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Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algorithms | Simplilearn

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This Machine Learning Algorithms video will help you learn what is Machine Learning, various Machine Learning problems and algorithms, key Machine Learning algorithms with simple examples, and use cases implemented in Python. The key Machine Learning algorithms discussed in detail are Linear Regression, Logistic Regression, Decision Tree, Random Forest, and KNN algorithm.
Below topics are covered in this Machine Learning Algorithms Tutorial:
00:00 - 03:39 Machine Learning example and real-world applications
03:39 - 04:40 What is Machine Learning?
04:40 - 06:14 Processes involved in Machine Learning
06:14 - 09:40 Type of Machine Learning Algorithms
09:40 - 10:04 Popular Algorithms in Machine Learning
10:04 - 29:10 Linear regression
29:10 - 52:49 Logistic regression
52:49 - 01:04:45 Decision tree and Random forest
01:04:52 - 01:10:28 K nearest neighbor
Dataset Link - https://drive.google.com/drive/folders/1FaV91OkTsABJrjnfeeTR4rwLe0mxFHxZ
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{'title': 'Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algorithms | Simplilearn', 'heatmap': [{'end': 940.788, 'start': 893.774, 'weight': 1}], 'summary': "Covers machine learning basics, real-world applications, and popular algorithms like linear regression, logistic regression, decision tree, random forest, and k nearest neighbors. it also highlights the widespread use of machine learning in industries such as security, healthcare, and entertainment, with specific examples like facial and voice recognition, and customer behavior analysis for content creation on netflix. the training process, model accuracy, and practical demonstrations are also included, achieving a root mean square error of 58 and an 87% accuracy for logistic regression. furthermore, it explores decision tree's versatility and ease of representation, and presents real-world applications such as job offer decision-making and kyphosis classification.", 'chapters': [{'end': 206.496, 'segs': [{'end': 73.166, 'src': 'embed', 'start': 43.286, 'weight': 6, 'content': [{'end': 50.511, 'text': 'Snapchat actually does this using a technique called facial recognition, which in turn uses machine learning.', 'start': 43.286, 'duration': 7.225}, {'end': 58.917, 'text': 'The machine learning algorithm detects the features on your face, like the nose, the eyes, and it knows where exactly your eyes are,', 'start': 50.871, 'duration': 8.046}, {'end': 62.839, 'text': 'where exactly your nose is, and accordingly it applies the filters.', 'start': 58.917, 'duration': 3.922}, {'end': 73.166, 'text': 'We will take a few more examples as we move along and try to understand how machine learning algorithms can be applied to solve some of our real life problems.', 'start': 63.199, 'duration': 9.967}], 'summary': 'Snapchat uses facial recognition and machine learning to apply filters based on specific facial features.', 'duration': 29.88, 'max_score': 43.286, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg43286.jpg'}, {'end': 185.105, 'src': 'embed', 'start': 85.973, 'weight': 0, 'content': [{'end': 89.195, 'text': 'We will also see the process involved in machine learning.', 'start': 85.973, 'duration': 3.222}, {'end': 94.437, 'text': 'types of machine learning algorithms and we will also see a few hands-on,', 'start': 89.995, 'duration': 4.442}, {'end': 100.82, 'text': 'including some code python code of the following algorithms linear regression, logistic regression,', 'start': 94.437, 'duration': 6.383}, {'end': 104.842, 'text': 'decision tree and random forest and k nearest neighbors.', 'start': 100.82, 'duration': 4.022}, {'end': 107.543, 'text': "Okay so, let's get started.", 'start': 105.302, 'duration': 2.241}, {'end': 111.625, 'text': "Let's consider some of the real world applications of machine learning.", 'start': 107.863, 'duration': 3.762}, {'end': 113.966, 'text': "It's no longer just a buzzword.", 'start': 112.105, 'duration': 1.861}, {'end': 121.23, 'text': 'Machine learning is being used in a variety of industries to solve a variety of problems.', 'start': 114.467, 'duration': 6.763}, {'end': 123.771, 'text': 'Facial recognition is one of them.', 'start': 121.61, 'duration': 2.161}, {'end': 130.435, 'text': "It's becoming very popular these days for security, for police, for solving crime, a lot of areas.", 'start': 124.152, 'duration': 6.283}, {'end': 132.536, 'text': 'facial recognition is being used.', 'start': 131.075, 'duration': 1.461}, {'end': 134.456, 'text': 'Voice recognition is another area.', 'start': 132.916, 'duration': 1.54}, {'end': 136.197, 'text': "It's becoming very common these days.", 'start': 134.516, 'duration': 1.681}, {'end': 138.458, 'text': 'Some of you must be using Siri.', 'start': 136.637, 'duration': 1.821}, {'end': 141.879, 'text': "That's an example of machine learning and voice recognition.", 'start': 138.678, 'duration': 3.201}, {'end': 147.381, 'text': 'Healthcare industry is another big area where machine learning is adopted in a very big way.', 'start': 142.099, 'duration': 5.282}, {'end': 158.705, 'text': "As you all may be aware, diagnostics needs analysis of images, let's say, like x-ray or MRI, and increasingly, because of the shortage of doctors,", 'start': 147.641, 'duration': 11.064}, {'end': 169.334, 'text': 'Machine learning and artificial intelligence is being used to help and support doctors in analyzing these images and identifying the advent of any diseases.', 'start': 159.105, 'duration': 10.229}, {'end': 171.156, 'text': 'Weather forecast is another area.', 'start': 169.674, 'duration': 1.482}, {'end': 176.941, 'text': 'And in fact, Netflix has actually come up with a very interesting use case.', 'start': 171.456, 'duration': 5.485}, {'end': 180.903, 'text': 'You all must be aware of the House of Cards show on Netflix.', 'start': 177.341, 'duration': 3.562}, {'end': 185.105, 'text': 'So they did an analysis on their customer behavior.', 'start': 181.203, 'duration': 3.902}], 'summary': 'Machine learning is used in facial recognition, voice recognition, healthcare, and weather forecast, with real-world applications in security, crime-solving, and medical diagnostics.', 'duration': 99.132, 'max_score': 85.973, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg85973.jpg'}], 'start': 3.697, 'title': 'Machine learning applications', 'summary': 'Covers machine learning basics and its real-world applications, discussing popular algorithms like linear regression, logistic regression, decision tree, random forest, and k nearest neighbors, along with examples in python. it also highlights the widespread use of machine learning in industries such as security, healthcare, and entertainment, with specific examples like facial and voice recognition, and customer behavior analysis for content creation on netflix.', 'chapters': [{'end': 62.839, 'start': 3.697, 'title': 'Introduction to machine learning algorithms', 'summary': 'Discusses the basics of machine learning, including popular algorithms such as linear regression and logistic regression, and illustrates the application of machine learning in facial recognition used by snapchat.', 'duration': 59.142, 'highlights': ['Snapchat uses facial recognition, a machine learning technique, to apply filters to photos by detecting facial features like nose and eyes, and applying filters accordingly.', 'The session covers the definition of machine learning, popular algorithms like linear regression and logistic regression, and real-life examples of their applications.', 'The explanation of how Snapchat uses machine learning for facial recognition to accurately apply filters to photos, even when multiple faces are present.']}, {'end': 111.625, 'start': 63.199, 'title': 'Real world applications of machine learning', 'summary': 'Discusses real world applications of machine learning, including types of machine learning algorithms and hands-on examples with python code, covering linear regression, logistic regression, decision tree, random forest, and k nearest neighbors.', 'duration': 48.426, 'highlights': ['Real world applications of machine learning are discussed.', 'The types of machine learning algorithms are explained.', 'Hands-on examples with python code are provided, covering linear regression, logistic regression, decision tree, random forest, and k nearest neighbors.']}, {'end': 206.496, 'start': 112.105, 'title': 'Applications of machine learning', 'summary': 'Discusses the widespread use of machine learning in various industries including security, healthcare, and entertainment, such as facial recognition for security, voice recognition like siri, and analyzing customer behavior for content creation on netflix.', 'duration': 94.391, 'highlights': ['Facial recognition is being used for security and crime-solving, with increasing popularity and adoption in various areas. Facial recognition is widely used for security and crime-solving, gaining popularity in various areas.', 'Voice recognition, exemplified by Siri, is becoming increasingly common, showcasing the widespread use of machine learning in consumer applications. Voice recognition, as seen with Siri, demonstrates the widespread adoption of machine learning in consumer applications.', 'Machine learning is significantly impacting the healthcare industry, particularly in aiding diagnostic analysis of images like x-rays and MRIs, addressing the shortage of doctors. Machine learning is making a significant impact on the healthcare industry, particularly in aiding diagnostic analysis of images like x-rays and MRIs to address the shortage of doctors.', "Netflix's analysis of customer behavior using data from 30 million customers, exemplifies the use of machine learning in content creation and audience engagement. Netflix's analysis of customer behavior using data from 30 million customers showcases the use of machine learning in content creation and audience engagement."]}], 'duration': 202.