321, 'duration': 8.867}, {'end': 24655.09, 'text': 'we saw movie lens and red data.', 'start': 24653.188, 'duration': 1.902}, {'end': 24657.051, 'text': 'there we have seen all the ratings.', 'start': 24655.09, 'duration': 1.961}, {'end': 24659.293, 'text': 'now the same thing call counts itself.', 'start': 24657.051, 'duration': 2.242}, {'end': 24661.115, 'text': 'here we have seen the movie names.', 'start': 24659.293, 'duration': 1.822}], 'summary': 'Movie lens stores movie ratings and names in data. it includes counts for movie names.', 'duration': 24.3, 'max_score': 24636.815, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ824636815.jpg'}], 'start': 23875.912, 'title': 'Movielens data analysis', 'summary': 'Introduces working with the movielens data set, which consists of 99,392 ratings, 943 users, and 1,664 movies, covers the process of converting the matrix into a vector, analyzing unique ratings, and removing zero ratings. it also discusses the analysis of movie ratings and views, including the removal of zero values, visualization of rating instances, and obtaining the number of views per movie. additionally, it explains how the movie lens data is stored as a matrix, with insights into the real rating metrics and movie storage.', 'chapters': [{'end': 24271.263, 'start': 23875.912, 'title': 'Working with movielens data set', 'summary': 'Introduces working with the movielens data set, which consists of a real rating matrix with 99,392 ratings, 943 users, and 1,664 movies, and covers the process of converting the matrix into a vector, analyzing unique ratings, and removing zero ratings to prepare for recommendation.', 'duration': 395.351, 'highlights': ['The MovieLens data set comprises 99,392 ratings, 943 users, and 1,664 movies, forming a real rating matrix.', 'The process involves converting the rating matrix into a vector using the ASDOT vector function for better data analysis.', 'The unique ratings provided by users include values from zero to five, with zero signifying no rating given.', 'The frequency of ratings reveals instances such as 6,059 ratings of 1, 27,002 ratings of 3, and 21,077 ratings of 5.', 'The removal of zero ratings from the vector prepares the data for recommendation, considering cases where users have not rated certain movies.']}, {'end': 24631.256, 'start': 24271.983, 'title': 'Data analysis: movie ratings and views', 'summary': 'Discusses the process of analyzing movie ratings and views, including the removal of zero values, visualization of rating instances, and obtaining the number of views per movie using the recommender lab package, with insights such as over 30,000 instances of a rating of four and toy story being viewed 452 times.', 'duration': 359.273, 'highlights': ['Over 30,000 instances of a rating of four. There are around 30,000 instances where people have given a rating of four, indicating a significant number of positive ratings.', 'Toy Story viewed 452 times. The movie Toy Story has been viewed 452 times, highlighting its popularity among the dataset users.', 'Insights from the recommender lab package. The chapter utilizes the recommender lab package to obtain insights such as the number of views per movie, demonstrating the use of specialized tools for data analysis.']}, {'end': 24981.417, 'start': 24636.815, 'title': 'Understanding movie lens data storage', 'summary': 'Explains how the movie lens data is stored as a matrix, with ratings and movie names, and how the call counts function works to determine the frequency of movie views based on the data values, providing insights into the real rating metrics and movie storage.', 'duration': 344.602, 'highlights': ['The Movie Lens data is stored as a matrix containing rating values and corresponding movie names and user numbers, providing a comprehensive structure for analyzing movie ratings and metadata.', 'The call counts function operates on the data values within the matrix, allowing the determination of the frequency of movie views, which aids in understanding user preferences and movie popularity.', 'The metadata of the Movie Lens includes movie names, release year, URL, and genre information, contributing to a comprehensive understanding of the movie data and its attributes.', 'The representation of movie names and corresponding ratings in the Movie Lens data provides insights into user preferences and movie popularity, aiding in the analysis and understanding of the real rating metrics.', 'The genres associated with each movie in the Movie Lens data provide valuable information on the categorization of movies, allowing for genre-based analysis and insights into movie attributes.']}, {'end': 25477.568, 'start': 24982.178, 'title': 'Understanding movie rating metrics', 'summary': 'Covers the process of creating and analyzing movie rating metrics, including the metadata and data, the algorithm behind the matrix creation, and visualizing the top five most viewed movies.', 'duration': 495.39, 'highlights': ['The process of creating and relating movie rating metrics is discussed, including the metadata and data, algorithm behind matrix creation, and its relevance to real-world applications. Discussion on metadata and data, algorithm behind matrix creation, and relevance to real-world applications.', 'The visualization of the top five most viewed movies, including arranging in descending and ascending order of views, and mapping colors based on views. Visualization of top five most viewed movies, arranging in descending and ascending order of views, and color mapping based on views.']}], 'duration': 1601.656, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ823875912.jpg', 'highlights': ['The MovieLens data set comprises 99,392 ratings, 943 users, and 1,664 movies, forming a real rating matrix.', 'The process involves converting the rating matrix into a vector using the ASDOT vector function for better data analysis.', 'The unique ratings provided by users include values from zero to five, with zero signifying no rating given.', 'The removal of zero ratings from the vector prepares the data for recommendation, considering cases where users have not rated certain movies.', 'Over 30,000 instances of a rating of four, indicating a significant number of positive ratings.', 'Toy Story viewed 452 times, highlighting its popularity among the dataset users.', 'The Movie Lens data is stored as a matrix containing rating values and corresponding movie names and user numbers, providing a comprehensive structure for analyzing movie ratings and metadata.', 'The call counts function operates on the data values within the matrix, allowing the determination of the frequency of movie views, which aids in understanding user preferences and movie popularity.', 'The metadata of the Movie Lens includes movie names, release year, URL, and genre information, contributing to a comprehensive understanding of the movie data and its attributes.', 'The process of creating and relating movie rating metrics is discussed, including the metadata and data, algorithm behind matrix creation, and its relevance to real-world applications.', 'The visualization of the top five most viewed movies, including arranging in descending and ascending order of views, and mapping colors based on views.']}, {'end': 26888.928, 'segs': [{'end': 25788.746, 'src': 'embed', 'start': 25761.972, 'weight': 0, 'content': [{'end': 25770.176, 'text': 'wherever the split movie tag is false, I will extract all of those values and store them in rec test.', 'start': 25761.972, 'duration': 8.204}, {'end': 25774.058, 'text': 'So now we have our training and testing sets ready.', 'start': 25770.756, 'duration': 3.302}, {'end': 25778.68, 'text': 'So now I will go ahead and build the model on top of the training set.', 'start': 25774.658, 'duration': 4.022}, {'end': 25781.985, 'text': 'And this time we are building the UBFC model.', 'start': 25779.304, 'duration': 2.681}, {'end': 25788.746, 'text': 'So all that math which we saw, all of this will be taken care by this recommender function itself.', 'start': 25782.445, 'duration': 6.301}], 'summary': 'Data values extracted for rec test. ubfc model built for recommender function.', 'duration': 26.774, 'max_score': 25761.972, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ825761972.jpg'}, {'end': 25848.585, 'src': 'embed', 'start': 25819.218, 'weight': 3, 'content': [{'end': 25828.3, 'text': 'so this stands for the number of um you know movies i want to recommend to each user.', 'start': 25819.218, 'duration': 9.082}, {'end': 25832.701, 'text': 'so we have built the ubfc model.', 'start': 25828.3, 'duration': 4.401}, {'end': 25835.682, 'text': "now it's time to predict the values.", 'start': 25832.701, 'duration': 2.981}, {'end': 25838.943, 'text': 'so this predict function takes in these parameters.', 'start': 25835.682, 'duration': 3.261}, {'end': 25842.022, 'text': "first is the UBFC model which you've just built.", 'start': 25838.943, 'duration': 3.079}, {'end': 25843.943, 'text': 'Next is the data set.', 'start': 25842.682, 'duration': 1.261}, {'end': 25848.585, 'text': 'So we are predicting the values or recommending the values on top of the test set.', 'start': 25844.123, 'duration': 4.462}], 'summary': 'Ubfc model predicts movie recommendations for each user.', 'duration': 29.367, 'max_score': 25819.218, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ825819218.jpg'}, {'end': 26130.919, 'src': 'embed', 'start': 26103.325, 'weight': 5, 'content': [{'end': 26109.027, 'text': 'So this is how we can implement user-based collaborative filtering and item-based collaborative filtering in R.', 'start': 26103.325, 'duration': 5.702}, {'end': 26115.015, 'text': 'So this is the practical part of the collaborative filtering.', 'start': 26110.854, 'duration': 4.161}, {'end': 26118.276, 'text': 'So Bernie, I have a question.', 'start': 26116.135, 'duration': 2.141}, {'end': 26124.117, 'text': 'When we do the actual final recommendation to that particular user?', 'start': 26118.856, 'duration': 5.261}, {'end': 26129.379, 'text': 'do we combine these two again, user-based and item-based, and then give a recommendation?', 'start': 26124.117, 'duration': 5.262}, {'end': 26130.919, 'text': 'How do we recommend??', 'start': 26130.219, 'duration': 0.7}], 'summary': 'Implement user-based and item-based collaborative filtering in r for practical collaborative filtering and combine for final recommendation.', 'duration': 27.594, 'max_score': 26103.325, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ826103325.jpg'}, {'end': 26379.931, 'src': 'embed', 'start': 26353.441, 'weight': 2, 'content': [{'end': 26357.022, 'text': 'Yes So all of this is basically the hyper parameter tuning.', 'start': 26353.441, 'duration': 3.581}, {'end': 26361.043, 'text': 'Right So you can go ahead and again.', 'start': 26357.842, 'duration': 3.201}, {'end': 26363.344, 'text': 'So your data set will also change.', 'start': 26361.083, 'duration': 2.261}, {'end': 26369.686, 'text': 'And as your data set changes, you can also go ahead and modify your hyper parameters.', 'start': 26364.004, 'duration': 5.682}, {'end': 26372.727, 'text': 'So all of that is, again, trial and error.', 'start': 26370.386, 'duration': 2.341}, {'end': 26376.088, 'text': "So what do you mean of hyper parameter? Sorry, I don't know this.", 'start': 26373.487, 'duration': 2.601}, {'end': 26379.931, 'text': 