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Data Science Interview Questions | Data Science Interview Questions Answers And Tips | Simplilearn

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Looking to excel in your Data Science job interviews? Look no further! Our latest video, Data Science Interview Questions,is packed with valuable insights to help you succeed like a pro.
🚀 Join us as we delve into the most crucial Data Science interview questions. Master key concepts, explore popular ML algorithms, sharpen your coding skills, and much more!
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{'title': 'Data Science Interview Questions | Data Science Interview Questions Answers And Tips | Simplilearn', 'heatmap': [{'end': 2391.557, 'start': 2325.549, 'weight': 0.825}, {'end': 2616.835, 'start': 2581.715, 'weight': 0.876}], 'summary': 'Covers logical interview questions, house price prediction, dimensionality reduction, rmse vs mse, accuracy calculation, and algorithm selection, addressing topics such as supervised vs unsupervised learning, decision trees, random forest, and collaborative filtering, providing insights into data science concepts and practical applications.', 'chapters': [{'end': 745.023, 'segs': [{'end': 51.968, 'src': 'embed', 'start': 20.68, 'weight': 0, 'content': [{'end': 24.803, 'text': 'In this one, you have two buckets, one of three liters and the other of five liters.', 'start': 20.68, 'duration': 4.123}, {'end': 26.925, 'text': "You're expected to measure exactly four liters.", 'start': 24.943, 'duration': 1.982}, {'end': 31.39, 'text': 'How will you complete the task? And note, you only have the two buckets.', 'start': 27.705, 'duration': 3.685}, {'end': 34.054, 'text': "You don't have a third bucket or anything like that, just the two buckets.", 'start': 31.47, 'duration': 2.584}, {'end': 39.501, 'text': 'And the object of the question like this is to see how well you are thinking outside the box.', 'start': 34.294, 'duration': 5.207}, {'end': 41.283, 'text': "In this case, you're in a larger box.", 'start': 39.781, 'duration': 1.502}, {'end': 42.004, 'text': 'You have two buckets.', 'start': 41.323, 'duration': 0.681}, {'end': 44.446, 'text': 'And also the pattern which you go on.', 'start': 42.144, 'duration': 2.302}, {'end': 45.326, 'text': 'and what that means is,', 'start': 44.446, 'duration': 0.88}, {'end': 51.968, 'text': "if you look at the two buckets and we'll show you their answer in just a second you have a bucket with three liters and a bucket with five liters,", 'start': 45.326, 'duration': 6.642}], 'summary': 'Use the 3-liter and 5-liter buckets to measure 4 liters without additional containers, necessitating creative problem-solving.', 'duration': 31.288, 'max_score': 20.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE20680.jpg'}, {'end': 181.384, 'src': 'embed', 'start': 145.026, 'weight': 1, 'content': [{'end': 147.427, 'text': 'We have logic like this one, which is a lot of fun.', 'start': 145.026, 'duration': 2.401}, {'end': 150.348, 'text': 'We have questions that come up that are more vocabulary.', 'start': 147.687, 'duration': 2.661}, {'end': 154.41, 'text': 'List the difference between supervised and unsupervised learning.', 'start': 150.589, 'duration': 3.821}, {'end': 158.312, 'text': 'Probably one of the fundamental breakdowns in data science.', 'start': 154.65, 'duration': 3.662}, {'end': 162.253, 'text': 'And supervised learning uses known and labeled data as input.', 'start': 158.592, 'duration': 3.661}, {'end': 165.375, 'text': 'Supervised learning has a feedback mechanism.', 'start': 162.573, 'duration': 2.802}, {'end': 171.297, 'text': 'Most commonly used supervised learning algorithms are decision tree, logistic regression, support vector machine.', 'start': 165.595, 'duration': 5.702}, {'end': 175.62, 'text': 'And you should know that those are probably the most common use right now, and there certainly are so many coming out.', 'start': 171.437, 'duration': 4.183}, {'end': 181.384, 'text': "So that's a very evolving thing, and be aware of a lot of the different algorithms that are out there, outside of the deep learning.", 'start': 175.74, 'duration': 5.644}], 'summary': 'Supervised learning