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3697.jpg', 'highlights': ["Netflix's analysis of customer behavior using data from 30 million customers showcases the use of machine learning in content creation and audience engagement.", 'Machine learning is significantly impacting the healthcare industry, aiding diagnostic analysis of images like x-rays and MRIs to address the shortage of doctors.', 'Voice recognition, as seen with Siri, demonstrates the widespread adoption of machine learning in consumer applications.', 'Facial recognition is being used for security and crime-solving, gaining popularity in various areas.', 'The session covers the definition of machine learning, popular algorithms like linear regression and logistic regression, and real-life examples of their applications.', 'Hands-on examples with python code are provided, covering linear regression, logistic regression, decision tree, random forest, and k nearest neighbors.', 'Snapchat uses facial recognition, a machine learning technique, to apply filters to photos by detecting facial features like nose and eyes, and applying filters accordingly.', 'The explanation of how Snapchat uses machine learning for facial recognition to accurately apply filters to photos, even when multiple faces are present.']}, {'end': 586.482, 'segs': [{'end': 306.32, 'src': 'embed', 'start': 277.797, 'weight': 0, 'content': [{'end': 280.799, 'text': "so let's take you through step-by-step process of machine learning.", 'start': 277.797, 'duration': 3.002}, {'end': 284.462, 'text': 'the first step in machine learning is data gathering.', 'start': 280.799, 'duration': 3.663}, {'end': 290.207, 'text': 'machine learning needs a lot of past data especially we will see a little later supervised learning.', 'start': 284.462, 'duration': 5.745}, {'end': 294.47, 'text': "we will see what that is in a little while, but that's the most common form of learning.", 'start': 290.207, 'duration': 4.263}, {'end': 296.772, 'text': 'so the first step there is data gathering.', 'start': 294.47, 'duration': 2.302}, {'end': 299.974, 'text': 'you need to have sufficient historical data.', 'start': 296.772, 'duration': 3.202}, {'end': 306.32, 'text': 'then the second step is pre-processing of this data so that this can be used for for the machine learning process.', 'start': 299.974, 'duration': 6.346}], 'summary': 'Machine learning process involves data gathering and preprocessing for supervised learning.', 'duration': 28.523, 'max_score': 277.797, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg277797.jpg'}, {'end': 404.308, 'src': 'embed', 'start': 377.696, 'weight': 1, 'content': [{'end': 386.878, 'text': 'Machine learning algorithms are broadly classified into three types the supervised learning, unsupervised learning and reinforcement learning.', 'start': 377.696, 'duration': 9.182}, {'end': 395.36, 'text': 'Supervised learning in turn consists of techniques like regression and classification and unsupervised learning.', 'start': 387.138, 'duration': 8.222}, {'end': 404.308, 'text': 'we use techniques like association and clustering, and reinforcement learning is a recently developed technique and it is very popular in gaming.', 'start': 395.36, 'duration': 8.948}], 'summary': 'Machine learning has 3 types: supervised, unsupervised, and reinforcement learning; including techniques like regression, classification, association, and clustering.', 'duration': 26.612, 'max_score': 377.696, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg377696.jpg'}, {'end': 549.573, 'src': 'embed', 'start': 524.155, 'weight': 2, 'content': [{'end': 532.882, 'text': "In case of unsupervised learning, we have input data, but we don't have the labels or what the output is supposed to be.", 'start': 524.155, 'duration': 8.727}, {'end': 540.608, 'text': 'So that is when we use unsupervised learning techniques like clustering and association and we try to analyze the data.', 'start': 533.222, 'duration': 7.386}, {'end': 549.573, 'text': 'In case of reinforcement learning, it allows the agent to automatically determine the ideal behavior within a specific context.', 'start': 540.988, 'duration': 8.585}], 'summary': 'Unsupervised learning uses clustering and association to analyze input data without labels. reinforcement learning enables agents to determine ideal behavior within specific contexts.', 'duration': 25.418, 'max_score': 524.155, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg524155.jpg'}], 'start': 206.496, 'title': 'Basics of machine learning', 'summary': 'Covers the basic concepts of machine learning, including data gathering, preprocessing, model training, testing, and deployment, along with discussions on supervised, unsupervised, and reinforcement learning algorithms.', 'chapters': [{'end': 586.482, 'start': 206.496, 'title': 'Machine learning: basics and applications', 'summary': 'Explains the basics of machine learning, including the process of data gathering, preprocessing, model training, testing, and deployment, as well as the different types of machine learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning.', 'duration': 379.986, 'highlights': ['Machine learning process involves data gathering, preprocessing, model training, testing, and deployment, aiming to achieve as accurate predictions as possible. The process of machine learning involves gathering historical data, preprocessing it, choosing a model, training and testing the model, and subsequently deploying it to make predictions, aiming for maximum accuracy.', 'Machine learning algorithms are broadly classified into supervised learning, unsupervised learning, and reinforcement learning, each with specific techniques and applications. Machine learning algorithms are categorized into three types: supervised learning, unsupervised learning, and reinforcement learning, each with distinct techniques and applications, such as regression, classification, association, and clustering.', 'Supervised learning is used with historical labeled data to predict specific target values, while unsupervised learning is applied when labeled data is unavailable, and reinforcement learning involves the agent learning from scratch to maximize performance within a specific context. Supervised learning utilizes labeled historical data to predict specific target values, unsupervised learning is used when labeled data is unavailable, and reinforcement learning involves the agent learning from scratch to maximize performance within a specific context.']}], 'duration': 379.986, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg206496.jpg', 'highlights': ['Machine learning process involves data gathering, preprocessing, model training, testing, and deployment, aiming to achieve as accurate predictions as possible.', 'Machine learning algorithms are broadly classified into supervised learning, unsupervised learning, and reinforcement learning, each with specific techniques and applications.', 'Supervised learning is used with historical labeled data to predict specific target values, while unsupervised learning is applied when labeled data is unavailable, and reinforcement learning involves the agent learning from scratch to maximize performance within a specific context.']}, {'end': 1006.652, 'segs': [{'end': 628.923, 'src': 'embed', 'start': 604.069, 'weight': 0, 'content': [{'end': 613.832, 'text': 'linear regression a little history about linear regression sir francis galton is credited with the discovery of the linear regression model.', 'start': 604.069, 'duration': 9.763}, {'end': 623.517, 'text': "so what he did was he started studying the heights of father and son to predict the sun's height or the child's height even before he or she is born.", 'start': 613.832, 'duration': 9.685}, {'end': 628.923, 'text': 'So he collected enough data of the heights of father and the respective sons.', 'start': 623.777, 'duration': 5.146}], 'summary': "Linear regression was discovered by sir francis galton to predict child's height using father's height data.", 'duration': 24.854, 'max_score': 604.069, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg604069.jpg'}, {'end': 686.709, 'src': 'embed', 'start': 656.632, 'weight': 1, 'content': [{'end': 663.419, 'text': 'So that was the very beginning or very initial phase of linear regression algorithms.', 'start': 656.632, 'duration': 6.787}, {'end': 665.04, 'text': 'so that was a little bit of history.', 'start': 663.579, 'duration': 1.461}, {'end': 666.86, 'text': 'but what is linear regression?', 'start': 665.04, 'duration': 1.82}, {'end': 678.625, 'text': 'so linear regression is a way of modeling a linear model, creating a linear model to find the relationship between one or more independent variables,', 'start': 666.86, 'duration': 11.765}, {'end': 686.709, 'text': 'denoted by x, and a dependent variable, which is also known as the target and denoted as y.', 'start': 678.625, 'duration': 8.084}], 'summary': 'Linear regression is a way of modeling a linear model to find the relationship between independent variables denoted by x and a dependent variable denoted as y.', 'duration': 30.077, 'max_score': 656.632, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg656632.jpg'}, {'end': 766.053, 'src': 'embed', 'start': 737.958, 'weight': 2, 'content': [{'end': 741.22, 'text': 'So, there is only one x, but that is simple linear regression.', 'start': 737.958, 'duration': 3.262}, {'end': 744.082, 'text': "So, let's see how this is actually done.", 'start': 741.34, 'duration': 2.742}, {'end': 754.427, 'text': 'So, linear regression is all about finding the best fit And the way it is done is in a recursive manner.', 'start': 744.382, 'duration': 10.045}, {'end': 762.131, 'text': 'So first, a random line is drawn and the distance is calculated from this line of all the points, as you can see in this example.', 'start': 754.647, 'duration': 7.484}, {'end': 766.053, 'text': 'And that distance is known as the error.', 'start': 762.751, 'duration': 3.302}], 'summary': 'Linear regression finds best-fit line by minimizing errors from data points.', 'duration': 28.095, 'max_score': 737.