'Hyperparameter is just the values which we can do know here.', 'start': 26376.949, 'duration': 2.982}], 'summary': 'Hyperparameter tuning involves modifying values based on changing datasets, utilizing trial and error.', 'duration': 26.49, 'max_score': 26353.441, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ826353441.jpg'}, {'end': 26674.972, 'src': 'embed', 'start': 26645.281, 'weight': 6, 'content': [{'end': 26654.947, 'text': 'we have taken three users and comparing I mean two and we give I mean creating the value for that for the user, one based on UBCF or ABCF.', 'start': 26645.281, 'duration': 9.666}, {'end': 26656.808, 'text': 'But exactly how.', 'start': 26655.547, 'duration': 1.261}, {'end': 26659.289, 'text': 'now we have 900 users in the UBCF model.', 'start': 26656.808, 'duration': 2.481}, {'end': 26660.67, 'text': 'how would you state the values?', 'start': 26659.289, 'duration': 1.381}, {'end': 26662.631, 'text': 'if you can elaborate that, really good?', 'start': 26660.67, 'duration': 1.961}, {'end': 26670.93, 'text': 'okay, so in this case we are not actually choosing the k nearest neighbors.', 'start': 26663.948, 'duration': 6.982}, {'end': 26674.972, 'text': 'so we have taken this rating movies data set.', 'start': 26670.93, 'duration': 4.042}], 'summary': 'Comparing ubcf and abcf for 900 users in rating movies dataset.', 'duration': 29.691, 'max_score': 26645.281, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ826645281.jpg'}, {'end': 26766.581, 'src': 'embed', 'start': 26740.735, 'weight': 1, 'content': [{'end': 26748.277, 'text': "So do you guys want to break now before we start off with association rule mining? Or should we just continue? No, I'm going to do it.", 'start': 26740.735, 'duration': 7.542}, {'end': 26751.758, 'text': "I mean, I'm running in the community.", 'start': 26748.337, 'duration': 3.421}, {'end': 26753.178, 'text': 'We have content based.', 'start': 26751.998, 'duration': 1.18}, {'end': 26756.198, 'text': 'Are you covering that part? I know.', 'start': 26753.538, 'duration': 2.66}, {'end': 26759.639, 'text': 'So only collaborative filtering is a part of the course.', 'start': 26756.279, 'duration': 3.36}, {'end': 26762.24, 'text': 'Content based is not part of the course.', 'start': 26759.919, 'duration': 2.321}, {'end': 26766.581, 'text': 'Even in the same page, the content is not content based.', 'start': 26764.2, 'duration': 2.381}], 'summary': 'Discussion on association rule mining and course content. only collaborative filtering is included.', 'duration': 25.846, 'max_score': 26740.735, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ826740735.jpg'}], 'start': 25478.348, 'title': 'Movie analysis and recommendation models', 'summary': 'Covers movie ratings distribution analysis using mean and qplot functions, collaborative filtering in r for movie recommendations, unsupervised learning, model tuning, and ubcf model with content-based filtering for movie recommendations.', 'chapters': [{'end': 25530.263, 'start': 25478.348, 'title': 'Movie ratings distribution analysis', 'summary': 'Demonstrates the use of call means function to calculate the average rating of movies, and then utilizes qplot function to visualize the distribution, revealing that most ratings fall around three or four with very few instances of a rating of five.', 'duration': 51.915, 'highlights': ['The chapter showcases the use of call means function to calculate the average rating of movies from the movie lens dataset.', 'The QPlot function is utilized to create a visualization showing the distribution of average ratings, indicating that most ratings are around three or four with very few instances of a rating of five.']}, {'end': 26237.236, 'start': 25533.584, 'title': 'Collaborative filtering in r', 'summary': "Discusses the process of extracting and dividing data, building user-based and item-based collaborative filtering models, and the recommended movies for users, with a focus on the method's application and practical implications.", 'duration': 703.652, 'highlights': ['The process of extracting a subset of the entire data set based on specific conditions, such as users who have seen at least 50 movies and movies seen at least 100 times, resulted in 118 users being recommended 6 movies each. Subset extraction based on specific user and movie conditions led to 118 users being recommended 6 movies each.', 'The chapter demonstrates the implementation of both user-based and item-based collaborative filtering models, showcasing the distinct movie recommendations generated for users using each method. Demonstration of user-based and item-based collaborative filtering models and their unique movie recommendations.', 'The discussion on practical implications highlights the importance of domain knowledge in deciding between user-based and item-based collaborative filtering, emphasizing the consideration of circumstances and user/item data availability. Emphasis on domain knowledge in selecting between user-based and item-based collaborative filtering based on circumstances and data availability.']}, {'end': 26617.989, 'start': 26237.236, 'title': 'Unsupervised learning and model tuning', 'summary': 'Discusses the concept of unsupervised learning, model tuning with increasing data, and the use of hyperparameters in recommendation systems, highlighting the need to adjust models as data grows and the importance of hyperparameter tuning in modifying the model.', 'duration': 380.753, 'highlights': ['The process of unsupervised learning and grouping people or items based on their similarity is explained, with the need to adjust the model as data increases.', 'The discussion on the impact of increasing data on model tuning and the need to modify hyperparameters as the dataset changes, emphasizing the trial and error nature of hyperparameter tuning.', 'Explanation of hyperparameters and their role in controlling the model, with examples of modifying recommendations based on hyperparameters.', 'The concept of finding similar users in UBCF model and comparing their similarities, along with the use of k-nearest neighbors and selecting the number of similar users to consider for predictions.']}, {'end': 26888.928, 'start': 26620.191, 'title': 'Ubcf model and content-based filtering', 'summary': 'Discusses the ubcf model, which calculates cosine values for 559 users and recommends movies based on their mean ratings, and explains content-based filtering, which clusters users based on content similarity for movie recommendations.', 'duration': 268.737, 'highlights': ['The UBCF model calculates cosine values for 559 users and recommends movies based on their mean ratings. The model calculates cosine values for 559 users and recommends movies based on their mean ratings, not choosing the nearest neighbors.', 'Content-based filtering clusters users based on content similarity for movie recommendations. Content-based filtering clusters users based on content similarity for movie recommendations, such as clustering users who watch only romantic movies.', 'Netflix uses both collaborative and content-based filtering as part of its hybrid filtering approach. Netflix uses both collaborative and content-based filtering as part of its hybrid filtering approach, which is not covered in the course.']}], 'duration': 1410.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ825478348.jpg', 'highlights': ['The QPlot function visualizes the distribution of average ratings, indicating most ratings are around three or four.', 'The process of extracting a subset of the entire data set resulted in 118 users being recommended 6 movies each.', 'The chapter demonstrates the implementation of both user-based and item-based collaborative filtering models.', 'The discussion emphasizes the consideration of circumstances and user/item data availability in selecting between user-based and item-based collaborative filtering.', 'The process of unsupervised learning and grouping people or items based on their similarity is explained.', 'The discussion highlights the impact of increasing data on model tuning and the need to modify hyperparameters.', 'The concept of finding similar users in UBCF model and comparing their similarities is explained.', 'The UBCF model calculates cosine values for 559 users and recommends movies based on their mean ratings.', 'Content-based filtering clusters users based on content similarity for movie recommendations.', 'Netflix uses both collaborative and content-based filtering as part of its hybrid filtering approach.']}, {'end': 28377.561, 'segs': [{'end': 27188.375, 'src': 'embed', 'start': 27151.23, 'weight': 3, 'content': [{'end': 27154.312, 'text': 'There were eight hundred orders which had A, B and C in it.', 'start': 27151.23, 'duration': 3.082}, {'end': 27158.155, 'text': 'And there were five thousand orders which had C in it.', 'start': 27154.853, 'duration': 3.302}, {'end': 27165.341, 'text': 'Now we were about to learn what is support, what is confidence and what is left.', 'start': 27159.336, 'duration': 6.005}, {'end': 27167.002, 'text': 'So support.', 'start': 27166.281, 'duration': 0.721}, {'end': 27177.107, 'text': 'basically signifies the importance of this rule in the overall scheme of things, or in other words,', 'start': 27167.6, 'duration': 9.507}, {'end': 27188.375, 'text': 'it gives us the proportion of the total orders in which the antecedent and the consequent is present.', 'start': 27177.107, 'duration': 11.268}], 'summary': '800 orders had a, b, and c; 5000 orders had c. learning about support, confidence, and left.', 'duration': 37.145, 'max_score': 27151.23, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ827151230.jpg'}, {'end': 27608.107, 'src': 'embed', 'start': 27570.238, 'weight': 12, 'content': [{'end': 27572.919, 'text': 'so there are 9835 orders in total, and 165 signifies the number of items.', 'start': 27570.238, 'duration': 2.681}, {'end': 27591.28, 'text': 'so there are 169 items and 9835 transactions or orders in total, And this tells us the most frequent items.', 'start': 27572.919, 'duration': 18.361}, {'end': 27600.884, 'text': 'So in that data set, whole milk is the most frequent item, which has been bought 2,513 times.', 'start': 27591.86, 'duration': 9.024}, {'end': 27608.107, 'text': 'Similarly, other vegetables is the next most bought item, which was present in 1903 transactions.', 'start': 27601.324, 'duration': 6.783}], 'summary': '9835 orders, 169 items; most frequent: whole milk (2,513) and other vegetables (1,903)', 'duration': 37.869, 'max_score': 27570.238, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ827570238.jpg'}, {'end': 27725.391, 'src': 'embed', 'start': 27662.39, 'weight': 1, 'content': [{'end': 27673.853, 'text': 'So this confidence of 0.5 states that out of all of the antecedents, which we have, 50% of them should also have the consequent present in them.', 'start': 27662.39, 'duration': 11.463}, {'end': 27679.054, 'text': 'And I will go ahead and store this in rule one.', 'start': 27674.292, 'duration': 4.762}, {'end': 27683.896, 'text': 'Now, let me go ahead and inspect the first six rules.', 'start': 27679.614, 'duration': 4.282}, {'end': 27690.718, 'text': 'And to do that, I will have the inspect function and I will pass in this rule one object inside this.', 'start': 27684.276, 'duration': 6.442}, {'end': 27692.219, 'text': 'Let me zoom this.', 'start': 