uses labeled data, with decision tree and logistic regression being common algorithms.', 'duration': 36.358, 'max_score': 145.026, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE145026.jpg'}, {'end': 228.353, 'src': 'embed', 'start': 201.958, 'weight': 3, 'content': [{'end': 206.521, 'text': "I'm going to say k-means definitely is at the top of the list in the hierarchical clustering.", 'start': 201.958, 'duration': 4.563}, {'end': 212.184, 'text': "Those two are used so many times, so really important to understand what those are and how they're used.", 'start': 206.641, 'duration': 5.543}, {'end': 214.846, 'text': 'And most important is to understand that supervised learning is.', 'start': 212.384, 'duration': 2.462}, {'end': 221.87, 'text': "you have your data set where you have training data and you have all those different pieces moving around, but you're able to train it.", 'start': 214.846, 'duration': 7.024}, {'end': 222.771, 'text': 'You know the answers.', 'start': 221.91, 'duration': 0.861}, {'end': 226.333, 'text': "And unsupervised, we're just grouping things together that look like they go together.", 'start': 223.011, 'duration': 3.322}, {'end': 228.353, 'text': 'How is logistic regression done?', 'start': 226.593, 'duration': 1.76}], 'summary': 'K-means and hierarchical clustering are frequently used, while supervised learning involves training with known answers. logistic regression method is discussed.', 'duration': 26.395, 'max_score': 201.958, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE201958.jpg'}, {'end': 408.325, 'src': 'embed', 'start': 379.534, 'weight': 4, 'content': [{'end': 382.156, 'text': 'You have to calculate your information gain of all attributes.', 'start': 379.534, 'duration': 2.622}, {'end': 387.121, 'text': 'Then you choose the attribute with the highest information gain as the root node.', 'start': 382.537, 'duration': 4.584}, {'end': 392.122, 'text': 'So if you can separate your group and each group chaos and each group is lowered.', 'start': 387.581, 'duration': 4.541}, {'end': 394.442, 'text': 'whichever split lowers the chaos the most.', 'start': 392.122, 'duration': 2.32}, {'end': 396.163, 'text': "that's where you split it and that's your root node.", 'start': 394.442, 'duration': 1.721}, {'end': 401.123, 'text': 'At that point you repeat the same procedure on every branch till the decision node of each branch finalized.', 'start': 396.303, 'duration': 4.82}, {'end': 408.325, 'text': 'So understanding that setup is pretty important as far as decision trees and you can see here we have a nice visual of a decision tree.', 'start': 401.383, 'duration': 6.942}], 'summary': 'Calculate information gain for attributes, choose highest gain as root node, repeat for each branch till decision nodes finalized.', 'duration': 28.791, 'max_score': 379.534, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE379534.jpg'}, {'end': 472.941, 'src': 'embed', 'start': 449.084, 'weight': 5, 'content': [{'end': 455.892, 'text': 'So if you split your data up into a lot of different packages and you do a decision tree on each of those different groups of data,', 'start': 449.084, 'duration': 6.808}, {'end': 458.435, 'text': 'the random forest is bringing all those trees together.', 'start': 455.892, 'duration': 2.543}, {'end': 466.258, 'text': 'So how do you build a random forest model? Randomly select K features from a total of M features, where K is less than M.', 'start': 458.655, 'duration': 7.603}, {'end': 470.18, 'text': 'Among the K features, calculate the node D using the best split point.', 'start': 466.258, 'duration': 3.922}, {'end': 472.941, 'text': 'Split the node into daughter nodes using the best split.', 'start': 470.46, 'duration': 2.481}], 'summary': 'Random forest combines decision trees by randomly selecting features and calculating split points.', 'duration': 23.857, 'max_score': 449.084, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE449084.jpg'}, {'end': 552.303, 'src': 'embed', 'start': 526.812, 'weight': 6, 'content': [{'end': 533.054, 'text': "Use regularization techniques such as lasso that penalize certain model parameters if they're likely to cause overfitting.", 'start': 526.812, 'duration': 6.242}, {'end': 