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg737958.jpg'}, {'end': 982.008, 'src': 'heatmap', 'start': 893.774, 'weight': 3, 'content': [{'end': 902.036, 'text': "you don't have to go into details And then we import the dataset and then we visualize the data just to get a quick idea about how the data is looking.", 'start': 893.774, 'duration': 8.262}, {'end': 906.797, 'text': 'And then we split the data into training and test datasets.', 'start': 902.296, 'duration': 4.501}, {'end': 910.718, 'text': 'This is a common procedure in machine learning process, any machine learning process.', 'start': 907.017, 'duration': 3.701}, {'end': 915.719, 'text': 'And the overall training and test process we do with two different datasets.', 'start': 911.198, 'duration': 4.521}, {'end': 916.879, 'text': "So that's what we're doing here.", 'start': 915.779, 'duration': 1.1}, {'end': 922.98, 'text': 'And then we build or train our model, the linear regression model, and then we do the testing.', 'start': 917.119, 'duration': 5.861}, {'end': 927.661, 'text': 'we find out what is the errors and visualize our results.', 'start': 923.479, 'duration': 4.182}, {'end': 930.063, 'text': 'and this is how the test results look.', 'start': 927.661, 'duration': 2.402}, {'end': 933.224, 'text': 'and this is how the training result looks all right.', 'start': 930.063, 'duration': 3.161}, {'end': 935.245, 'text': 'and then we calculate the residuals.', 'start': 933.224, 'duration': 2.021}, {'end': 937.607, 'text': 'residuals are nothing but the errors.', 'start': 935.245, 'duration': 2.362}, {'end': 940.788, 'text': 'there are a couple of ways of measuring the accuracy.', 'start': 937.607, 'duration': 3.181}, {'end': 949.873, 'text': 'the root mean square error is the most common one rmsc and in this case we got root mean square error of 58, which is pretty good,', 'start': 940.788, 'duration': 9.085}, {'end': 951.774, 'text': "and that's our best fit.", 'start': 949.873, 'duration': 1.901}, {'end': 952.094, 'text': 'all right.', 'start': 951.774, 'duration': 0.32}, {'end': 958.216, 'text': "so now let's go into jupyter notebook and take a look at by running it live okay.", 'start': 952.094, 'duration': 6.122}, {'end': 968.88, 'text': 'so this is our code for the linear regression demo and this is how the jupyter notebook looks and this code in the jupyter notebook looks.', 'start': 958.216, 'duration': 10.664}, {'end': 973.482, 'text': "i will walk you through the code pretty much line by line and let's see how this works.", 'start': 968.88, 'duration': 4.602}, {'end': 974.382, 'text': 'the linear regression.', 'start': 973.482, 'duration': 0.9}, {'end': 979.106, 'text': 'So the first part is pretty much a standard template in pretty much all our code.', 'start': 974.482, 'duration': 4.624}, {'end': 982.008, 'text': 'We will see this is importing the required library.', 'start': 979.146, 'duration': 2.862}], 'summary': 'Machine learning process: data import, visualization, training, test datasets, linear regression model, testing, root mean square error of 58, and live demo in jupyter notebook.', 'duration': 44.401, 'max_score': 893.774, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg893774.jpg'}], 'start': 586.522, 'title': 'Linear regression basics and demo', 'summary': 'Provides an overview of linear regression, including its history, the process of finding the best fit line, and its application in determining salary based on years of experience. it also covers the process of importing libraries, dataset visualization, data splitting, model training, testing, and error calculation, achieving a root mean square error of 58, followed by a live demonstration in a jupyter notebook.', 'chapters': [{'end': 878.687, 'start': 586.522, 'title': 'Linear regression basics', 'summary': 'Provides an overview of linear regression, its history, the process of finding the best fit line, and its application in determining salary based on years of experience.', 'duration': 292.165, 'highlights': ["Linear regression was discovered by Sir Francis Galton to predict a child's height based on the father's height using the mean square error. Sir Francis Galton discovered linear regression to predict a child's height based on the father's height using the mean square error.", 'The process of linear regression involves creating a linear model to find the relationship between independent (x) and dependent (y) variables, represented by a best fit line with minimal distance from the data points. Linear regression involves creating a linear model to find the relationship between independent (x) and dependent (y) variables, represented by a best fit line with minimal distance from the data points.', 'The iterative process of linear regression involves drawing a random line, calculating the distance of all points from the line, and adjusting the line to minimize the sum of squared errors, resulting in the best fit regression line. The iterative process of linear regression involves drawing a random line, calculating the distance of all points from the line, and adjusting the line to minimize the sum of squared errors, resulting in the best fit regression line.', 'The application of linear regression in determining salary based on years of experience will be demonstrated using Python in Jupyter Notebook. The application of linear regression in determining salary based on years of experience will be demonstrated using Python in Jupyter Notebook.']}, {'end': 1006.652, 'start': 878.687, 'title': 'Linear regression demo', 'summary': 'Covers the process of importing libraries, dataset visualization, data splitting, model training, testing, and error calculation, achieving a root mean square error of 58, followed by a live demonstration in a jupyter notebook.', 'duration': 127.965, 'highlights': ['The chapter covers the process of importing libraries, dataset visualization, data splitting, model training, testing, and error calculation, achieving a root mean square error of 58. The process involves importing libraries, visualizing the dataset, splitting it into training and test datasets, training a linear regression model, testing, and calculating the root mean square error of 58.', 'The live demonstration in a Jupyter notebook is then conducted to walk through the code for the linear regression demo. A live demonstration in a Jupyter notebook is conducted to walk through the code for the linear regression demo, explaining each line and showcasing the process.']}], 'duration': 420.13, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg586522.jpg', 'highlights': ["Linear regression was discovered by Sir Francis Galton to predict a child's height based on the father's height using the mean square error.", 'The process of linear regression involves creating a linear model to find the relationship between independent (x) and dependent (y) variables, represented by a best fit line with minimal distance from the data points.', 'The iterative process of linear regression involves drawing a random line, calculating the distance of all points from the line, and adjusting the line to minimize the sum of squared errors, resulting in the best fit regression line.', 'The application of linear regression in determining salary based on years of experience will be demonstrated using Python in Jupyter Notebook.', 'The chapter covers the process of importing libraries, dataset visualization, data splitting, model training, testing, and error calculation, achieving a root mean square error of 58.', 'The live demonstration in a Jupyter notebook is then conducted to walk through the code for the linear regression demo.']}, {'end': 1625.333, 'segs': [{'end': 1058.982, 'src': 'embed', 'start': 1006.652, 'weight': 0, 'content': [{'end': 1016.262, 'text': 'so in this example, what we are trying to do the use case in this particular example is we have some historical value of salary data.', 'start': 1006.652, 'duration': 9.61}, {'end': 1023.969, 'text': 'And now we want to build a model so that we can predict the salary for new employee, a person who is joining new.', 'start': 1016.543, 'duration': 7.426}, {'end': 1029.973, 'text': 'And we will use the same the characteristics that were available to us or the features that were available to us.', 'start': 1024.309, 'duration': 5.664}, {'end': 1033.915, 'text': 'And we will try to predict what will be this salary of this new person.', 'start': 1030.113, 'duration': 3.802}, {'end': 1036.078, 'text': "Okay, so that's the kind of the use case.", 'start': 1034.357, 'duration': 1.721}, {'end': 1043.587, 'text': "So let's go ahead and load the data and let me introduce a cell and see how the data looks.", 'start': 1036.198, 'duration': 7.389}, {'end': 1046.611, 'text': 'So salary underscore data.', 'start': 1043.627, 'duration': 2.984}, {'end': 1055.921, 'text': "So we have basically two features, right? So it's pretty much like a simple linear regression we are trying to do.", 'start': 1049.198, 'duration': 6.723}, {'end': 1058.982, 'text': 'So we have years of experience and salary.', 'start': 1056.081, 'duration': 2.901}], 'summary': 'Building a model to predict salary using historical salary data and features like years of experience.', 'duration': 52.33, 'max_score': 1006.652, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1006652.jpg'}, {'end': 1176.694, 'src': 'embed', 'start': 1151.86, 'weight': 1, 'content': [{'end': 1159.727, 'text': 'So what this shows is the count, how many records are there with a given experience and things like that.', 'start': 1151.86, 'duration': 7.867}, {'end': 1163.47, 'text': 'So this is another way of visualizing the data.', 'start': 1160.027, 'duration': 3.443}, {'end': 1168.51, 'text': 'This is a third view.', 'start': 1166.769, 'duration': 1.741}, {'end': 1170.711, 'text': 'And this is one more view.', 'start': 1169.27, 'duration': 1.441}, {'end': 1173.472, 'text': 'And then we can do a quick heat map.', 'start': 1171.291, 'duration': 2.181}, {'end': 1176.694, 'text': "So there are there's only one or actually there are only two variables.", 'start': 1173.672, 'duration': 3.022}], 'summary': 'Visualizing data with count and two variables using different views.', 'duration': 24.834, 'max_score': 1151.86, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1151860.jpg'}, {'end': 1226.512, 'src': 'embed', 'start': 1196.287, 'weight': 3, 'content': [{'end': 1205.354, 'text': 'So once we are done with that, this is the most important part of our demo here, which is basically this is the beginning of our training process.', 'start': 1196.287, 'duration': 9.067}, {'end': 1216.483, 'text': 'So the first thing before we start the model building and model training process is to split the data into training and test data sets.', 'start': 1205.995, 'duration': 10.488}, {'end': 1226.512, 'text': 'Now, whenever we do any machine learning exercise, especially supervised learning, we never use the entire label data for training purpose.', 'start': 1216.864, 'duration': 9.648}], 'summary': 'Training data split is crucial for supervised learning.', 'duration': 30.225, 'max_score': 1196.287, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1196287.jpg'}, {'end': 1348.939, 'src': 'embed', 'start': 1311.872, 'weight': 4, 'content': [{'end': 1313.834, 'text': 'That means it is 33%, right? 