27691.539, 'duration': 0.68}, {'end': 27696.12, 'text': 'So this is the antecedent and this is the consequent over here.', 'start': 27692.779, 'duration': 3.341}, {'end': 27703.123, 'text': 'So this first rule states that if someone buys cereals, he would also buy whole milk.', 'start': 27696.741, 'duration': 6.382}, {'end': 27711.408, 'text': 'And the support for this is 0.003 or in other words, it is 0.3%.', 'start': 27703.726, 'duration': 7.682}, {'end': 27720.87, 'text': 'So what we are basically trying to tell us out of all of the orders, this rule is present 0.3% of the times,', 'start': 27711.408, 'duration': 9.462}, {'end': 27725.391, 'text': 'or this transaction is present 0.3% of the times.', 'start': 27720.87, 'duration': 4.521}], 'summary': 'Confidence of 0.5 indicates 50% antecedent-consequent association. first rule: cereals purchase associated with whole milk with 0.3% support.', 'duration': 63.001, 'max_score': 27662.39, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ827662390.jpg'}, {'end': 28013.767, 'src': 'embed', 'start': 27950.472, 'weight': 5, 'content': [{'end': 27956.556, 'text': 'Right So we were supposed to plot this plot of rule one.', 'start': 27950.472, 'duration': 6.084}, {'end': 27959.698, 'text': 'Let me zoom this now.', 'start': 27958.677, 'duration': 1.021}, {'end': 27964.703, 'text': 'So on the Y axis, we have the confidence on the X axis.', 'start': 27961.621, 'duration': 3.082}, {'end': 27971.026, 'text': 'We have the support and this range of red color, which you see, it basically stands for the left.', 'start': 27964.743, 'duration': 6.283}, {'end': 27975.209, 'text': 'So darker the red value, higher is the left.', 'start': 27971.627, 'duration': 3.582}, {'end': 27984.634, 'text': 'And the inference which we can draw from this is the higher left rules are present at lower support thresholds.', 'start': 27975.749, 'duration': 8.885}, {'end': 27994.957, 'text': 'So basically this higher left rules are not that significant in the entire scheme of things right.', 'start': 27985.275, 'duration': 9.682}, {'end': 27998.519, 'text': 'so this is what we can infer from this plot over here.', 'start': 27994.957, 'duration': 3.562}, {'end': 28005.743, 'text': 'so these dark red dots which you see, even though the lift is high, but there are very,', 'start': 27998.519, 'duration': 7.224}, {'end': 28011.706, 'text': 'very few rules which are present in the overall scheme of things right.', 'start': 28005.743, 'duration': 5.963}, {'end': 28013.767, 'text': 'so this is what this plot tells us.', 'start': 28011.706, 'duration': 2.061}], 'summary': 'The plot shows that higher lift rules have lower support, indicating their insignificance.', 'duration': 63.295, 'max_score': 27950.472, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ827950472.jpg'}, {'end': 28192.777, 'src': 'embed', 'start': 28165.024, 'weight': 0, 'content': [{'end': 28169.747, 'text': 'And this is because the empty set is also taken as one item.', 'start': 28165.024, 'duration': 4.723}, {'end': 28177.064, 'text': 'So empty set is basically those instances where the person has basically not bought anything.', 'start': 28170.919, 'duration': 6.145}, {'end': 28180.647, 'text': 'And that instance is also taken as one item.', 'start': 28177.604, 'duration': 3.043}, {'end': 28184.71, 'text': "Right So for this case, let's take this.", 'start': 28181.468, 'duration': 3.242}, {'end': 28192.777, 'text': 'If someone buys tropical fruit, other vegetables, butter and yogurt, then the probability of that person also buying whole milk.', 'start': 28185.11, 'duration': 7.667}], 'summary': 'Empty set considered as one item, probability of buying whole milk given specific purchases.', 'duration': 27.753, 'max_score': 28165.024, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ828165024.jpg'}, {'end': 28244.346, 'src': 'embed', 'start': 28216.456, 'weight': 2, 'content': [{'end': 28222.497, 'text': 'then there is 76% likelihood that he would also buy whole milk along with it.', 'start': 28216.456, 'duration': 6.041}, {'end': 28226.578, 'text': 'Now again, let me go ahead and make a plot of this.', 'start': 28223.758, 'duration': 2.82}, {'end': 28228.899, 'text': "It's a plot of rule two.", 'start': 28226.598, 'duration': 2.301}, {'end': 28230.737, 'text': 'Let me zoom this.', 'start': 28229.696, 'duration': 1.041}, {'end': 28232.178, 'text': 'So this is what we have.', 'start': 28231.297, 'duration': 0.881}, {'end': 28237.301, 'text': 'So this is these are all the items in LHS and these are all the items in RHS.', 'start': 28232.638, 'duration': 4.663}, {'end': 28242.585, 'text': 'So you see that these two bubbles, the support is also high and the left is also high.', 'start': 28237.401, 'duration': 5.184}, {'end': 28244.346, 'text': "So let's take this rule.", 'start': 28243.365, 'duration': 0.981}], 'summary': '76% likelihood of buying whole milk with another item.', 'duration': 27.89, 'max_score': 28216.456, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ828216456.jpg'}, {'end': 28323.407, 'src': 'embed', 'start': 28287.576, 'weight': 11, 'content': [{'end': 28289.597, 'text': 'So again, these are all of the hyper parameters.', 'start': 28287.576, 'duration': 2.021}, {'end': 28291.117, 'text': 'So you can go ahead and play with this.', 'start': 28289.657, 'duration': 1.46}, {'end': 28297.339, 'text': 'And, as I told you guys, this is called a priority because before we go ahead and build the model,', 'start': 28291.617, 'duration': 5.722}, {'end': 28304.622, 'text': 'we tell the model what should be the minimum threshold of support and what should be the minimum threshold of confidence.', 'start': 28297.339, 'duration': 7.283}, {'end': 28312.881, 'text': 'And you have to give me only those rules which are above the minimum support and the minimum threshold.', 'start': 28305.122, 'duration': 7.759}, {'end': 28315.223, 'text': "Again, I'll hit on enter.", 'start': 28314.102, 'duration': 1.121}, {'end': 28323.407, 'text': 'Now let me inspect the top four rules with respect to this support and confidence value.', 'start': 28315.803, 'duration': 7.604}], 'summary': 'Explaining hyperparameters and setting thresholds for support and confidence in data mining.', 'duration': 35.831, 'max_score': 28287.576, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ828287576.jpg'}], 'start': 26889.528, 'title': 'Association rule mining and analysis', 'summary': 'Covers association rule mining, concepts of support, confidence, and lift, and their application in analyzing groceries data, emphasizing the significance of these measures. it also discusses the analysis of association rules and the trade-off between support and lift for identifying meaningful associations. notable findings include a 76% likelihood of buying whole milk when purchasing specific items together.', 'chapters': [{'end': 27088.036, 'start': 26889.528, 'title': 'Association rule mining', 'summary': 'Covers association rule mining, which involves finding patterns and correlations in data using if-then clauses, with a focus on unsupervised learning and three basic measures: support, confidence, and lift.', 'duration': 198.508, 'highlights': ['Association rule mining involves finding patterns and correlations in data using if-then clauses. It aims to discover correlations and patterns in data by using if-then clauses, such as predicting the likelihood of buying a bread packet when purchasing milk.', "The chapter introduces the concept of antecedent and consequent in association rule mining. The antecedent represents the 'if' part, for example, buying milk, while the consequent represents the 'then' part, such as buying a bread packet, in the context of predicting future events based on current ones.", 'Three basic measures in association rule mining: support, confidence, and lift. These measures are crucial for evaluating the strength of associations between items in the data, providing insights into the frequency of co-occurrence and the reliability of the associations.']}, {'end': 27608.107, 'start': 27088.616, 'title': 'Association rule mining concepts', 'summary': 'Explains the concepts of support, confidence, and lift using a scenario where 100,000 orders are analyzed, showcasing the calculation and significance of each metric, and concludes by introducing the a rules package in r.', 'duration': 519.491, 'highlights': ['Out of 100,000 orders, 800 had both A, B, and C, indicating a significant association between these items. 800 orders out of 100,000 had both A, B, and C, demonstrating a strong association between these items.', 'The lift value of 8 indicates that if A and B are present, there is eight times the likelihood that C is also present, emphasizing a strong association between the antecedent and the consequent. The lift value of 8 signifies that if A and B are present, there is eight times the likelihood that C is also present, highlighting a strong association between the antecedent and the consequent.', "The groceries dataset in R comprises 9835 orders and 169 items, with 'whole milk' being the most frequent item bought 2,513 times. The groceries dataset in R consists of 9835 orders and 169 items, with 'whole milk' being the most frequently purchased item, occurring 2,513 times."]}, {'end': 27948.251, 'start': 27609.888, 'title': 'Association rules in groceries data', 'summary': "Discusses using the a priori algorithm to generate association rules from groceries dataset with a support value of 0.002 and confidence value of 0.5, resulting in rules like 'cereals' and 'whole milk' with a support of 0.003 and confidence of 0.64, and 'baking powder' and 'whole milk' with a support of 0.009 and confidence of 0.52, and emphasizes the importance of the 'left' value for association.", 'duration': 338.363, 'highlights': ['The support value of 0.002 states that out of all of the transactions, the antecedent and consequent should be present in 0.2% of all of the orders. Quantifiable data: Support value of 0.002', 'The confidence of 0.5 states that out of all of the antecedents, 50% of them should also have the consequent present in them. Quantifiable data: Confidence value of 0.5', "The rule 'cereals' and 'whole milk' has a support of 0.003 and confidence of 0.64. Quantifiable data: Support of 0.003, Confidence of 0.64", "The rule 'baking powder' and 'whole milk' has a support of 0.009 and confidence of 0.52. Quantifiable data: Support of 0.009, Confidence of 0.52", "Emphasis on the importance of the 'left' value for association rules, such as 'butter' and 'hard cheese' with a left value of 7, indicating the likelihood of buying 'whipped or sour cream'. Quantifiable data: 'Left' value of 7"]}, {'end': 28110.292, 'start': 27950.472, 'title': 'Association rules analysis', 'summary': 'Discusses the analysis of association rules using a plot with confidence on the y axis and support on the x axis, highlighting the significance of higher lift rules at lower support thresholds and the trade-off between support and lift in identifying meaningful associations.', 'duration': 159.82, 'highlights': ['The plot demonstrates that higher lift rules are present at lower support thresholds, indicating their lower significance in the overall scheme of things.', 'The method set to group shows the trade-off between support and lift in identifying meaningful associations between items, emphasizing the need for a right balance between the two.', 'An example is given where the support is high enough but the lift is not, indicating a lack of regular purchase of a specific item, highlighting the importance of finding the right trade-off between support and lift.']