542.158, 'text': "And you should also be well aware that your cross-validation techniques that's like a pre-data or your lasso and your regularization techniques are usually during the process.", 'start': 533.294, 'duration': 8.864}, {'end': 547.561, 'text': "So when you're prepping your data, that's when you're going to do a cross-validation such as like splitting your data into three groups,", 'start': 542.398, 'duration': 5.163}, {'end': 552.303, 'text': 'and you train it on two groups and test it on one and then switch which two groups you test it on that kind of thing.', 'start': 547.561, 'duration': 4.742}], 'summary': 'Regularization techniques like lasso can prevent overfitting, with cross-validation involving data splitting and testing.', 'duration': 25.491, 'max_score': 526.812, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE526812.jpg'}, {'end': 580.681, 'src': 'embed', 'start': 555.625, 'weight': 7, 'content': [{'end': 561.909, 'text': 'There are nine balls out of which one ball is heavy in weight and the rest are of the same weight.', 'start': 555.625, 'duration': 6.284}, {'end': 565.991, 'text': 'In how many minimum weighings will you find the heavier ball?', 'start': 562.369, 'duration': 3.622}, {'end': 573.036, 'text': 'And when we say weighing, think of a scale where you can put objects on one side and the other and you can see which side is heavier.', 'start': 566.172, 'duration': 6.864}, {'end': 574.477, 'text': 'And you want to minimize that.', 'start': 573.256, 'duration': 1.221}, {'end': 579.18, 'text': "You want to split the balls up in such a way that you're going to do as few measurements as you can.", 'start': 574.517, 'duration': 4.663}, {'end': 580.681, 'text': 'You will need to perform two weighings.', 'start': 579.24, 'duration': 1.441}], 'summary': 'To find the heavier ball, a minimum of two weighings is required.', 'duration': 25.056, 'max_score': 555.625, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE555625.jpg'}, {'end': 667.177, 'src': 'embed', 'start': 640.556, 'weight': 8, 'content': [{'end': 646.26, 'text': "And hopefully, if you know a little Latin, you'll kick in there that you have uni and you have bi and you have multi.", 'start': 640.556, 'duration': 5.704}, {'end': 648.502, 'text': 'because the answer is in the words themselves.', 'start': 646.36, 'duration': 2.142}, {'end': 654.747, 'text': "So the first one, this type of data contains only one variable, so that's the univariate.", 'start': 649.002, 'duration': 5.745}, {'end': 659.791, 'text': 'Purpose of the univariate analysis is to describe the data and find patterns that exist within it.', 'start': 654.787, 'duration': 5.004}, {'end': 667.177, 'text': "So when you only see one variable coming in, in this case we're using height of students, you're limited as far as what you can do with that data.", 'start': 660.071, 'duration': 7.106}], 'summary': 'Univariate analysis describes single-variable data to find patterns.', 'duration': 26.621, 'max_score': 640.556, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE640556.jpg'}], 'start': 2.728, 'title': 'Decision trees, random forest, and supervised vs unsupervised learning', 'summary': 'Covers logical interview questions involving two buckets to measure 4 liters, and the differences between supervised and unsupervised learning. it also explains decision tree creation, entropy, information gain, and building a random forest model, emphasizing the understanding of overfitting and various types of analysis.', 'chapters': [{'end': 339.954, 'start': 2.728, 'title': 'Logical interview questions & supervised vs unsupervised learning', 'summary': 'Explores a logical interview question solving a task with two buckets of 3 and 5 liters to measure exactly 4 liters, demonstrating how to think outside the box. it also covers fundamental differences between supervised and unsupervised learning, including key algorithms and functions like logistic regression.', 'duration': 337.226, 'highlights': ['Logical interview question solution Explains the process of using two buckets of 3 and 5 liters to measure exactly 4 liters, demonstrating thinking outside the box.', 'Fundamental breakdown of supervised and unsupervised learning Details the key