33.33%.', 'start': 1311.872, 'duration': 1.962}, {'end': 1318.92, 'text': 'So, one third of the data you want to set aside for test.', 'start': 1313.834, 'duration': 5.086}, {'end': 1327.827, 'text': 'Now, there are no hard and fast rules as to So, what should be the split of test and training data set is a matter of individual preferences.', 'start': 1319.2, 'duration': 8.627}, {'end': 1334.491, 'text': 'Some people would like to have 5050, some people would prefer 8020, and so on and so forth.', 'start': 1327.907, 'duration': 6.584}, {'end': 1338.093, 'text': 'So it is completely up to the individuals to decide on that.', 'start': 1334.571, 'duration': 3.522}, {'end': 1339.494, 'text': 'So, in this particular case,', 'start': 1338.173, 'duration': 1.321}, {'end': 1348.939, 'text': 'what we are doing is we are setting aside one third of the data set for testing purpose and two thirds of the data set for training purpose.', 'start': 1339.494, 'duration': 9.445}], 'summary': 'Data split for testing is 33% and for training is 67%.', 'duration': 37.067, 'max_score': 1311.872, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1311872.jpg'}, {'end': 1389.212, 'src': 'embed', 'start': 1358.743, 'weight': 5, 'content': [{'end': 1363.085, 'text': 'So we create an instance of the linear regression model and give it a name like LR.', 'start': 1358.743, 'duration': 4.342}, {'end': 1367.947, 'text': 'And then we call the fit method of the linear regression model.', 'start': 1363.805, 'duration': 4.142}, {'end': 1372.108, 'text': 'Now, this fit method is common across all the algorithms.', 'start': 1367.967, 'duration': 4.141}, {'end': 1377.63, 'text': 'So any algorithm you use, if you want to start the training process, you call the fit method.', 'start': 1372.148, 'duration': 5.482}, {'end': 1381.551, 'text': 'OK, and then we pass the training data set.', 'start': 1378.05, 'duration': 3.501}, {'end': 1389.212, 'text': 'Now training data set we send the predictor as I was saying or the independent variable and also the dependent variable.', 'start': 1381.631, 'duration': 7.581}], 'summary': 'Creating and training a linear regression model using training data set and fit method.', 'duration': 30.469, 'max_score': 1358.743, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1358743.jpg'}, {'end': 1553.491, 'src': 'embed', 'start': 1525.098, 'weight': 6, 'content': [{'end': 1527.339, 'text': 'Okay, so this is for the training part.', 'start': 1525.098, 'duration': 2.241}, {'end': 1530.021, 'text': 'Now we do the same for our test as well.', 'start': 1527.399, 'duration': 2.622}, {'end': 1534.063, 'text': 'And see how it is doing or how it looks here.', 'start': 1530.581, 'duration': 3.482}, {'end': 1540.287, 'text': 'Also, it looks pretty good because the line passes pretty much in the middle of the overall data set.', 'start': 1534.143, 'duration': 6.144}, {'end': 1541.668, 'text': "That's what we are trying to do here.", 'start': 1540.347, 'duration': 1.321}, {'end': 1542.508, 'text': 'All right.', 'start': 1542.228, 'duration': 0.28}, {'end': 1549.05, 'text': 'And then how do we measure the accuracy? So these residuals are nothing but the errors.', 'start': 1542.788, 'duration': 6.262}, {'end': 1553.491, 'text': 'The term residuals is nothing but the errors we have seen in the slides as well.', 'start': 1549.23, 'duration': 4.261}], 'summary': 'Training and test data show good accuracy with residuals as errors.', 'duration': 28.393, 'max_score': 1525.098, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1525098.jpg'}], 'start': 1006.652, 'title': 'Building a salary prediction model', 'summary': 'Discusses building a model to predict the salary for new employees based on historical salary data using years of experience as a predictor, including visualization techniques like bar plots and heat maps, and covers the importance of data splitting in machine learning, using a 1:3 test size split, creating an instance of linear regression, and measuring model accuracy through residuals and visualization.', 'chapters': [{'end': 1196.186, 'start': 1006.652, 'title': 'Salary prediction model', 'summary': 'Discusses building a model to predict the salary for new employees based on historical salary data using years of experience as a predictor, and includes visualization techniques like bar plots and heat maps for exploratory analysis.', 'duration': 189.534, 'highlights': ['Building a model to predict the salary for new employees based on historical salary data using years of experience as a predictor The use case involves building a model to predict the salary for new employees based on historical salary data, using years of experience as a predictor.', 'Visualization techniques like bar plots and heat maps for exploratory analysis The chapter includes visualization techniques like bar plots and heat maps for exploratory analysis of the data, to understand the correlation and visualization of variables.', 'Introduction of two features for simple linear regression: years of experience and salary The data includes two features for simple linear regression: years of experience and salary, where years of experience is used as a predictor to predict the salary of new employees.']}, {'end': 1625.333, 'start': 1196.287, 'title': 'Model training process and data splitting', 'summary': 'Covers the importance of splitting data into training and test sets in machine learning, using a 1:3 test size split, creating an instance of linear regression, and measuring model accuracy through residuals and visualization.', 'duration': 429.046, 'highlights': ["The chapter covers the importance of splitting data into training and test sets in machine learning. Splitting data into training and test sets is crucial in machine learning to accurately evaluate the model's performance.", 'Using a 1:3 test size split for data splitting. A 1:3 test size split is used to allocate one third of the dataset for testing purposes and two thirds for training.', 'Creating an instance of linear regression and using the fit method for training. An instance of linear regression model is created and the fit method is used to train the model with the training data set.', 'Measuring model accuracy through residuals and visualization. Model accuracy is measured through residuals such as mean square error, mean absolute error, and root mean square error, and visualization of the training and test set results.']}], 'duration': 618.681, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1006652.jpg', 'highlights': ['Building a model to predict the salary for new employees based on historical salary data using years of experience as a predictor', 'Visualization techniques like bar plots and heat maps for exploratory analysis', 'Introduction of two features for simple linear regression: years of experience and salary', 'The chapter covers the importance of splitting data into training and test sets in machine learning', 'Using a 1:3 test size split for data splitting', 'Creating an instance of linear regression and using the fit method for training', 'Measuring model accuracy through residuals and visualization']}, {'end': 2257.853, 'segs': [{'end': 1648.424, 'src': 'embed', 'start': 1625.333, 'weight': 2, 'content': [{'end': 1632.556, 'text': "let's assume this goes to 60,000, which means that one of the data points is very accurately predicted by our model.", 'start': 1625.333, 'duration': 7.223}, {'end': 1635.017, 'text': 'but there are two of them which are off.', 'start': 1632.556, 'duration': 2.461}, {'end': 1637.679, 'text': 'So there is an error for these two values.', 'start': 1635.418, 'duration': 2.261}, {'end': 1642.601, 'text': 'And what is that error? The error is nothing but the distance of this point from this line.', 'start': 1638.139, 'duration': 4.462}, {'end': 1648.424, 'text': 'right, the distance of this point of each of these data points from the from the line is the error.', 'start': 1643.021, 'duration': 5.403}], 'summary': 'Model predicts 60,000 accurately, but two data points have errors due to distance from the line.', 'duration': 23.091, 'max_score': 1625.333, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1625333.jpg'}, {'end': 1718.207, 'src': 'embed', 'start': 1692.098, 'weight': 0, 'content': [{'end': 1703.608, 'text': 'And these values, the root mean square error, the mean mean squared error and the mean absolute error, the lower these values are the better.', 'start': 1692.098, 'duration': 11.51}, {'end': 1708.874, 'text': 'So the accuracy is higher if these values are lower.', 'start': 1704.048, 'duration': 4.826}, {'end': 1711.918, 'text': 'So in a way it is inversely proportional.', 'start': 1708.894, 'duration': 3.024}, {'end': 1718.207, 'text': "OK, so that's the way we measure the accuracy of our linear regression model.", 'start': 1712.559, 'duration': 5.648}], 'summary': 'Lower root mean square error, mean squared error, and mean absolute error indicate higher accuracy in linear regression.', 'duration': 26.109, 'max_score': 1692.098, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1692098.jpg'}, {'end': 1772.083, 'src': 'embed', 'start': 1742.436, 'weight': 3, 'content': [{'end': 1748.957, 'text': 'but keep in mind, this is not used for regression, but this algorithm is used for classification.', 'start': 1742.436, 'duration': 6.521}, {'end': 1753.538, 'text': 'Now, many people get confused by this name, so you need to be aware of it.', 'start': 1749.117, 'duration': 4.421}, {'end': 1760.079, 'text': 'Linear regression is used to solve regression problems, where we are trying to predict a value,', 'start': 1753.818, 'duration': 6.261}, {'end': 1765.621, 'text': 'whereas logistic regression is used to solve a classification problem.', 'start': 1760.079, 'duration': 5.542}, {'end': 1772.083, 'text': 'so we are trying to find, for example, whether a person will repay the loan or not,', 'start': 1765.621, 'duration': 6.462}], 