}, {'end': 28377.561, 'start': 28112.418, 'title': 'Association rule mining', 'summary': 'Explores association rule mining using the apriori algorithm to discover rules with a minimum antecedent length of five, revealing insights on support, confidence, and lift values for various item combinations, with notable findings including a 76% likelihood of buying whole milk when purchasing specific items together.', 'duration': 265.143, 'highlights': ['The chapter explores association rule mining using the Apriori algorithm to discover rules with a minimum antecedent length of five. The speaker discusses using the Apriori algorithm to find association rules with a minimum antecedent length of five, indicating a focus on discovering specific item combinations with a substantial number of antecedents.', 'Notable findings include a 76% likelihood of buying whole milk when purchasing specific items together. The discussion highlights a 76% likelihood of a person buying whole milk when purchasing specific items together, indicating a strong association between the mentioned items and whole milk purchase.', 'Insights on support, confidence, and lift values for various item combinations are revealed. The chapter provides insights into the support, confidence, and lift values for various item combinations, offering quantitative data to assess the significance and probability of certain item sets being purchased together.']}], 'duration': 1488.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ826889528.jpg', 'highlights': ['Association rule mining uses if-then clauses to find patterns and correlations in data.', "Antecedent and consequent represent the 'if' and 'then' parts in association rule mining.", 'Support, confidence, and lift are crucial measures for evaluating associations between items.', '800 orders out of 100,000 had both A, B, and C, demonstrating a strong association.', 'The lift value of 8 signifies a strong association between the antecedent and the consequent.', "The groceries dataset in R consists of 9835 orders and 169 items, with 'whole milk' being the most frequently purchased item.", 'Support value of 0.002 indicates the presence of antecedent and consequent in 0.2% of all orders.', 'Confidence of 0.5 states that 50% of antecedents also have the consequent present.', "The rule 'cereals' and 'whole milk' has a support of 0.003 and confidence of 0.64.", "The rule 'baking powder' and 'whole milk' has a support of 0.009 and confidence of 0.52.", "The 'lift' value of 7 indicates the likelihood of buying 'whipped or sour cream'.", 'Higher lift rules are present at lower support thresholds, indicating lower significance.', 'The method set to group shows the trade-off between support and lift in identifying meaningful associations.', 'The chapter explores association rule mining using the Apriori algorithm to discover rules with a minimum antecedent length of five.', 'A 76% likelihood of buying whole milk when purchasing specific items together is highlighted.', 'Insights on support, confidence, and lift values for various item combinations are revealed.']}, {'end': 29487.209, 'segs': [{'end': 28537.862, 'src': 'embed', 'start': 28506.573, 'weight': 4, 'content': [{'end': 28511.417, 'text': 'And after that, there would be one final project class which would be taken by Abhishek.', 'start': 28506.573, 'duration': 4.844}, {'end': 28512.358, 'text': "That is what I've been told.", 'start': 28511.457, 'duration': 0.901}, {'end': 28515.781, 'text': 'And the topics which you guys have listed to me.', 'start': 28512.378, 'duration': 3.403}, {'end': 28522.907, 'text': "I'll speak with the operations team and I'll see if there is a possibility that I could cover those topics.", 'start': 28515.781, 'duration': 7.126}, {'end': 28531.113, 'text': 'All right.', 'start': 28530.833, 'duration': 0.28}, {'end': 28535.517, 'text': 'I asked a question regarding unsupervised learning last time we discussed.', 'start': 28531.393, 'duration': 4.124}, {'end': 28537.862, 'text': 'Sure, go ahead.', 'start': 28537.201, 'duration': 0.661}], 'summary': 'One final project class by abhishek, discussing topics and possibility of covering them.', 'duration': 31.289, 'max_score': 28506.573, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ828506573.jpg'}, {'end': 29181.777, 'src': 'embed', 'start': 29130.575, 'weight': 2, 'content': [{'end': 29136.638, 'text': 'Over here, assuming that these columns are present in different units, we will go ahead and normalize these values.', 'start': 29130.575, 'duration': 6.063}, {'end': 29150.664, 'text': 'But then again, the purpose of PCA is to represent this data with the minimum amount of dimensions possible.', 'start': 29137.298, 'duration': 13.366}, {'end': 29154.562, 'text': "So we'll go ahead and normalize this data set.", 'start': 29151.98, 'duration': 2.582}, {'end': 29166.929, 'text': 'Now, if I want to build a PCA on this data set, my aim should be to represent or understand this data set with as less dimensions as possible.', 'start': 29155.182, 'duration': 11.747}, {'end': 29173.432, 'text': 'And those dimensions should give me the maximum variance which is present in this data.', 'start': 29167.389, 'duration': 6.043}, {'end': 29181.777, 'text': 'Now, again, when I say variance, it basically talks about the different distribution which is present in the data.', 'start': 29174.733, 'duration': 7.044}], 'summary': 'Normalize and represent data with minimum dimensions for pca.', 'duration': 51.202, 'max_score': 29130.575, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ829130575.jpg'}, {'end': 29293.54, 'src': 'embed', 'start': 29240.751, 'weight': 0, 'content': [{'end': 29246.395, 'text': 'Yeah, but actually we get the distribution and see how much is that load and see what you see variance.', 'start': 29240.751, 'duration': 5.644}, {'end': 29248.276, 'text': 'So we see one.', 'start': 29246.995, 'duration': 1.281}, {'end': 29251.458, 'text': "Yeah So that's I got it right.", 'start': 29248.296, 'duration': 3.162}, {'end': 29255.261, 'text': 'So normalization is basically the first step.', 'start': 29252.479, 'duration': 2.782}, {'end': 29260.605, 'text': 'And after applying the first step, you are applying an unsupervised learning algorithm.', 'start': 29256.142, 'duration': 4.463}, {'end': 29263.387, 'text': "So if it's key means your purpose is different.", 'start': 29261.065, 'duration': 2.322}, {'end': 29268.27, 'text': 'You are basically trying to cluster the data set into different groups.', 'start': 29263.807, 'duration': 4.463}, {'end': 29275.626, 'text': 'and try and see if there is similarity between the groups and if there is dissimilarity between the groups.', 'start': 29268.72, 'duration': 6.906}, {'end': 29277.007, 'text': 'Right Yeah.', 'start': 29275.886, 'duration': 1.121}, {'end': 29290.578, 'text': 'So again, before applying any model, whether it is supervised or unsupervised, you need to ask yourself, what is the purpose of it? Yes.', 'start': 29278.328, 'duration': 12.25}, {'end': 29291.899, 'text': "Now I got it, I'm clear.", 'start': 29290.638, 'duration': 1.261}, {'end': 29293.54, 'text': 'Thanks Thanks.', 'start': 29292.819, 'duration': 0.721}], 'summary': 'Applying unsupervised learning to cluster data and find similarities between groups.', 'duration': 52.789, 'max_score': 29240.751, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ829240751.jpg'}], 'start': 28379.161, 'title': 'Introduction to neural networks and pca', 'summary': 'Covers the clarification of concepts related to support, confidence, and lift, including an introduction to neural networks and tensorflow with python, and the concept of unsupervised learning focusing on pca, k-mean algorithms, and hierarchical clustering, emphasizing the importance of normalization before applying unsupervised learning algorithms.', 'chapters': [{'end': 28505.523, 'start': 28379.161, 'title': 'Introduction to neural networks and tensorflow', 'summary': 'Covers the clarification of concepts related to support, confidence, and lift, including the discussion of minimum length values and an introduction to the next session on neural networks and tensorflow with python.', 'duration': 126.362, 'highlights': ['The chapter clarifies concepts related to support, confidence, and lift and discusses minimum length values.', 'The next session will cover an introduction to neural networks, deep learning, and the basic TensorFlow package implementation with Python.', "The instructor seeks participants' comfort level and experience with Python for the upcoming session."]}, {'end': 29181.777, 'start': 28506.573, 'title': 'Unsupervised learning with pca', 'summary': 'Covers the concept of unsupervised learning, focusing on k-mean algorithms, hierarchical clustering, and pca, explaining how pca works to reduce dimensions and maximize variance, with a clarification on the purpose and process of pca, and its distinction from normalization and scaling.', 'duration': 675.204, 'highlights': ['PCA reduces dimensions to capture maximum variance, aiding in understanding data distribution. Principal Component Analysis (PCA) works to reduce the number of dimensions, aiming to understand the data with the least number of dimensions while maximizing the variance present in the data.', 'Process of building principal components stops when additional components do not contribute significantly to variance. The process of building principal components stops when adding another component does not add much variance information to the data.', 'Clarification on the purpose of PCA, emphasizing its role in reducing dimensions and capturing variance, distinct from normalization and scaling processes. The purpose of PCA is to reduce the number of dimensions and understand the data with minimal dimensions, capturing maximum variance, distinct from the normalization and scaling processes.', "Explanation of the need for normalizing data when units are not the same, but clarifying that normalization is not directly related to PCA's purpose. The need for normalizing data arises when units are not the same, although the process of normalization is not directly related to the purpose of PCA in representing data with minimal dimensions and capturing variance."]}, {'end': 29487.209, 'start': 29182.421, 'title': 'Pca and hierarchical clustering', 'summary': 'Discusses the confusion around the use of pca without scaling, emphasizing the importance of normalization before applying an unsupervised learning algorithm, and explains the trial and error process of hierarchical clustering to determine the optimal number of clusters based on within-cluster similarity.', 'duration': 304.788, 'highlights': ['The importance of normalization before applying an unsupervised learning algorithm, such as PCA, is emphasized for obtaining accurate results. Normalization of data significantly affects the Principal Component Analysis (PCA) results, highlighting the need for it to be carried out before applying unsupervised learning algorithms. This emphasizes the significance of preprocessing techniques for accurate analysis.', 'The purpose of unsupervised learning algorithms, like PCA, is explained to be clustering the dataset into different groups to identify similarities and dissimilarities, emphasizing the need to understand the purpose before applying any model. The purpose of applying unsupervised learning algorithms, specifically PCA, is to cluster the dataset into different groups and analyze the similarities and dissimilarities between them, stressing the importance of understanding the purpose before applying any model.', 'The trial and error process of hierarchical clustering is described, where the optimal number of clusters is determined based on within-cluster similarity, and the iterative nature of the process is emphasized. Hierarchical clustering involves a trial and error process to determine the optimal number of clusters, based on the assessment of within-cluster similarity, highlighting the iterative nature of the decision-making process.']