differences between supervised and unsupervised learning, including the use of labeled and unlabeled data, feedback mechanisms, and common algorithms.', 'Supervised learning algorithms Lists commonly used supervised learning algorithms such as decision tree, logistic regression, and support vector machine, highlighting their significance in data science.', 'Unsupervised learning algorithms Lists commonly used unsupervised learning algorithms like k-means and hierarchical clustering, emphasizing their importance and usage in data science.', 'Logistic regression function and its significance Explains logistic regression and its significance in measuring the relationship between dependent and independent variables, including the use of the sigmoid function and probability estimation.']}, {'end': 745.023, 'start': 339.954, 'title': 'Decision trees and random forest model', 'summary': 'Explains the steps in making a decision tree, including the calculation of entropy and information gain, and the process of building a random forest model, highlighting the importance of understanding overfitting and the difference between univariate, bivariate, and multivariate analysis.', 'duration': 405.069, 'highlights': ['The process of making a decision tree involves calculating entropy and information gain to choose the attribute with the highest information gain as the root node, and then repeating the same procedure on every branch till the decision node of each branch is finalized. Calculation of entropy and information gain, selection of attribute with highest information gain as root node', 'The process of building a random forest model includes randomly selecting K features from a total of M features, calculating the node D using the best split point, splitting the node into daughter nodes using the best split, and building forests by repeating the steps to create n number of trees. Random selection of features, calculation of node D using best split point, building forests to create n number of trees', "Three main methods to avoid overfitting in a model are keeping the model simple by using fewer variables, using cross-validation techniques such as k-folds cross-validation, and using regularization techniques such as lasso that penalize certain model parameters if they're likely to cause overfitting. Methods to avoid overfitting: keeping model simple, using cross-validation techniques, using regularization techniques", 'The minimum number of weighings needed to find the heavier ball out of nine balls is two, achieved by dividing the balls into three groups of three and performing specific measurements. Minimum number of weighings needed to find the heavier ball', 'Explanation of univariate, bivariate, and multivariate analysis, including the purpose and characteristics of each type of analysis. Explanation of univariate, bivariate, and multivariate analysis, purpose and characteristics of each type']}], 'duration': 742.295, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2728.jpg', 'highlights': ['Logical interview question solution: Using two buckets of 3 and 5 liters to measure exactly 4 liters, demonstrating thinking outside the box.', 'Fundamental breakdown of supervised and unsupervised learning: Details key differences, use of labeled and unlabeled data, feedback mechanisms, and common algorithms.', 'Supervised learning algorithms: Lists commonly used algorithms such as decision tree, logistic regression, and support vector machine, highlighting their significance.', 'Unsupervised learning algorithms: Lists commonly used algorithms like k-means and hierarchical clustering, emphasizing their importance and usage.', 'Decision tree creation: Involves calculating entropy and information gain to choose the attribute with the highest information gain as the root node.', 'Building a random forest model: Includes randomly selecting K features from a total of M features, calculating the node D using the best split point, and building forests to create n number of trees.', 'Methods to avoid overfitting: Keeping model simple, using cross-validation techniques, using regularization techniques.', 'Minimum number of weighings needed to find the heavier ball out of nine balls is two, achieved by dividing the balls into three groups of three and performing specific measurements.', 'Explanation of univariate, bivariate, and multivariate analysis, including the purpose and characteristics of each type.']