'summary': 'Logistic regression used for classification, linear for regression problems.', 'duration': 29.647, 'max_score': 1742.436, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1742436.jpg'}, {'end': 1954.316, 'src': 'embed', 'start': 1924.77, 'weight': 4, 'content': [{'end': 1927.032, 'text': "So that's the way the sigmoid function works.", 'start': 1924.77, 'duration': 2.262}, {'end': 1933.938, 'text': 'This is how the graph looks and there has to be a threshold value, which is like in this case 0.5.', 'start': 1927.392, 'duration': 6.546}, {'end': 1938.382, 'text': 'So if the value is greater than 0.5, we consider the output as 1.', 'start': 1933.938, 'duration': 4.444}, {'end': 1944.188, 'text': 'Whereas if the value is less than 0.5, we consider the value as 0.', 'start': 1938.382, 'duration': 5.806}, {'end': 1946.19, 'text': 'because, remember, let me go back.', 'start': 1944.188, 'duration': 2.002}, {'end': 1950.893, 'text': "in this case it doesn't exactly give us a 1 or a 0..", 'start': 1946.19, 'duration': 4.703}, {'end': 1952.214, 'text': 'Okay, so we need to keep that in mind.', 'start': 1950.893, 'duration': 1.321}, {'end': 1954.316, 'text': "It doesn't give us exactly a 1 or a 0.", 'start': 1952.334, 'duration': 1.982}], 'summary': 'The sigmoid function has a threshold value of 0.5, producing outputs of 1 or 0.', 'duration': 29.546, 'max_score': 1924.77, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1924770.jpg'}, {'end': 2257.853, 'src': 'embed', 'start': 2230.315, 'weight': 5, 'content': [{'end': 2232.957, 'text': 'So 117 of them have been correctly predicted, which gives us an accuracy of 87%.', 'start': 2230.315, 'duration': 2.642}, {'end': 2235.118, 'text': 'So out of 134, 117 have been correctly predicted.', 'start': 2232.957, 'duration': 2.161}, {'end': 2236.739, 'text': 'And the 6 plus 11, 17 of them have been incorrectly.', 'start': 2235.138, 'duration': 1.601}, {'end': 2238.3, 'text': 'So this is 6 plus 11, 17 of them have been misclassified.', 'start': 2236.759, 'duration': 1.541}, {'end': 2238.821, 'text': 'So which is about 0.13%.', 'start': 2238.32, 'duration': 0.501}, {'end': 2257.853, 'text': 'So we have an accuracy of 87%.', 'start': 2238.821, 'duration': 19.032}], 'summary': '87% accuracy with 117 correct predictions out of 134.', 'duration': 27.538, 'max_score': 2230.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2230315.jpg'}], 'start': 1625.333, 'title': 'Regression models', 'summary': 'Discusses measuring accuracy of linear regression model using root mean square error, mean squared error, and mean absolute error, and implementing logistic regression for classification problems with an achieved accuracy of 87%.', 'chapters': [{'end': 1718.207, 'start': 1625.333, 'title': 'Linear regression model accuracy', 'summary': 'Explains the calculation of root mean square error, mean squared error, and mean absolute error to measure the accuracy of the linear regression model, with lower values indicating higher accuracy.', 'duration': 92.874, 'highlights': ['The root mean square error, mean squared error, and mean absolute error are calculated to measure the accuracy of the linear regression model, with lower values indicating higher accuracy.', 'The error for specific data points in the model is determined by the distance of each point from the line, and the mean square error is calculated by taking the square of these distances.', 'The accuracy of the linear regression model is inversely proportional to the values of root mean square error, mean squared error, and mean absolute error.']}, {'end': 2257.853, 'start': 1718.547, 'title': 'Understanding logistic regression', 'summary': 'Covers logistic regression, explaining its application in classification problems, the sigmoid curve for probability calculation, and logistic regression implementation using python, achieving an accuracy of 87%.', 'duration': 539.306, 'highlights': ['Logistic regression is used for classification problems, such as predicting loan repayment or image classification, with an accuracy of 87% demonstrated in the Python implementation. Logistic regression is employed for solving classification problems, like predicting loan repayment or image classification, with an accuracy of 87% demonstrated in the Python implementation.', 'The use of the sigmoid curve in logistic regression ensures probability calculation between 0 and 1, with the threshold value of 0.5 for decision making. The sigmoid curve in logistic regression ensures probability calculation between 0 and 1, with the threshold value of 0.5 for decision making.', "The Python implementation of logistic regression achieved an accuracy of 87% using a confusion matrix to assess the model's performance. The Python implementation of logistic regression achieved an accuracy of 87% using a confusion matrix to assess the model's performance."]}], 'duration': 632.52, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg1625333.jpg', 'highlights': ['The root mean square error, mean squared error, and mean absolute error are calculated to measure the accuracy of the linear regression model, with lower values indicating higher accuracy.', 'The accuracy of the linear regression model is inversely proportional to the values of root mean square error, mean squared error, and mean absolute error.', 'The error for specific data points in the model is determined by the distance of each point from the line, and the mean square error is calculated by taking the square of these distances.', 'Logistic regression is used for classification problems, such as predicting loan repayment or image classification, with an accuracy of 87% demonstrated in the Python implementation.', 'The use of the sigmoid curve in logistic regression ensures probability calculation between 0 and 1, with the threshold value of 0.5 for decision making.', "The Python implementation of logistic regression achieved an accuracy of 87% using a confusion matrix to assess the model's performance."]}, {'end': 2550.851, 'segs': [{'end': 2307.212, 'src': 'embed', 'start': 2279.716, 'weight': 0, 'content': [{'end': 2282.737, 'text': 'All right, so this is the demo of logistic regression.', 'start': 2279.716, 'duration': 3.021}, {'end': 2295.181, 'text': "And here what we're doing is we've taken an example of a data set and a scenario where we will predict whether a person is going to buy an SUV or not.", 'start': 2283.397, 'duration': 11.784}, {'end': 2297.582, 'text': 'And we will use logistic regression for this.', 'start': 2295.741, 'duration': 1.841}, {'end': 2304.61, 'text': "the parameters we will take are, for example, the person's age, his salary and a few other parameters.", 'start': 2298.104, 'duration': 6.506}, {'end': 2307.212, 'text': 'we will see very quickly what those are okay.', 'start': 2304.61, 'duration': 2.602}], 'summary': 'Demo of logistic regression to predict suv purchase based on age and salary.', 'duration': 27.496, 'max_score': 2279.716, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2279716.jpg'}, {'end': 2400.13, 'src': 'embed', 'start': 2375.124, 'weight': 1, 'content': [{'end': 2380.645, 'text': 'And the other way of also looking at it is more from a mathematical perspective.', 'start': 2375.124, 'duration': 5.521}, {'end': 2383.046, 'text': 'These are our independent variables.', 'start': 2380.885, 'duration': 2.161}, {'end': 2389.447, 'text': 'Gender, age, and estimated salary are our independent variables, and purchased is our dependent variables.', 'start': 2383.146, 'duration': 6.301}, {'end': 2397.009, 'text': 'So, in our equation like y is equal to something, something, 1x plus m2x and so on, this is our y.', 'start': 2389.927, 'duration': 7.082}, {'end': 2397.769, 'text': 'Okay All right.', 'start': 2397.009, 'duration': 0.76}, {'end': 2400.13, 'text': "So, now let's move forward.", 'start': 2397.829, 'duration': 2.301}], 'summary': 'Analysis of independent variables (gender, age, salary) and its relation to purchase behavior.', 'duration': 25.006, 'max_score': 2375.124, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2375124.jpg'}, {'end': 2496.413, 'src': 'embed', 'start': 2466.661, 'weight': 2, 'content': [{'end': 2473.646, 'text': "So, in terms of visualization, let's visualize the data and perform a little bit of what is known as exploratory analysis.", 'start': 2466.661, 'duration': 6.985}, {'end': 2476.589, 'text': 'right?. as a data scientist, whenever you get new data,', 'start': 2473.646, 'duration': 2.943}, {'end': 2484.839, 'text': 'you just play around and see how the data is looking before you actually launch into the actual training or the modeling part of it.', 'start': 2476.589, 'duration': 8.25}, {'end': 2488.784, 'text': "so let's run a small heat map and see how the data looks.", 'start': 2484.839, 'duration': 3.945}, {'end': 2496.413, 'text': 'so we have just passed the entire data set here and this is how the heat map looks, how how they are related,', 'start': 2488.784, 'duration': 7.629}], 'summary': 'Perform exploratory analysis using visualization before training or modeling, as a data scientist.', 'duration': 29.752, 'max_score': 2466.661, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2466661.jpg'}, {'end': 2561.06, 'src': 'embed', 'start': 2531.134, 'weight': 3, 'content': [{'end': 2538.04, 'text': 'for example, there is a correlation of 0.6 in this area, which is basically, if you take age and purchased right,', 'start': 2531.134, 'duration': 6.906}, {'end': 2541.163, 'text': 'whether the person has purchased and the age.', 'start': 2538.04, 'duration': 3.123}, {'end': 2544.446, 'text': "so that's that's a quick look at exploring the data.", 'start': 2541.163, 'duration': 3.283}, {'end': 2546.487, 'text': "so that's, uh, all we are doing here here.", 'start': 2544.446, 'duration': 2.041}, {'end': 2550.851, 'text': "here's where the actual, the crux of this code is.", 'start': 2546.487, 'duration': 4.364}, {'end': 2561.06, 'text': "So, from here on, what's what we do is the first step, before we start the training process, is split our data into train and test,", 'start': 2550.931, 'duration': 10.129}], 'summary': 'Correlation of 0.6 found between age and purchases, data split into train and test', 'duration': 29.926, 'max_score': 2531.