}], 'duration': 1108.048, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ828379161.jpg', 'highlights': ['The importance of normalization before applying an unsupervised learning algorithm, such as PCA, is emphasized for obtaining accurate results.', 'The purpose of unsupervised learning algorithms, like PCA, is explained to be clustering the dataset into different groups to identify similarities and dissimilarities, emphasizing the need to understand the purpose before applying any model.', 'PCA reduces dimensions to capture maximum variance, aiding in understanding data distribution.', 'The chapter clarifies concepts related to support, confidence, and lift and discusses minimum length values.', 'The next session will cover an introduction to neural networks, deep learning, and the basic TensorFlow package implementation with Python.']}, {'end': 31233.781, 'segs': [{'end': 29527.756, 'src': 'embed', 'start': 29502.682, 'weight': 0, 'content': [{'end': 29511.347, 'text': "so we'd want to extract only those records where the value of the cut is equal to ideal and this price which you see and this price needs to be greater than thousand,", 'start': 29502.682, 'duration': 8.665}, {'end': 29513.248, 'text': 'and we are supposed to implement this with our.', 'start': 29511.347, 'duration': 1.901}, {'end': 29514.588, 'text': "So let's do that.", 'start': 29513.928, 'duration': 0.66}, {'end': 29518.451, 'text': 'Now this diamonds data set is a part of the ggplot2 package.', 'start': 29515.189, 'duration': 3.262}, {'end': 29522.173, 'text': "So first we'll go ahead and load this ggplot2 package.", 'start': 29518.931, 'duration': 3.242}, {'end': 29527.756, 'text': "So I'll use the command library of ggplot2 and then let me have a glance at this data set.", 'start': 29522.533, 'duration': 5.223}], 'summary': 'Extract records with cut=ideal and price>1000 from diamonds dataset using ggplot2.', 'duration': 25.074, 'max_score': 29502.682, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ829502682.jpg'}, {'end': 29655.874, 'src': 'embed', 'start': 29629.866, 'weight': 1, 'content': [{'end': 29635.988, 'text': 'So on the same diamonds data set, we are supposed to make a scatter plot between the price and the carrot.', 'start': 29629.866, 'duration': 6.122}, {'end': 29638.989, 'text': 'And we are supposed to do that using the ggplot2 package.', 'start': 29636.588, 'duration': 2.401}, {'end': 29645.911, 'text': 'So over here, the price needs to be on the y-axis and the carrot needs to be on the x-axis.', 'start': 29639.469, 'duration': 6.442}, {'end': 29648.512, 'text': 'So this column, it needs to be on the x-axis.', 'start': 29646.071, 'duration': 2.441}, {'end': 29649.872, 'text': 'And then we have price.', 'start': 29648.852, 'duration': 1.02}, {'end': 29651.673, 'text': 'This needs to be on the y-axis.', 'start': 29650.052, 'duration': 1.621}, {'end': 29655.874, 'text': 'And the color of the points need to be determined by the cut value.', 'start': 29652.053, 'duration': 3.821}], 'summary': 'Create a scatter plot of price vs. carat using ggplot2, with color based on cut value.', 'duration': 26.008, 'max_score': 29629.866, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ829629866.jpg'}, {'end': 29811.826, 'src': 'embed', 'start': 29788.317, 'weight': 2, 'content': [{'end': 29802.042, 'text': 'So this time we are supposed to introduce 25% missing values in the iris dataset and impute the sepal length column with the mean and similarly impute the petal length column with the median.', 'start': 29788.317, 'duration': 13.725}, {'end': 29803.823, 'text': 'So this is the iris dataset.', 'start': 29802.402, 'duration': 1.421}, {'end': 29807.204, 'text': "So again, let's head back to RStudio and perform these tasks.", 'start': 29804.183, 'duration': 3.021}, {'end': 29809.805, 'text': 'Let me have a glance at the iris dataset first.', 'start': 29807.744, 'duration': 2.061}, {'end': 29811.826, 'text': 'View of iris.', 'start': 29810.105, 'duration': 1.721}], 'summary': 'Introduce 25% missing values in iris dataset, impute sepal length with mean, petal length with median.', 'duration': 23.509, 'max_score': 29788.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ829788317.jpg'}, {'end': 30096.533, 'src': 'embed', 'start': 30072.503, 'weight': 3, 'content': [{'end': 30079.666, 'text': 'So linear regression is a supervised learning algorithm which helps us in finding the linear relationship between two variables.', 'start': 30072.503, 'duration': 7.163}, {'end': 30086.808, 'text': 'So one is the predictor or the independent variable, and the other is the response or the dependent variable.', 'start': 30080.306, 'duration': 6.502}, {'end': 30092.29, 'text': 'and we try to understand how does the dependent variable change with the independent variable?', 'start': 30086.808, 'duration': 5.482}, {'end': 30096.533, 'text': "So let's say there's this telecom company called as Neo,", 'start': 30093.07, 'duration': 3.463}], 'summary': 'Linear regression finds linear relationship between 2 variables in supervised learning.', 'duration': 24.03, 'max_score': 30072.503, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ830072503.jpg'}, {'end': 30161.098, 'src': 'embed', 'start': 30134.025, 'weight': 4, 'content': [{'end': 30142.85, 'text': 'So, guys, this is the underlying concept of linear regression, where we have one dependent variable and multiple or a single independent variable,', 'start': 30134.025, 'duration': 8.825}, {'end': 30148.894, 'text': 'and we try to understand the linear relationship between the dependent variable and the independent variables.', 'start': 30142.85, 'duration': 6.044}, {'end': 30155.156, 'text': "So next, we're supposed to implement this simple linear regression in R on this empty cars data set,", 'start': 30149.634, 'duration': 5.522}, {'end': 30161.098, 'text': 'where the dependent variable is this mpg column and the independent variable is this displacement column.', 'start': 30155.156, 'duration': 5.942}], 'summary': 'Linear regression in r on cars dataset, mpg vs. displacement.', 'duration': 27.073, 'max_score': 30134.025, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ830134025.jpg'}, {'end': 30476.307, 'src': 'embed', 'start': 30452.236, 'weight': 5, 'content': [{'end': 30459.141, 'text': 'Now when we actually build a regression model, it predicts certain Y values associated with a given X value.', 'start': 30452.236, 'duration': 6.905}, {'end': 30463.264, 'text': 'But there always is an error associated with this prediction.', 'start': 30459.601, 'duration': 3.663}, {'end': 30470.789, 'text': 'So to get an estimate of average error during prediction, RMSE or root mean square error is used.', 'start': 30463.884, 'duration': 6.905}, {'end': 30476.307, 'text': "So again, let's go ahead and calculate the RMSE value for the model which we've just built.", 'start': 30471.485, 'duration': 4.822}], 'summary': 'Regression model predicts y values for given x with rmse estimation.', 'duration': 24.071, 'max_score': 30452.236, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ830452236.jpg'}, {'end': 30660.652, 'src': 'embed', 'start': 30631.781, 'weight': 6, 'content': [{'end': 30635.064, 'text': 'So this is how we can calculate the root mean square error.', 'start': 30631.781, 'duration': 3.283}, {'end': 30640.129, 'text': "So final data dollar error and we'll square this error first.", 'start': 30635.524, 'duration': 4.605}, {'end': 30649.037, 'text': "After that we'll take the mean of this squared error and then finally we'll take the square root of the mean squared error.", 'start': 30640.669, 'duration': 8.368}, {'end': 30651.284, 'text': 'So this is what we have.', 'start': 30650.083, 'duration': 1.201}, {'end': 30654.787, 'text': "So the RMSE value for this model which we've built is 4.33.", 'start': 30651.524, 'duration': 3.263}, {'end': 30660.652, 'text': 'Now you guys need to keep in mind that the lower the value of RMSE is, the better the model.', 'start': 30654.787, 'duration': 5.865}], 'summary': 'Rmse for the model is 4.33, indicating its predictive accuracy.', 'duration': 28.871, 'max_score': 30631.781, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ830631781.jpg'}, {'end': 31196.972, 'src': 'embed', 'start': 31169.445, 'weight': 8, 'content': [{'end': 31174.706, 'text': 'and again, logistic regression algorithm, it actually produces an s curve.', 'start': 31169.445, 'duration': 5.261}, {'end': 31180.147, 'text': "so let's say x-axis over here it represents the number of runs scored by virat kohli,", 'start': 31174.706, 'duration': 5.441}, {'end': 31184.468, 'text': 'and the y-axis represents the probability of team india winning the match.', 'start': 31180.147, 'duration': 4.321}, {'end': 31188.009, 'text': "So let's say this point over here it denotes 50 runs.", 'start': 31185.248, 'duration': 2.761}, {'end': 31196.972, 'text': 'So what we can see from this graph is so if Virat Kohli scores more than 50 runs, then there is a greater probability for Team India to win the match.', 'start': 31188.509, 'duration': 8.463}], 'summary': "Logistic regression shows higher virat kohli runs increase india's match win probability.", 'duration': 27.527, 'max_score': 31169.445, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ831169445.jpg'}], 'start': 29488.095, 'title': 'Data analysis techniques', 'summary': 'Covers data set filtering, scatter plot creation, imputing missing values, linear regression, and model evaluation in r and python, achieving rmse values and providing insights on model accuracy and improvement potential.', 'chapters': [{'end': 29629.326, 'start': 29488.095, 'title': 'Data set filtering with dplyr', 'summary': 'Demonstrates how to use the dplyr package to extract 14,700 records from a data set of 53,940 entries, where the cut is equal to ideal and the price of the diamond is greater than 1000.', 'duration': 141.231, 'highlights': ['The dplyr package is used to filter the diamonds data set, resulting in 14,700 records where the cut is equal to ideal and the price is greater than 1000.', 'The total number of entries in the filtered data set is specified as 14,700 out of the original 53,940 records.', "The command 'library(dplyr)' is used to load the dplyr package for data manipulation."]