}, {'end': 1247.507, 'segs': [{'end': 777.105, 'src': 'embed', 'start': 745.403, 'weight': 0, 'content': [{'end': 746.823, 'text': 'So the word prediction should come up.', 'start': 745.403, 'duration': 1.42}, {'end': 749.064, 'text': 'So we have description and prediction.', 'start': 746.843, 'duration': 2.221}, {'end': 753.948, 'text': 'When the data involves three or more variables, it is categorized under multivariate.', 'start': 749.304, 'duration': 4.644}, {'end': 757.39, 'text': 'It is similar to bivariate, but contains more than one dependent variable.', 'start': 754.188, 'duration': 3.202}, {'end': 760.933, 'text': 'In this example another really common one the data for house price prediction.', 'start': 757.51, 'duration': 3.423}, {'end': 767.878, 'text': 'the patterns can be studied by drawing conclusions using mean, median and mode dispersion or range minimum maximum, etc.', 'start': 760.933, 'duration': 6.945}, {'end': 775.884, 'text': "And so you can start describing the data, that's what all that was, and then using that description to guess what the price is going to be.", 'start': 768.138, 'duration': 7.746}, {'end': 777.105, 'text': 'So this is very good.', 'start': 776.184, 'duration': 0.921}], 'summary': 'Multivariate analysis involves studying patterns in data with three or more variables, such as house price prediction, using mean, median, mode, dispersion, and range.', 'duration': 31.702, 'max_score': 745.403, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE745403.jpg'}, {'end': 957.82, 'src': 'embed', 'start': 931.139, 'weight': 5, 'content': [{'end': 936.184, 'text': 'For numbers which are multiples of both, 3 and 5 print FizzBuzz.', 'start': 931.139, 'duration': 5.045}, {'end': 940.407, 'text': 'And this really is testing your knowledge in iterating over data.', 'start': 936.724, 'duration': 3.683}, {'end': 941.168, 'text': 'Very important.', 'start': 940.527, 'duration': 0.641}, {'end': 946.592, 'text': 'My sister who runs the university, the data science team is in charge of their department.', 'start': 941.308, 'duration': 5.284}, {'end': 952.516, 'text': 'The first question she asks in her interview of anybody who comes in, is how do they iterate through data?', 'start': 946.772, 'duration': 5.744}, {'end': 957.82, 'text': "So this question comes up a lot and it's very important you have an understanding.", 'start': 953.657, 'duration': 4.163}], 'summary': 'Understanding iteration is crucial for data science. fizzbuzz for multiples of 3 and 5.', 'duration': 26.681, 'max_score': 931.139, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE931139.jpg'}, {'end': 1125.733, 'src': 'embed', 'start': 1093.492, 'weight': 3, 'content': [{'end': 1096.494, 'text': 'So with smaller data, you start running into problems because you lose a lot of data.', 'start': 1093.492, 'duration': 3.002}, {'end': 1103.439, 'text': "And so we can substitute missing values with the mean or average of the rest of the data using Panda's data frame in Python.", 'start': 1096.614, 'duration': 6.825}, {'end': 1105.841, 'text': "There's different ways to do this, obviously, in different languages.", 'start': 1103.559, 'duration': 2.282}, {'end': 1108.082, 'text': "And even in Python, there's different ways to do this.", 'start': 1105.901, 'duration': 2.181}, {'end': 1109.724, 'text': "But in Python, it's real easy.", 'start': 1108.263, 'duration': 1.461}, {'end': 1111.285, 'text': 'You can do the df.mean.', 'start': 1109.744, 'duration': 1.541}, {'end': 1112.526, 'text': 'So you get the mean value.', 'start': 1111.445, 'duration': 1.081}, {'end': 1117.249, 'text': 'So if you set mean equal to that, then you can do a df.fillna with the mean value.', 'start': 1112.766, 'duration': 4.483}, {'end': 1119.991, 'text': 'Very easy to do in a Python Panda script.', 'start': 1117.409, 'duration': 2.582}, {'end': 1125.733, 'text': "And if you're using Python, you should really know pandas and numpy, number Python and pandas data frames.", 'start': 1120.211, 'duration': 5.522}], 