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2531134.jpg'}], 'start': 2257.853, 'title': 'Logistic regression demo and data visualization', 'summary': 'Includes a logistic regression demo predicting suv purchases and data visualization techniques such as heatmap for correlation analysis.', 'chapters': [{'end': 2328.607, 'start': 2257.853, 'title': 'Logistic regression demo: predicting suv purchase', 'summary': 'Covers a demo of logistic regression using a data set to predict suv purchases based on parameters such as age and salary, while also providing the option for viewers to request the data set for personal use.', 'duration': 70.754, 'highlights': ['Viewers can request the dataset for personal use by commenting under the video.', 'The demo focuses on predicting SUV purchases using logistic regression based on parameters like age and salary.', 'The use of required libraries such as numpy, matplotlib, and pandas for data preparation is demonstrated.']}, {'end': 2550.851, 'start': 2328.607, 'title': 'Data visualization and analysis', 'summary': 'Introduces the process of data visualization and exploratory analysis, including extracting independent and dependent variables, and using a heatmap to measure correlations between features in a data set.', 'duration': 222.244, 'highlights': ['The chapter explains the process of extracting independent and dependent variables from a data set in Python, focusing on gender, age, and estimated salary as independent variables and purchase as the dependent variable.', 'It discusses the use of a heatmap to visually measure correlations between features, where a correlation of 0.6 is observed between age and purchase, indicating a moderate relationship.', 'The chapter emphasizes the importance of exploratory analysis in data science, highlighting the need to understand the data before proceeding with modeling and training.', 'It mentions the use of a heatmap to visualize correlations between features, where a darker color signifies less correlation and a lighter color indicates a higher correlation, providing a visual representation of the relationships within the data.']}], 'duration': 292.998, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2257853.jpg', 'highlights': ['The demo focuses on predicting SUV purchases using logistic regression based on parameters like age and salary.', 'The chapter explains the process of extracting independent and dependent variables from a data set in Python, focusing on gender, age, and estimated salary as independent variables and purchase as the dependent variable.', 'The chapter emphasizes the importance of exploratory analysis in data science, highlighting the need to understand the data before proceeding with modeling and training.', 'It discusses the use of a heatmap to visually measure correlations between features, where a correlation of 0.6 is observed between age and purchase, indicating a moderate relationship.']}, {'end': 3167.994, 'segs': [{'end': 2675.709, 'src': 'embed', 'start': 2641.096, 'weight': 3, 'content': [{'end': 2645.359, 'text': 'Some people prefer 50-50, some people prefer 80-20 and so on and so forth.', 'start': 2641.096, 'duration': 4.263}, {'end': 2650.103, 'text': 'So that is flexible and it could be to some extent individual preferences.', 'start': 2645.86, 'duration': 4.243}, {'end': 2659.129, 'text': "So in our case, we are splitting this data into 75-25, which means 75% of the data we will use for training, 25% we'll use for test.", 'start': 2650.243, 'duration': 8.886}, {'end': 2664.536, 'text': 'So that is what we are specifying here as a parameter we say test underscore size is equal to point to five.', 'start': 2659.39, 'duration': 5.146}, {'end': 2675.709, 'text': 'That means put or keep 25% of the data set aside 25% of the data as test data, and therefore the remaining 70% will be used for training.', 'start': 2664.976, 'duration': 10.733}], 'summary': 'Data is split into 75-25 for training and test, with 25% allocated for testing.', 'duration': 34.613, 'max_score': 2641.096, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2641096.jpg'}, {'end': 2764.001, 'src': 'embed', 'start': 2715.584, 'weight': 0, 'content': [{'end': 2721.669, 'text': 'There is a standard method available or standard class available called standard scaler.', 'start': 2715.584, 'duration': 6.085}, {'end': 2725.452, 'text': 'So we just create an instance of that and pass our data for scaling purpose.', 'start': 2721.709, 'duration': 3.743}, {'end': 2728.194, 'text': "Okay, so let's go ahead and do that now.", 'start': 2725.932, 'duration': 2.262}, {'end': 2732.638, 'text': 'So this is all what we have done is more like a data preparation.', 'start': 2728.654, 'duration': 3.984}, {'end': 2739.423, 'text': 'So we chose what are the parameters, what features we want, we did the feature scaling, and we split the data.', 'start': 2732.678, 'duration': 6.745}, {'end': 2740.624, 'text': 'Now everything is ready.', 'start': 2739.563, 'duration': 1.061}, {'end': 2744.086, 'text': 'Next is to start the actual training process.', 'start': 2740.964, 'duration': 3.122}, {'end': 2746.008, 'text': 'So this is the most crucial part of the code.', 'start': 2744.106, 'duration': 1.902}, {'end': 2749.771, 'text': 'So here, as we said, we will use the logistic regression model.', 'start': 2746.088, 'duration': 3.683}, {'end': 2754.994, 'text': 'So we have to create we create an instance of regression model.', 'start': 2750.191, 'duration': 4.803}, {'end': 2758.177, 'text': 'So I call that as classifier, you can give any name.', 'start': 2755.275, 'duration': 2.902}, {'end': 2764.001, 'text': 'And we just do some kind of initialization random value initialization.', 'start': 2758.997, 'duration': 5.004}], 'summary': 'Data preparation includes feature scaling, parameter selection, and data splitting for logistic regression model training.', 'duration': 48.417, 'max_score': 2715.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2715584.jpg'}, {'end': 2993.789, 'src': 'embed', 'start': 2965.722, 'weight': 2, 'content': [{'end': 2970.144, 'text': 'So let me execute this code and take a look in a, in a, form of a plot.', 'start': 2965.722, 'duration': 4.422}, {'end': 2972.765, 'text': 'So this is how the classification is done.', 'start': 2970.624, 'duration': 2.141}, {'end': 2974.265, 'text': 'So these are like the class.', 'start': 2973.025, 'duration': 1.24}, {'end': 2975.885, 'text': 'This is like the class boundary.', 'start': 2974.425, 'duration': 1.46}, {'end': 2985.987, 'text': 'The green color belongs to the class 1 and the red color belongs to class 0 and these dots indicate it has been misclassified.', 'start': 2976.305, 'duration': 9.682}, {'end': 2988.168, 'text': 'So some of them have been misclassified.', 'start': 2986.067, 'duration': 2.101}, {'end': 2989.748, 'text': "So that's what we are seeing.", 'start': 2988.688, 'duration': 1.06}, {'end': 2993.789, 'text': 'Some red dots in the green area and some green dots in the red area right?', 'start': 2989.848, 'duration': 3.941}], 'summary': 'Code executed for classification, misclassified dots observed.', 'duration': 28.067, 'max_score': 2965.722, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2965722.jpg'}, {'end': 3146.688, 'src': 'embed', 'start': 3120.829, 'weight': 1, 'content': [{'end': 3125.191, 'text': 'The higher that number is, the total in the diagonal, the higher the accuracy.', 'start': 3120.829, 'duration': 4.362}, {'end': 3132.957, 'text': 'And if we have quite a few numbers in non-diagonal locations, that means the accuracy is not very high.', 'start': 3126.731, 'duration': 6.226}, {'end': 3136.179, 'text': 'So here looks like the accuracy is pretty good.', 'start': 3133.037, 'duration': 3.142}, {'end': 3139.922, 'text': 'Now, we can actually quantify the accuracy by using these numbers.', 'start': 3136.259, 'duration': 3.663}, {'end': 3146.688, 'text': 'So what we do is the sum along the diagonal we have to take and divide that by the total observation.', 'start': 3139.963, 'duration': 6.725}], 'summary': 'High diagonal sum indicates higher accuracy, quantifiable by summing and dividing by total observations.', 'duration': 25.859, 'max_score': 3120.829, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3120829.jpg'}], 'start': 2550.931, 'title': 'Machine learning training', 'summary': 'Explains the process of data splitting, training models, and feature scaling. it covers logistic regression training with a performance of approximately 90% accuracy.', 'chapters': [{'end': 2739.423, 'start': 2550.931, 'title': 'Data splitting, training, & feature scaling', 'summary': 'Explains the importance of splitting data into a training set and test set for machine learning, using an example of splitting the data into 75% for training and 25% for testing, and performing feature scaling to normalize the data values.', 'duration': 188.492, 'highlights': ['Data Splitting Importance Explains the importance of splitting data into training and test sets for machine learning, using an example of splitting the data into 75% for training and 25% for testing.', 'Feature Scaling Explanation Details the concept of feature scaling to normalize data values, highlighting the use of the standard scaler class to achieve this normalization.']}, {'end': 3167.994, 'start': 2739.563, 'title': 'Logistic regression training', 'summary': 'Covers the process of training a logistic regression model, testing its performance, visualizing the results, and quantifying accuracy using a confusion matrix, achieving approximately 90% accuracy.', 'duration': 428.431, 'highlights': ['The process involves creating an instance of the logistic regression model, using the fit method for training, and the predict method for testing, achieving an accuracy of approximately 90% for the model.', 'Visualization of the training results using a plot demonstrates the classification with class boundaries, correctly predicted data points, and misclassified data points, providing a high-level overview of accuracy.', 'Quantification of accuracy is achieved using a confusion matrix, where a higher total value in the diagonals indicates higher accuracy, and in this case, the model achieves approximately 90% accuracy.']