}, {'end': 30024.929, 'start': 29629.866, 'title': 'Creating scatter plot and imputing missing values', 'summary': 'Covers creating a scatter plot using ggplot2 in r to visualize the relationship between diamond price, carat, and cut, and introducing 25% missing values in the iris dataset and imputing the sepal length column with the mean and the petal length column with the median.', 'duration': 395.063, 'highlights': ['Creating a scatter plot using ggplot2 in R to visualize the relationship between diamond price, carat, and cut The scatter plot illustrates that fair cut diamonds tend to have higher prices and carat values, while ideal cut diamonds have a carat range from 0 to 4, providing insights into the relationship between diamond characteristics.', 'Introducing 25% missing values in the iris dataset and imputing the sepal length column with the mean and the petal length column with the median 25% missing values are introduced using the Miss Forest package, and the sepal length column is imputed with the mean, and the petal length column is imputed with the median using the hmisc package, resulting in the replacement of missing values with appropriate statistical measures.']}, {'end': 30476.307, 'start': 30024.929, 'title': 'Linear regression and model building', 'summary': 'Discusses linear regression, including its definition, application in a telecom company, division of dataset for training and testing, and building a simple linear regression model in r on the empty cars dataset, achieving an rmse value for the model.', 'duration': 451.378, 'highlights': ['Linear regression is a supervised learning algorithm that finds the linear relationship between two variables, with examples of predictor and response variables and an application in a telecom company. Linear regression is a supervised learning algorithm that finds the linear relationship between two variables, with examples of predictor and response variables and an application in a telecom company.', 'Explanation of simple and multiple linear regression and their respective characteristics. Explanation of simple and multiple linear regression and their respective characteristics.', 'Division of the dataset into training and testing sets, the importance of this division, and the use of the caret package for this purpose. Division of the dataset into training and testing sets, the importance of this division, and the use of the caret package for this purpose.', 'Building a simple linear regression model in R on the empty cars dataset, including the selection of dependent and independent variables, and division of the dataset into training and testing sets. Building a simple linear regression model in R on the empty cars dataset, including the selection of dependent and independent variables, and division of the dataset into training and testing sets.', 'Calculation of the root mean square error (RMSE) value for the model, and its importance in estimating the average error during prediction. Calculation of the root mean square error (RMSE) value for the model, and its importance in estimating the average error during prediction.']}, {'end': 30675.124, 'start': 30477.227, 'title': 'Calculating rmse for model evaluation', 'summary': "Demonstrates how to bind actual and predicted values, calculate prediction error, and determine the root mean square error (rmse) for a model, with the resulting rmse value being 4.33, indicating the model's accuracy and improvement potential.", 'duration': 197.897, 'highlights': ['The chapter demonstrates how to bind actual and predicted values, calculate prediction error, and determine the root mean square error (RMSE) for a model. The process involves creating a data frame with actual and predicted values, converting the error metrics into a data frame, and calculating the RMSE.', "The resulting RMSE value is 4.33, indicating the model's accuracy and improvement potential. The RMSE value of 4.33 denotes the average error in the model's predictions, with lower values indicating a better model performance and potential for improvement."]}, {'end': 31233.781, 'start': 30675.885, 'title': 'Implementing simple linear regression in python', 'summary': 'Covers implementing simple linear regression in python on the boston dataset, with insights on data exploration, model building, and evaluation metrics, achieving a mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62, and then briefly explains logistic regression with examples and visual representation.', 'duration': 557.896, 'highlights': ["The implemented simple linear regression model achieved a mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62, indicating the model's performance in predicting the median value of house prices.", 'The chapter demonstrates separating the independent and dependent variables, visualizing the relationship between the independent and dependent variables using a scatter plot, and splitting the dataset into training and test sets with an 80-20 split, essential steps in building a linear regression model.', 'The chapter provides a brief explanation of logistic regression as a classification algorithm for binary dependent variables, offering an example of determining the probability of rain based on temperature and humidity and providing a visual representation of logistic regression using the example of runs scored by Virat Kohli and the probability of Team India winning a match.']}], 'duration': 1745.686, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ829488095.jpg', 'highlights': ['The dplyr package is used to filter the diamonds data set, resulting in 14,700 records where the cut is equal to ideal and the price is greater than 1000.', 'Creating a scatter plot using ggplot2 in R to visualize the relationship between diamond price, carat, and cut.', 'Introducing 25% missing values in the iris dataset and imputing the sepal length column with the mean and the petal length column with the median.', 'Linear regression is a supervised learning algorithm that finds the linear relationship between two variables, with examples of predictor and response variables and an application in a telecom company.', 'Building a simple linear regression model in R on the empty cars dataset, including the selection of dependent and independent variables, and division of the dataset into training and testing sets.', 'The chapter demonstrates how to bind actual and predicted values, calculate prediction error, and determine the root mean square error (RMSE) for a model.', "The resulting RMSE value is 4.33, indicating the model's accuracy and improvement potential.", "The implemented simple linear regression model achieved a mean absolute error of 4.69, mean squared error of 43, and root mean squared error of 6.62, indicating the model's performance in predicting the median value of house prices.", 'The chapter provides a brief explanation of logistic regression as a classification algorithm for binary dependent variables, offering an example of determining the probability of rain based on temperature and humidity and providing a visual representation of logistic regression using the example of runs scored by Virat Kohli and the probability of Team India winning a match.']}, {'end': 32414.267, 'segs': [{'end': 31477.452, 'src': 'embed', 'start': 31446.655, 'weight': 2, 'content': [{'end': 31449.897, 'text': 'So this basically means that we can reject the null hypothesis.', 'start': 31446.655, 'duration': 3.242}, {'end': 31457.202, 'text': 'The null hypothesis states that there is no relationship between the H and the target columns.', 'start': 31450.778, 'duration': 6.424}, {'end': 31460.144, 'text': 'But since we have three stars over here,', 'start': 31457.783, 'duration': 2.361}, {'end': 31468.01, 'text': 'this states that the null hypothesis can be rejected and there is a strong relationship between the H column and the target column.', 'start': 31460.144, 'duration': 7.866}, {'end': 31470.331, 'text': 'Now again, we have other parameters over here.', 'start': 31468.59, 'duration': 1.741}, {'end': 31473.151, 'text': 'So we have something known as null deviance and residual deviance.', 'start': 31470.471, 'duration': 2.68}, {'end': 31477.452, 'text': 'So simply put, the lower the deviance value, the better the model.', 'start': 31473.671, 'duration': 3.781}], 'summary': 'Reject null hypothesis, strong relationship between h and target columns, lower deviance value indicates better model.', 'duration': 30.797, 'max_score': 31446.655, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ831446655.jpg'}, {'end': 31519.258, 'src': 'embed', 'start': 31493.937, 'weight': 1, 'content': [{'end': 31501.304, 'text': 'And residual deviance is that deviance when we include the independent variables and try to predict the target column.', 'start': 31493.937, 'duration': 7.367}, {'end': 31507.549, 'text': 'So when we include the independent variable, which is H, we see that the residual deviance drops.', 'start': 31501.764, 'duration': 5.785}, {'end': 31513.254, 'text': 'So initially when there were no independent variables, the null deviance was 417.', 'start': 31507.97, 'duration': 5.284}, {'end': 31519.258, 'text': 'After we included the age column, we see that the null deviance has reduced to 401.', 'start': 31513.254, 'duration': 6.004}], 'summary': "Including the independent variable 'h' reduced null deviance from 417 to 401.", 'duration': 25.321, 'max_score': 31493.937, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ831493937.jpg'}, {'end': 31620.375, 'src': 'embed', 'start': 31593.007, 'weight': 0, 'content': [{'end': 31600.569, 'text': 'So you see that when the age is 29, the probability of the person not having heart disease is 82%.', 'start': 31593.007, 'duration': 7.562}, {'end': 31607.33, 'text': 'Similarly, when the age of the person is 30, the probability of the person not having heart disease is 81%.', 'start': 31600.569, 'duration': 6.761}, {'end': 31615.793, 'text': 'So as the age increases from 29 to 77, the probability of the person not having heart disease decreases.', 'start': 31607.33, 'duration': 8.463}, {'end': 31620.375, 'text': 'So this is the final case over here when the age of the person is 77.', 'start': 31616.294, 'duration': 4.081}], 'summary': 'Probability of not having heart disease decreases with age, from 82% at 29 to 77% at 77.', 'duration': 27.368, 'max_score': 31593.007, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ831593007.jpg'}, {'end': 31831.306, 'src': 'embed', 'start': 31805.832, 'weight': 3, 'content': [{'end': 31814.155, 'text': 'So we see that the range of the predicted values of the heart disease, it varies from 0.21 to 0.86.', 'start': 31805.832, 'duration': 8.323}, {'end': 31818.717, 'text': 'That is the probability of the patient having heart disease.', 'start': 31814.155, 'duration': 4.562}, {'end': 31823.88, 'text': 'It varies from 21% to 86%.', 'start': 31819.138, 'duration': 4.742}, {'end': 31829.062, 'text': 'And this is how we can build a simple logistic regression model on top of this heart disease dataset.', 'start': 31823.88, 'duration': 5.182}, {'end': 31831.306, 'text': "Now let's head on to the next question.", 'start': 31829.898, 'duration': 1.408}], 'summary': 'Logistic regression model predicts heart disease probability from 21% to 86%.', 'duration': 25.474, 'max_score': 31805.832, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ831805832.jpg'}, {'end': 31926.272, 'src': 'embed', 'start': 31901.359, 'weight': 4, 'content': [{'end': 31909.403, 'text': 'So if you want to get the correct values, then correct values would basically represent