'summary': 'In python, missing values can be replaced with the mean using pandas data frame, making it easy and efficient.', 'duration': 32.241, 'max_score': 1093.492, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1093492.jpg'}, {'end': 1160.835, 'src': 'embed', 'start': 1131.915, 'weight': 4, 'content': [{'end': 1137.417, 'text': 'So back to our basic algebra from high school Euclidean distance is the line on the triangle.', 'start': 1131.915, 'duration': 5.502}, {'end': 1143.702, 'text': "And so if we're given the points plot 1 equals 1, comma 3, plot 2 equals 2, comma 5,", 'start': 1137.837, 'duration': 5.865}, {'end': 1151.548, 'text': 'we know that from this we can take the difference of each one of those points, square them and then take the square root of everything.', 'start': 1143.702, 'duration': 7.846}, {'end': 1160.835, 'text': 'So the Euclidean distance equals the square root of plot 1, 0 minus plot 2 of 0 squared plus plot 1 of 1 minus plot 2 of 1 squared, mouthful there.', 'start': 1151.808, 'duration': 9.027}], 'summary': 'Euclidean distance calculation for given points: (1,3) and (2,5)', 'duration': 28.92, 'max_score': 1131.915, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1131915.jpg'}, {'end': 1251.432, 'src': 'embed', 'start': 1224.049, 'weight': 6, 'content': [{'end': 1227.93, 'text': "The hour hand has traveled for 6.5 hours, six and a half, 6.5, so it's covered 6.5 times 30, which equals 195 degrees.", 'start': 1224.049, 'duration': 3.881}, {'end': 1243.121, 'text': 'The difference between the two will give the angle between the two hands, thus the required angle equals 195 minus 180 equals 15 degrees.', 'start': 1235.111, 'duration': 8.01}, {'end': 1247.507, 'text': 'And this is nice the way they solved it because you can now punch in any kind of time within reason.', 'start': 1243.382, 'duration': 4.125}, {'end': 1251.432, 'text': 'The hard part is on the hours, is you have to be able to convert the hours into decimals.', 'start': 1247.587, 'duration': 3.845}], 'summary': 'The hour hand traveled 6.5 hours, resulting in a 15-degree angle between the hands.', 'duration': 27.383, 'max_score': 1224.049, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1224049.jpg'}], 'start': 745.403, 'title': 'House price prediction and multivariate data', 'summary': 'Explores using multivariate data to predict house prices, employing mean, median, mode, dispersion, and range for predictions. it also addresses feature selection methods, handling missing data values, and python programming in data science.', 'chapters': [{'end': 795.461, 'start': 745.403, 'title': 'House price prediction and multivariate data', 'summary': 'Discusses the use of multivariate data to predict house prices, utilizing mean, median, mode, dispersion, and range to describe the data and make predictions, with an example of predicting house prices based on features like number of bedrooms, floors, and square footage.', 'duration': 50.058, 'highlights': ['The data involves three or more variables, categorized as multivariate, similar to bivariate but with more than one dependent variable.', 'Describing the data using mean, median, mode, dispersion, and range to predict house prices based on features like number of bedrooms, floors, and square footage.', 'Using existing knowledge of similar properties to guess the price of a new property, such as a two-bedroom, zero-floor, 900-square-foot house usually running about 40,000.']}, {'end': 1247.507, 'start': 795.642, 'title': 'Feature selection methods & data handling', 'summary': 'Discusses feature selection methods including filter and wrapper methods, and how to handle missing data values, with a focus on python programming and data science concepts.', 'duration': 451.865, 'highlights': ["The chapter discusses feature selection methods including filter and wrapper methods, and how to handle missing data values using Python's Panda data frame. It explains the two main feature selection methods: filter and wrapper, and the methods for handling missing data values, emphasizing Python's Panda data frame.", 'It explains the calculation of Euclidean distance in Python and the concept of predictive and postscriptive methods in data science. It covers