}], 'duration': 617.063, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg2550931.jpg', 'highlights': ['The process involves creating an instance of the logistic regression model, using the fit method for training, and the predict method for testing, achieving an accuracy of approximately 90% for the model.', 'Quantification of accuracy is achieved using a confusion matrix, where a higher total value in the diagonals indicates higher accuracy, and in this case, the model achieves approximately 90% accuracy.', 'Visualization of the training results using a plot demonstrates the classification with class boundaries, correctly predicted data points, and misclassified data points, providing a high-level overview of accuracy.', 'Data Splitting Importance Explains the importance of splitting data into training and test sets for machine learning, using an example of splitting the data into 75% for training and 25% for testing.', 'Feature Scaling Explanation Details the concept of feature scaling to normalize data values, highlighting the use of the standard scaler class to achieve this normalization.']}, {'end': 3439.37, 'segs': [{'end': 3212.036, 'src': 'embed', 'start': 3187.719, 'weight': 0, 'content': [{'end': 3195.844, 'text': 'decision tree can be used for classification as well as regression, even though it is more popular for classification,', 'start': 3187.719, 'duration': 8.125}, {'end': 3200.987, 'text': 'and it can be used to classify multiple classes as well, not just binary classification.', 'start': 3195.844, 'duration': 5.143}, {'end': 3202.809, 'text': 'So how does decision tree work?', 'start': 3201.148, 'duration': 1.661}, {'end': 3209.394, 'text': 'One of the good things about decision trees is that it is easy to represent and show how exactly it works,', 'start': 3202.909, 'duration': 6.485}, {'end': 3212.036, 'text': 'and therefore it is very easy to understand as well.', 'start': 3209.394, 'duration': 2.642}], 'summary': 'Decision tree can be used for classification and regression, and it is easy to understand and represent.', 'duration': 24.317, 'max_score': 3187.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3187719.jpg'}, {'end': 3291.467, 'src': 'embed', 'start': 3261.295, 'weight': 3, 'content': [{'end': 3265.057, 'text': 'And in between, we have decision nodes or internal nodes.', 'start': 3261.295, 'duration': 3.762}, {'end': 3269.399, 'text': 'There are different terms used, so need not be hung up by the exact terminology.', 'start': 3265.117, 'duration': 4.282}, {'end': 3275.781, 'text': "Now, let's say we have to use this decision tree to find out whether this person will accept the job offer or not.", 'start': 3269.479, 'duration': 6.302}, {'end': 3278.302, 'text': 'So first thing he considers is the salary.', 'start': 3275.821, 'duration': 2.481}, {'end': 3280.663, 'text': 'Is the salary greater than 60,000?', 'start': 3278.522, 'duration': 2.141}, {'end': 3282.444, 'text': "if no, it's a clear decision.", 'start': 3280.663, 'duration': 1.781}, {'end': 3283.704, 'text': 'the offer will be rejected.', 'start': 3282.444, 'duration': 1.26}, {'end': 3285.485, 'text': 'so we reach a decision.', 'start': 3283.704, 'duration': 1.781}, {'end': 3286.946, 'text': 'therefore, this is a leaf node.', 'start': 3285.485, 'duration': 1.461}, {'end': 3291.467, 'text': 'now, if the salary is greater than sixty thousand, it is not a clear-cut decision,', 'start': 3286.946, 'duration': 4.521}], 'summary': 'Using decision tree to predict job offer acceptance based on salary, with 60,000 as threshold.', 'duration': 30.172, 'max_score': 3261.295, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3261295.jpg'}, {'end': 3411.329, 'src': 'embed', 'start': 3342.846, 'weight': 4, 'content': [{'end': 3345.688, 'text': 'So this belongs to one class, which is accept offer.', 'start': 3342.846, 'duration': 2.842}, {'end': 3349.55, 'text': 'And these leaf nodes belong to a different class, which is reject offer.', 'start': 3345.868, 'duration': 3.682}, {'end': 3354.872, 'text': "So let's take an example and see how we can solve this problem using decision tree.", 'start': 3349.71, 'duration': 5.162}, {'end': 3360.235, 'text': "Let's say we have to implement a classification algorithm for kyphosis patient.", 'start': 3355.032, 'duration': 5.203}, {'end': 3369.221, 'text': 'And the problem is, a bunch of kids have been have undergone kyphosis surgery and we need to predict whether kyphosis is present in them or not.', 'start': 3360.375, 'duration': 8.846}, {'end': 3370.642, 'text': 'and how can we do this?', 'start': 3369.221, 'duration': 1.421}, {'end': 3372.543, 'text': 'using decision tree algorithm.', 'start': 3370.642, 'duration': 1.901}, {'end': 3376.186, 'text': 'so this is how the classification tree for kyphosis looks.', 'start': 3372.543, 'duration': 3.643}, {'end': 3381.229, 'text': 'so it starts with if the age is greater than 8.5.', 'start': 3376.186, 'duration': 5.043}, {'end': 3384.552, 'text': 'so this is how the decision tree looks.', 'start': 3381.229, 'duration': 3.323}, {'end': 3390.656, 'text': 'so the first criteria is the vertebra, the number on which the surgery has been performed.', 'start': 3384.552, 'duration': 6.104}, {'end': 3397.861, 'text': 'if it is greater than 8.5, then we need to perform further analysis and look at other criteria.', 'start': 3390.656, 'duration': 7.205}, {'end': 3402.684, 'text': 'If it is less than 8.5, it is clear that kyphosis is present.', 'start': 3398.081, 'duration': 4.603}, {'end': 3411.329, 'text': 'And if it is greater than 8.5, then we check whether the vertebra operated upon is greater than 14.5 or not.', 'start': 3403.024, 'duration': 8.305}], 'summary': 'Using decision tree for kyphosis classification in kids.', 'duration': 68.483, 'max_score': 3342.846, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3342846.jpg'}], 'start': 3168.494, 'title': 'Decision tree and random forest', 'summary': "Delves into decision tree and random forest algorithms, emphasizing their efficacy in handling classification and regression tasks. it explores decision tree's versatility and ease of representation, and presents real-world applications such as job offer decision-making and kyphosis classification.", 'chapters': [{'end': 3209.394, 'start': 3168.494, 'title': 'Decision tree and random forest', 'summary': "Explores decision tree and random forest algorithms, highlighting their ability to handle both classification and regression tasks, with a focus on decision tree's versatility and ease of representation.", 'duration': 40.9, 'highlights': ['Decision tree can be used for both classification and regression tasks, making it more versatile than logistic regression.', 'It is more popular for classification and can handle multiple classes, not just binary classification.', 'One of the advantages of decision trees is their easy representation and visualization of how they work.']}, {'end': 3324.985, 'start': 3209.394, 'title': 'Decision tree for job offer', 'summary': 'Explains the concept of decision tree through an example of using it to decide whether to accept a job offer based on salary, commute time, and performance incentives.', 'duration': 115.591, 'highlights': ['The decision tree is used to decide whether to accept a job offer based on factors such as salary, commute time, and performance incentives.', 'If the salary is greater than 60,000, the decision depends on factors such as commute time and performance incentives.', 'If the commute time is greater than one hour, the job offer is rejected, regardless of the salary being higher than 60,000.', 'If there are insufficient performance incentives, the job offer is rejected, even if the salary and commute time meet the criteria.']}, {'end': 3439.37, 'start': 3325.226, 'title': 'Decision tree for kyphosis classification', 'summary': 'Discusses the use of decision tree algorithm for classifying kyphosis patients based on criteria such as age, vertebra number, and age group, with the aim of predicting the presence of kyphosis with a focus on the leaf nodes and different classification classes.', 'duration': 114.144, 'highlights': ['The decision tree algorithm is used to classify kyphosis patients based on criteria such as age, vertebra number, and age group, in order to predict the presence of kyphosis. The chapter discusses the use of decision tree algorithm for classifying kyphosis patients based on criteria such as age, vertebra number, and age group, with the aim of predicting the presence of kyphosis.', "The leaf nodes are categorized into 'accept offer' and 'reject offer' classes, providing a binary classification for the decision tree model. The leaf nodes belong to different classes, 'accept offer' and 'reject offer,' enabling a binary classification for the decision tree model.", 'The criteria for classification include age, vertebra number, and age group, with specific thresholds for each criterion, such as 8.5 and 14.5 for vertebra number, to determine the presence or absence of kyphosis. Specific criteria such as age, vertebra number, and age group, with thresholds like 8.5 and 14.5 for vertebra number, are used to classify the presence or absence of kyphosis.']}], 'duration': 270.876, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3168494.jpg', 'highlights': ['Decision tree is more versatile than logistic regression, suitable for both classification and regression tasks.', "Decision tree's easy representation and visualization make it advantageous for understanding its workings.", 'Decision tree is popular for classification and can handle multiple classes, not just binary classification.', 'Decision tree is used to decide job offers based on factors like salary, commute time, and performance incentives.', 'Decision tree algorithm is utilized for classifying kyphosis patients based on age, vertebra number, and age group.', "Leaf nodes in the decision tree model enable binary classification for 'accept offer' and 'reject offer' classes.", 'Specific criteria and thresholds like 8.5 and 14.5 for vertebra number are used to classify the presence or absence of kyphosis.']