all of the true positives and the true negatives.', 'start': 31901.359, 'duration': 8.044}, {'end': 31912.225, 'text': 'And this is how confusion matrix actually works.', 'start': 31909.964, 'duration': 2.261}, {'end': 31918.289, 'text': 'So now we are supposed to build a confusion matrix for the model which we built,', 'start': 31914.448, 'duration': 3.841}, {'end': 31926.272, 'text': 'where the threshold value for the probability of predicted values is 0.6, and then we have to find the accuracy of the model.', 'start': 31918.289, 'duration': 7.983}], 'summary': 'Building a confusion matrix for a model with a 0.6 threshold to find accuracy.', 'duration': 24.913, 'max_score': 31901.359, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ831901359.jpg'}], 'start': 31235.342, 'title': 'Logistic regression and heart disease prediction', 'summary': 'Covers the implementation of logistic regression on a heart data set to predict the probability of heart disease based on age, demonstrating a strong relationship between age and heart disease. it discusses splitting the data into training and test sets, building a logistic regression model, and evaluating model performance using confusion matrix, accuracy, true positive rate, false positive rate, and roc curve.', 'chapters': [{'end': 31446.195, 'start': 31235.342, 'title': 'Logistic regression on heart data set', 'summary': 'Explains logistic regression using a heart data set, where logistic regression is implemented to predict the probability of a person having heart disease based on age, and the model is built with all the data without division into training and testing sets.', 'duration': 210.853, 'highlights': ['Logistic regression model predicts probability of heart disease based on age without splitting data into training and testing sets The chapter explains logistic regression using a heart data set, where logistic regression is implemented to predict the probability of a person having heart disease based on age, and the model is built with all the data without division into training and testing sets.', "Renaming column 'IH' to 'H' using column names function The column 'IH' is successfully renamed to 'H' using the column names function in R.", 'Conversion of integer target column into a categorical value with levels 0 and 1 The integer target column is converted into a categorical value with two levels (0 and 1) using the as.factor function.', 'Summary of logistic regression model with P value and three stars The summary of the logistic regression model displays a P value with three associated stars, indicating significance.']}, {'end': 31644.999, 'start': 31446.655, 'title': 'Modeling heart disease probabilities', 'summary': 'Discusses rejecting the null hypothesis, demonstrating a strong relationship between age and heart disease, with a 417 to 401 drop in deviance, and predicting heart disease probabilities based on age, with a decrease from 82% at age 29 to 26% at age 77.', 'duration': 198.344, 'highlights': ['The null hypothesis can be rejected, indicating a strong relationship between the H column and the target column, demonstrated by three stars.', "The residual deviance drops from 417 to 401 when the independent variable 'H' is included, showing a strong relationship between the age column and the target column.", 'The probability of a person not having heart disease decreases from 82% at age 29 to 26% at age 77, suggesting an increasing likelihood of heart disease with age.']}, {'end': 31831.306, 'start': 31645.279, 'title': 'Splitting data, building model, and predicting values', 'summary': 'Covers splitting the dataset into training and test sets with a 70-30 split, building a logistic regression model on the training set to predict the probability of heart disease based on age, and achieving predicted values ranging from 0.21 to 0.86.', 'duration': 186.027, 'highlights': ['Splitting the dataset into training and test sets with a 70-30 split The data is divided into training and test sets with a split ratio of 70% for training and 30% for testing.', "Building a logistic regression model to predict the probability of heart disease based on age A logistic regression model is built using the GLM function with the formula 'target ~ age' to predict the probability of heart disease based on the patient's age.", 'Achieving predicted values ranging from 0.21 to 0.86 for the probability of heart disease The range of predicted values for the probability of heart disease varies from 0.21 to 0.86, indicating a wide spectrum of probabilities for the presence of heart disease.']}, {'end': 32414.267, 'start': 31831.87, 'title': 'Confusion matrix and model evaluation', 'summary': 'Explains the concept of a confusion matrix, its use in evaluating model performance, building a confusion matrix for a model with a probability threshold of 0.6, and finding the accuracy of the model. it also delves into true positive rate, false positive rate, and roc curve, including their definitions, calculations, and significance in evaluating model performance.', 'duration': 582.397, 'highlights': ['The chapter explains the concept of a confusion matrix, its use in evaluating model performance, building a confusion matrix for a model with a probability threshold of 0.6, and finding the accuracy of the model. It details the process of building a confusion matrix and calculating the accuracy of a model with a probability threshold of 0.6, resulting in an overall accuracy of 53%.', 'It delves into true positive rate, false positive rate, and ROC curve, including their definitions, calculations, and significance in evaluating model performance. It provides explanations and formulas for true positive rate, false positive rate, and ROC curve, emphasizing their role in evaluating model performance and finding the right trade-off between true positive and false positive rates.', 'The chapter also discusses the process of building a logistic regression model in Python for customer churn prediction and finding the log loss of the model. It outlines the steps for building a logistic regression model in Python for customer churn prediction, emphasizing the use of monthly charges as an independent variable and finding the log loss of the model.']}], 'duration': 1178.925, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ831235342.jpg', 'highlights': ['The probability of a person not having heart disease decreases from 82% at age 29 to 26% at age 77, suggesting an increasing likelihood of heart disease with age.', "The residual deviance drops from 417 to 401 when the independent variable 'H' is included, showing a strong relationship between the age column and the target column.", 'The null hypothesis can be rejected, indicating a strong relationship between the H column and the target column, demonstrated by three stars.', 'The range of predicted values for the probability of heart disease varies from 0.21 to 0.86, indicating a wide spectrum of probabilities for the presence of heart disease.', 'The chapter explains the concept of a confusion matrix, its use in evaluating model performance, building a confusion matrix for a model with a probability threshold of 0.6, and finding the accuracy of the model. It details the process of building a confusion matrix and calculating the accuracy of a model with a probability threshold of 0.6, resulting in an overall accuracy of 53%.']}, {'end': 33725.351, 'segs': [{'end': 32506.241, 'src': 'embed', 'start': 32479.462, 'weight': 1, 'content': [{'end': 32483.667, 'text': 'so the independent variable is stored in x and the dependent variable is stored in y.', 'start': 32479.462, 'duration': 4.205}, {'end': 32487.512, 'text': "after that i'll set the test size to be equal to 0.3.", 'start': 32483.667, 'duration': 3.845}, {'end': 32494.835, 'text': 'So this basically states that 70% of the records would be in the training set and the rest 30% of the records would be in the test set.', 'start': 32487.512, 'duration': 7.323}, {'end': 32500.018, 'text': "And again, I'll set a random state value so that I can use the same values again if I want to.", 'start': 32495.236, 'duration': 4.782}, {'end': 32506.241, 'text': 'And I am storing all of these values into X train, X test, Y train and Y test.', 'start': 32500.478, 'duration': 5.763}], 'summary': 'Data split with 70% training and 30% testing.', 'duration': 26.779, 'max_score': 32479.462, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ832479462.jpg'}, {'end': 32608.086, 'src': 'embed', 'start': 32577.04, 'weight': 0, 'content': [{'end': 32580.962, 'text': 'So the question stated that we were supposed to find out the log loss.', 'start': 32577.04, 'duration': 3.922}, {'end': 32590.929, 'text': "So I'll just import the log loss from sklearn.metrics and inside this log loss function, I will pass in the actual values and the predicted values.", 'start': 32581.523, 'duration': 9.406}, {'end': 32595.892, 'text': 'So actual values are stored in ytest and predicted values are stored in ypret.', 'start': 32591.349, 'duration': 4.543}, {'end': 32600.843, 'text': 'So we get a log loss value of 0.55.', 'start': 32598.382, 'duration': 2.461}, {'end': 32608.086, 'text': 'Now again similar to RMSE the lower the value of log loss the better the model is.', 'start': 32600.843, 'duration': 7.243}], 'summary': 'Log loss value of 0.55 indicates model performance.', 'duration': 31.046, 'max_score': 32577.04, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ832577040.jpg'}, {'end': 32641.918, 'src': 'embed', 'start': 32620.17, 'weight': 2, 'content': [{'end': 32629.231, 'text': 'So what do you understand by decision tree? So decision tree is a supervised learning algorithm which is used for both classification and regression.', 'start': 32620.17, 'duration': 9.061}, {'end': 32635.634, 'text': 'Right So decision tree can be used for both classification purpose as well as regression purpose.', 'start': 32629.831, 'duration': 5.803}, {'end': 32641.918, 'text': 'So in this case, the dependent variable can be both a numerical value as well as a categorical value.', 'start': 32635.994, 'duration': 5.924}], 'summary': 'Decision tree is a supervised learning algorithm for classification and regression, handling both numerical and categorical values.', 'duration': 21.748, 'max_score': 32620.17, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ832620170.jpg'}, {'end': 33071.281, 'src': 'embed', 'start': 33048.045, 'weight': 3, 'content': [{'end': 33055.39, 'text': 'this states that there is one record where the actual value was virginica, but it has been incorrectly classified as versicolor.', 'start': 33048.045, 'duration': 7.345}, {'end': 33057.811, 'text': 'Similarly the 16 which we see.', 'start': 33055.811, 'duration': 2}, {'end': 33065.396, 'text': 'so, out of the 16 records which were actually virginica, all of those 16 records have been correctly classified as virginica.', 'start': 33057.811, 'duration': 7.585}, {'end': 33071.281, 'text': 'Now again, to find out the accuracy, we are supposed to divide this left diagonal with all of the values.', 'start': 33065.797, 'duration': 5.484}], 'summary': 'Misclassification of 1 virginica as versicolor, 16 correctly classified virginica out of 16.', 'duration': 23.236, 'max_score': 33048.045, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ833048045.jpg'}, {'end': 33340.292, 'src': 'embed', 'start': 33312.389, 