the calculation of Euclidean distance in Python and the concept of predictive and postscriptive methods in data science.', 'The chapter includes a programming exercise to print numbers from 1 to 50, with specific conditions for multiples of 3 and 5. It presents a programming exercise to print numbers from 1 to 50 with conditions for multiples of 3, 5, and both, testing knowledge in iterating over data.', 'It explains the calculation of the angle between the hour and minute hands of a clock, demonstrating mathematical problem-solving. It demonstrates the calculation of the angle between the hour and minute hands of a clock, showcasing mathematical problem-solving skills.']}], 'duration': 502.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE745403.jpg', 'highlights': ['Using mean, median, mode, dispersion, and range to predict house prices based on features like number of bedrooms, floors, and square footage.', 'Describing the data using mean, median, mode, dispersion, and range to predict house prices based on features like number of bedrooms, floors, and square footage.', 'The data involves three or more variables, categorized as multivariate, similar to bivariate but with more than one dependent variable.', "The chapter discusses feature selection methods including filter and wrapper methods, and how to handle missing data values using Python's Panda data frame.", 'It explains the calculation of Euclidean distance in Python and the concept of predictive and postscriptive methods in data science.', 'It includes a programming exercise to print numbers from 1 to 50 with conditions for multiples of 3, 5, and both, testing knowledge in iterating over data.', 'It explains the calculation of the angle between the hour and minute hands of a clock, demonstrating mathematical problem-solving skills.']}, {'end': 1609.578, 'segs': [{'end': 1297.967, 'src': 'embed', 'start': 1270.352, 'weight': 0, 'content': [{'end': 1274.373, 'text': 'It reduces computation time as less dimensions lead to less computing.', 'start': 1270.352, 'duration': 4.021}, {'end': 1276.054, 'text': 'It removes redundant features.', 'start': 1274.514, 'duration': 1.54}, {'end': 1281.397, 'text': "For example, there's no point in storing a value in two different units, meters and inches.", 'start': 1276.134, 'duration': 5.263}, {'end': 1284.879, 'text': 'And I certainly run into a lot with this with text analysis.', 'start': 1281.877, 'duration': 3.002}, {'end': 1291.802, 'text': "I've been known to run a text analysis over a series of documents ends up with over 1.4 million different features.", 'start': 1285.279, 'duration': 6.523}, {'end': 1293.743, 'text': "That's a lot of different words being used.", 'start': 1292.162, 'duration': 1.581}, {'end': 1297.967, 'text': 'And if you do what they call by connect them, you connect two words together.', 'start': 1294.044, 'duration': 3.923}], 'summary': 'Reducing dimensions in text analysis can save time, e.g. 1.4m features in documents.', 'duration': 27.615, 'max_score': 1270.352, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1270352.jpg'}, {'end': 1474.355, 'src': 'embed', 'start': 1443.756, 'weight': 3, 'content': [{'end': 1448.94, 'text': 'So eigenvalues based on that one are 3, minus 5, and 6.', 'start': 1443.756, 'duration': 5.184}, {'end': 1453.263, 'text': 'And then from there we can calculate the eigenvector for lambda equals 3.', 'start': 1448.94, 'duration': 4.323}, {'end': 1459.286, 'text': 'And you can see here where the matrix as we write it out is the minus 5, minus 4, 2, minus 2, minus 2, minus 2, 4, 2, 2.', 'start': 1453.263, 'duration': 6.023}, {'end': 1460.667, 'text': 'That was from the beginning.', 'start': 1459.286, 'duration': 1.381}, {'end': 1464.109, 'text': 'Put in the x, y, and z equals 0, 0, 0.', 'start': 1460.847, 'duration': 3.262}, {'end': 1474.355, 'text': 'And so when we put in those numbers and we calculate them out, we have for x equals 1, we have the minus 5, minus 4y plus 2z equals 0, minus 2,', 'start': 1464.109, 'duration': 10.246}], 'summary': 'Eigenvalues: 3, -5, 6. calculated eigenvector for λ=3. matrix values and calculated x=1.', 'duration': 30.599, 'max_score': 1443.756, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1443756.jpg'}, {'end': 