}, {'end': 4262.098, 'segs': [{'end': 3591.111, 'src': 'embed', 'start': 3554.338, 'weight': 4, 'content': [{'end': 3558.461, 'text': "So that's the reason we feel there is good accuracy, which is 64%.", 'start': 3554.338, 'duration': 4.123}, {'end': 3559.982, 'text': 'They have been correctly classified.', 'start': 3558.461, 'duration': 1.521}, {'end': 3563.965, 'text': '64% of the observations have been correctly classified by this model.', 'start': 3560.002, 'duration': 3.963}, {'end': 3569.19, 'text': 'whereas 24% have been misclassified, which consists of this 4 plus this 2.', 'start': 3564.205, 'duration': 4.985}, {'end': 3574.175, 'text': "So that's how we use the confusion matrix to determine the accuracy of our decision tree model.", 'start': 3569.19, 'duration': 4.985}, {'end': 3579.86, 'text': "So let's go into the Jupyter notebook and take a look and run the code and see how it looks.", 'start': 3574.255, 'duration': 5.605}, {'end': 3591.111, 'text': 'So this is our Python notebook for decision tree and I will take you through the code not line by line of course but we will see the blocks.', 'start': 3580.621, 'duration': 10.49}], 'summary': 'A decision tree model achieved 64% accuracy with 24% misclassification, as determined by the confusion matrix.', 'duration': 36.773, 'max_score': 3554.338, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3554338.jpg'}, {'end': 3902.444, 'src': 'embed', 'start': 3873.638, 'weight': 1, 'content': [{'end': 3882.508, 'text': 'Using just the decision tree, now when we use random forest, the performance has improved to a good extent and it has become 76%.', 'start': 3873.638, 'duration': 8.87}, {'end': 3890.016, 'text': 'So random forest usually helps in increasing the accuracy when we are using decision trees as our algorithm.', 'start': 3882.508, 'duration': 7.508}, {'end': 3894.559, 'text': 'Okay, the last algorithm is the k-nearest neighbors algorithm.', 'start': 3890.276, 'duration': 4.283}, {'end': 3902.444, 'text': 'This is again a classification algorithm and it is actually very simple and straightforward, very easy to understand as well.', 'start': 3894.739, 'duration': 7.705}], 'summary': 'Random forest improved performance to 76%, k-nearest neighbors is simple and straightforward.', 'duration': 28.806, 'max_score': 3873.638, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3873638.jpg'}, {'end': 4050.879, 'src': 'embed', 'start': 4004.033, 'weight': 0, 'content': [{'end': 4016.842, 'text': "Now let's use K nearest neighbors and take an example to solve one of our previous problems that we did using logistic regression whether a person is going to buy an SUV or not,", 'start': 4004.033, 'duration': 12.809}, {'end': 4020.145, 'text': 'based on the age and estimated salary.', 'start': 4016.842, 'duration': 3.303}, {'end': 4024.908, 'text': "So before we go into Jupyter Notebook, let's again take a quick look at the core.", 'start': 4020.545, 'duration': 4.363}, {'end': 4026.829, 'text': 'So what are the various sections in the core?', 'start': 4024.928, 'duration': 1.901}, {'end': 4035.013, 'text': 'we import the libraries, we load the data set, we visualize the data, we split the data into training and test data set.', 'start': 4027.89, 'duration': 7.123}, {'end': 4039.895, 'text': 'we do some feature scaling and then we train our model and then we test our model.', 'start': 4035.013, 'duration': 4.882}, {'end': 4044.816, 'text': 'we visualize the training set results and then we visualize our test results.', 'start': 4039.895, 'duration': 4.921}, {'end': 4047.577, 'text': 'And both of these seem to be looking pretty good.', 'start': 4044.976, 'duration': 2.601}, {'end': 4050.879, 'text': 'And then we evaluate our model using the confusion matrix.', 'start': 4047.718, 'duration': 3.161}], 'summary': 'Using k nearest neighbors to predict suv purchase likelihood.', 'duration': 46.846, 'max_score': 4004.033, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg4004033.jpg'}], 'start': 3439.73, 'title': 'Implementing decision tree and knn algorithm', 'summary': 'Covers implementing a decision tree in python for a classification problem achieving 64% accuracy and discusses the k nearest neighbors algorithm with a 93% accuracy in predicting suv purchase.', 'chapters': [{'end': 3894.559, 'start': 3439.73, 'title': 'Implementing decision tree in python', 'summary': 'Covers implementing a decision tree in python for a classification problem, visualizing the data, training the model, evaluating using a confusion matrix, and comparing the accuracy with a random forest model, achieving 76% accuracy with random forest compared to 64% with a decision tree.', 'duration': 454.829, 'highlights': ['The model achieved 76% accuracy with a random forest compared to 64% with a decision tree. Using a random forest model improved the accuracy from 64% with a decision tree to 76%.', 'The confusion matrix showed 19 correct predictions out of 25, resulting in a 76% accuracy for the random forest model. The confusion matrix displayed 19 correct predictions out of 25, yielding a 76% accuracy for the random forest model.', 'The process involves visualizing the data, training the model, and evaluating using a confusion matrix. The process includes visualizing the data, training the model, and evaluating using a confusion matrix to determine accuracy.', 'The chapter explains the concept of using a random forest to improve performance and increase accuracy. The chapter elaborates on using a random forest to enhance performance and achieve higher accuracy compared to a decision tree.', 'The chapter demonstrates the use of a decision tree model for a classification problem and achieving 64% accuracy. The chapter showcases the implementation of a decision tree model for a classification problem and attaining 64% accuracy.']}, {'end': 4262.098, 'start': 3894.739, 'title': 'K nearest neighbors algorithm', 'summary': 'Discusses the k nearest neighbors (knn) algorithm, using historical data of heights and weights, determining the class of new data points based on the value of k, and applying knn to a problem of predicting suv purchase with a 93% accuracy, showcasing the training, testing, and evaluation process.', 'duration': 367.359, 'highlights': ['The K Nearest Neighbors (KNN) algorithm determines the class of new data points based on the value of K, which represents the number of nearest neighbors to consider. For a given data point, the algorithm finds the nearest objects based on historical data and assigns a class based on the majority of the nearest objects. The KNN algorithm determines the class of new data points based on the value of K, where it finds the nearest objects to a given data point and assigns a class based on the majority of the nearest objects. It demonstrates the impact of changing the value of K on the classification outcome, showcasing the importance of selecting the right value of K for training the model.', 'Applying KNN to a problem of predicting SUV purchase based on age and estimated salary resulted in a 93% accuracy, with 73 out of 80 observations classified correctly and 7 misclassifications, demonstrating the effectiveness of the algorithm in practical applications. The application of KNN to predict SUV purchase based on age and estimated salary achieved 93% accuracy, with 73 out of 80 observations classified correctly and 7 misclassifications, indicating the effectiveness of the algorithm in practical applications and its ability to produce accurate predictions.', 'The chapter covers the various steps involved in machine learning, including importing libraries, loading and visualizing the dataset, splitting the data into training and test sets, feature scaling, training the model, testing the model, and evaluating the model using the confusion matrix. The chapter details the various steps in machine learning, encompassing importing libraries, loading and visualizing the dataset, splitting the data into training and test sets, feature scaling, training the model, testing the model, and evaluating the model using the confusion matrix, providing a comprehensive overview of the machine learning process.']}], 'duration': 822.368, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/I7NrVwm3apg/pics/I7NrVwm3apg3439730.jpg', 'highlights': ['The K Nearest Neighbors (KNN) algorithm achieved 93% accuracy in predicting SUV purchase based on age and estimated salary.', 'The random forest model improved the accuracy from 64% with a decision tree to 76%.', 'The process includes visualizing the data, training the model, and evaluating using a confusion matrix to determine accuracy.', 'The chapter elaborates on using a random forest to enhance performance and achieve higher accuracy compared to a decision tree.', 'The chapter demonstrates the use of a decision tree model for a classification problem and achieving 64% accuracy.']}], 'highlights': ["Netflix's analysis of customer behavior using data from 30 million customers showcases the use of machine learning in content creation and audience engagement.", 'Machine learning is significantly impacting the healthcare industry, aiding diagnostic analysis of images like x-rays and MRIs to address the shortage of doctors.', 'Voice recognition, as seen with Siri, demonstrates the widespread adoption of machine learning in consumer applications.', 'Facial recognition is being used for security and crime-solving, gaining popularity in various areas.', 'The training process, model accuracy, and practical demonstrations are also included, achieving a root mean square error of 58 and an 87% accuracy for logistic regression.', "Furthermore, it explores decision tree's versatility and ease of representation, and presents real-world applications such as job offer decision-making and kyphosis classification.", 'Machine learning algorithms are broadly classified into supervised learning, unsupervised learning, and reinforcement learning, each with specific techniques and applications.', "Linear regression was discovered by Sir Francis Galton to predict a child's height based on the father's height using the mean square error.", 'The root mean square error, mean squared error, and mean absolute error are calculated to measure the accuracy of the linear regression model, with lower values indicating higher accuracy.', 'Logistic regression is used for classification problems, such as predicting loan repayment or image classification, with an accuracy of 87% demonstrated in the Python implementation.', 'The demo focuses on predicting SUV purchases using logistic regression based on parameters like age and salary.', 'Decision tree is more versatile than logistic regression, suitable for both classification and regression tasks.', 'The K Nearest Neighbors (KNN) algorithm achieved 93% accuracy in predicting SUV purchase based on age and estimated salary.']}