'weight': 4, 'content': [{'end': 33315.913, 'text': 'So guys, this is the working mechanism of random forest.', 'start': 33312.389, 'duration': 3.524}, {'end': 33321.858, 'text': "So now we'd have to go and build a random forest model on top of the CTG dataset,", 'start': 33316.333, 'duration': 5.525}, {'end': 33326.684, 'text': 'where NSP is the dependent variable and all other columns are independent variables.', 'start': 33321.858, 'duration': 4.826}, {'end': 33330.669, 'text': "So let's head on to RStudio and implement the random forest model.", 'start': 33327.324, 'duration': 3.345}, {'end': 33334.871, 'text': 'right. so let me start off by loading the CTG data set.', 'start': 33331.75, 'duration': 3.121}, {'end': 33338.252, 'text': "so I'll use the read dot CSV function and load this file.", 'start': 33334.871, 'duration': 3.381}, {'end': 33340.292, 'text': 'now let me have a glance at this data.', 'start': 33338.252, 'duration': 2.04}], 'summary': 'Building a random forest model on ctg dataset in rstudio.', 'duration': 27.903, 'max_score': 33312.389, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ833312389.jpg'}], 'start': 32414.727, 'title': 'Building predictive models in machine learning', 'summary': 'Covers building logistic regression and random forest models, achieving a 70-30 data split, log loss value of 0.55, 96% accuracy rate, and using decision tree for supervised learning with a train-test split of 65-35. it also includes constructing a confusion matrix and using the random forest model on the ctg dataset to predict cancer presence.', 'chapters': [{'end': 32619.43, 'start': 32414.727, 'title': 'Building logistic regression model', 'summary': 'Focuses on building a logistic regression model to predict customer churn, separating the data into training and testing sets with a 70-30 split and achieving a log loss value of 0.55.', 'duration': 204.703, 'highlights': ["The monthly charges column tells us the monthly charges incurred by the customer in dollars, and the churn column indicates whether the customer would churn or not. The monthly charges column provides information on the customer's monthly charges in dollars, while the churn column indicates customer churn status.", 'The model is divided into training and testing sets with a 70% training set and a 30% testing set, resulting in X train, X test, Y train, and Y test. The data is split into a 70-30 ratio for training and testing sets, resulting in X train, X test, Y train, and Y test.', "The log loss value obtained for the model is 0.55, indicating the model's performance in predicting customer churn. The log loss value achieved for the model is 0.55, demonstrating its performance in predicting customer churn."]}, {'end': 32965.441, 'start': 32620.17, 'title': 'Decision tree in supervised learning', 'summary': "Discusses the concept of decision tree as a supervised learning algorithm for classification and regression, demonstrated through the construction of a model on the iris data set with a train-test split of 65-35, resulting in an accuracy assessment, and visual representation of the decision tree's split criteria and class probabilities.", 'duration': 345.271, 'highlights': ['The chapter explains the concept of a decision tree as a supervised learning algorithm for classification and regression, with the ability to handle both numerical and categorical dependent variables.', 'The demonstration involves building a decision tree model on the iris data set, utilizing a train-test split of 65-35, resulting in 99 rows in the training set and 51 rows in the testing set for model construction and accuracy assessment.', "The visual representation of the decision tree's split criteria and class probabilities shows the root node's split based on the petal length column, with subsequent test conditions and associated probabilities for species classification.", 'The process involves utilizing the party package for building the decision tree model and the carrot package for the train-test split, emphasizing the practical implementation of the supervised learning algorithm.', "The explanation covers the structure of a decision tree, including the root node, branch nodes, and leaf nodes, each denoting a test on an attribute and holding a class label, providing a comprehensive understanding of the algorithm's components and functionality."]}, {'end': 33311.788, 'start': 32965.821, 'title': 'Building confusion matrix and understanding random forest model', 'summary': "Discusses the process of building a confusion matrix to evaluate the model's accuracy, achieving a 96% accuracy rate. it also explains the working mechanism of a random forest model, which involves creating multiple data sets from a single data set and fitting multiple decision trees to achieve different predictions.", 'duration': 345.967, 'highlights': ['Building a confusion matrix to evaluate model accuracy, achieving a 96% accuracy rate by correctly classifying setosa, versicolor, and virginica instances.', 'Explaining the working mechanism of a random forest model, involving the creation of multiple data sets from a single data set, and fitting multiple decision trees with a random subset of predictors to achieve different predictions.']}, {'end': 33725.351, 'start': 33312.389, 'title': 'Building random forest model on ctg dataset', 'summary': 'Discusses building a random forest model with a 96% accuracy on the ctg dataset to predict the presence of cancer, dividing the data into training and test sets, and using the random forest package in rstudio.', 'duration': 412.962, 'highlights': ['Building a random forest model with a 96% accuracy on the CTG dataset, predicting the presence of cancer.', 'Dividing the data into training and test sets with 1383 records in the training set and 743 records in the test set.', 'Using the random forest package in RStudio to build the model, setting a seed value of 222, and converting the NSP column from an integer to a factor with three levels.']}], 'duration': 1310.624, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/yl7o-56NMJ8/pics/yl7o-56NMJ832414727.jpg', 'highlights': ['Log loss value achieved for the model is 0.55, demonstrating its performance in predicting customer churn.', 'Data is split into a 70-30 ratio for training and testing sets, resulting in X train, X test, Y train, and Y test.', 'The chapter explains the concept of a decision tree as a supervised learning algorithm for classification and regression.', 'Building a confusion matrix to evaluate model accuracy, achieving a 96% accuracy rate by correctly classifying setosa, versicolor, and virginica instances.', 'Building a random forest model with a 96% accuracy on the CTG dataset, predicting the presence of cancer.']}], 'highlights': ['Data science is one of the hottest jobs of the 21st century, with an average salary of $123,000 per year.', 'India has seen a 400 times growth in job postings for data science profile in the past one year.', 'The United States faces a shortage of 1.5 million data scientists.', 'Data science enables manipulation and visualization of data to derive new and meaningful insights, making it a crucial aspect of decision-making processes.', 'Data science concepts can be used to perform predictive analysis, offering the potential to predict future events based on current data, facilitating informed decision-making.', 'Data science is instrumental in the telecom industry for customer retention by analyzing data usage patterns, social media activity, demographics, and tailoring personalized offers to retain customers.', 'Data science is pivotal in fraud detection by analyzing and identifying unusual transaction patterns, allowing for proactive measures to prevent fraudulent activities and ensure customer security.', 'R is the most widely used language for data science tasks, providing over 10,000 packages for data visualization, manipulation, machine learning, and statistical analysis.', 'Python is in close competition with R, offering packages for deep learning like Keras and TensorFlow, facilitating the creation of deep neural networks.', 'Linear regression predicts the increase in monthly charges with customer tenure, enabling predictions of specific values, e.g., monthly charges at 45 months tenure to be around $64 and at 69 months tenure to be around $110.', 'Logistic regression is introduced as a technique for determining the probability of an observation belonging to a particular category, exemplified through the example of predicting rain based on temperature and humidity.', 'The ROC curve assesses model performance with respect to all classification thresholds, determining the right threshold value.', 'Random forest introduces randomness by providing a random subset of columns to the algorithm for each node split, resulting in very different decision trees compared to bagging.', 'Decision tree classification achieved 75% accuracy on the test set.', 'Ensemble learning combines the results of multiple decision trees to obtain a collective opinion, illustrated through the example of movie recommendations.', 'The K-Means algorithm clustered 150 data points into four clusters, assigning each data point to a specific cluster.', 'The reduction of total sum of squares from 681 to 71 after applying the k-means algorithm signifies a significant decrease in deviation within the dataset.', 'The K-means algorithm divides the dataset into four clusters using the kmeans.any function, with 11 data points in the first cluster, 12 in the second, 15 in the third, and 12 in the fourth, resulting in a reduction of total within SS from 9.34 to 1.95.', 'The K-means algorithm is also applied to the iris dataset, dividing the data into three clusters, with the first 50 records clustered into cluster number three, while a mix of versicolor and virginica is present in clusters two and one, demonstrating its ability to understand similarities between different properties.', 'The Miss Forest package is utilized to introduce 30% missing values randomly into the original Iris dataset.', "The Miss Forest package's functionality of imputing missing values with the random forest algorithm is detailed, including an explanation of how the algorithm works and its application to both numerical and categorical values.", 'The process of imputing missing values in a dataset is crucial, emphasizing the importance of imputation over omission.', "Collaborative filtering involves recommending values from similar entities, demonstrated by recommending comedy sitcoms to User 1 based on User 2's similar taste.", 'The MovieLens data set comprises 99,392 ratings, 943 users, and 1,664 movies, forming a real rating matrix.', 'Association rule mining uses if-then clauses to find patterns and correlations in data.', 'PCA reduces dimensions to capture maximum variance, aiding in understanding data distribution.', 'The dplyr package is used to filter the diamonds data set, resulting in 14,700 records where the cut is equal to ideal and the price is greater than 1000.', 'Linear regression is a supervised learning algorithm that finds the linear relationship between two variables, with examples of predictor and response variables and an application in a telecom company.', 'Building a confusion matrix to evaluate model accuracy, achieving a 96% accuracy rate by correctly classifying setosa, versicolor, and virginica instances.']}