1553.819, 'src': 'embed', 'start': 1523.971, 'weight': 4, 'content': [{'end': 1526.795, 'text': 'where you change something and you want to figure out how your changes are going to affect things.', 'start': 1523.971, 'duration': 2.824}, {'end': 1529.318, 'text': "We need to monitor it and make sure it's doing what it's supposed to do.", 'start': 1526.895, 'duration': 2.423}, {'end': 1534.766, 'text': 'Evaluation metrics of the current model is calculated to determine if new algorithm is needed.', 'start': 1529.539, 'duration': 5.227}, {'end': 1536.087, 'text': 'And then we compare it.', 'start': 1535.166, 'duration': 0.921}, {'end': 1540.393, 'text': 'The new models are compared against each other to determine which model performs the best.', 'start': 1536.228, 'duration': 4.165}, {'end': 1541.575, 'text': 'And then we do a rebuild.', 'start': 1540.634, 'duration': 0.941}, {'end': 1545.016, 'text': 'the best performing model is rebuilt on the current state of data.', 'start': 1541.835, 'duration': 3.181}, {'end': 1545.736, 'text': 'This is interesting.', 'start': 1545.156, 'duration': 0.58}, {'end': 1546.957, 'text': 'I found this out just recently.', 'start': 1545.796, 'duration': 1.161}, {'end': 1553.819, 'text': "If you're in weather prediction, the really big weather areas have about seven or eight different models depending on what's going on.", 'start': 1547.077, 'duration': 6.742}], 'summary': 'Monitoring changes and evaluating models, weather prediction uses 7-8 different models.', 'duration': 29.848, 'max_score': 1523.971, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1523971.jpg'}, {'end': 1596.191, 'src': 'embed', 'start': 1570.812, 'weight': 5, 'content': [{'end': 1575.498, 'text': 'Most commonly used nowadays in marketing, so very big industry understanding.', 'start': 1570.812, 'duration': 4.686}, {'end': 1580.784, 'text': 'recommender systems predicts the rating or preference a user would give to a product.', 'start': 1575.498, 'duration': 5.286}, {'end': 1583.045, 'text': "And they're split into two different areas.", 'start': 1581.284, 'duration': 1.761}, {'end': 1584.726, 'text': 'One is collaborative filtering.', 'start': 1583.265, 'duration': 1.461}, {'end': 1591.709, 'text': 'And a good example of that is the last.fm recommends tracks that are often played by other users with similar interests.', 'start': 1584.886, 'duration': 6.823}, {'end': 1596.191, 'text': "So people who, if you're on Amazon, people who bought this also bought that.", 'start': 1591.989, 'duration': 4.202}], 'summary': 'Marketing heavily relies on recommender systems, such as collaborative filtering in last.fm and amazon, to predict user preferences.', 'duration': 25.379, 'max_score': 1570.812, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1570812.jpg'}], 'start': 1247.587, 'title': 'Benefits of dimensionality reduction', 'summary': 'Discusses dimensionality reduction, converting high-dimensional data into lower dimensions to compress data, reduce storage space, computation time, and eliminate redundant features, demonstrated through a text analysis with over 1.4 million features.', 'chapters': [{'end': 1311.738, 'start': 1247.587, 'title': 'Dimensionality reduction benefits', 'summary': 'Discusses dimensionality reduction, which involves converting high-dimensional data into lower dimensions to compress data, reduce storage space, computation time, and eliminate redundant features, demonstrated through a text analysis with over 1.4 million features.', 'duration': 64.151, 'highlights': ['Dimension reduction refers to the process of converting a set of data having vast dimensions into data with lesser dimensions fields to convey similar information concisely, reducing the storage space (1.4 million features in the text analysis).', 'It reduces computation time as less dimensions lead to less computing and removes redundant features, such as eliminating the need to store values in multiple units (e.g., meters and inches) (4.8 million features in the text analysis).', 'Text analysis example demonstrates the challenges of dealing with over 1.4 million different features, illustrating the need to reduce the list and find ways to bring down the high processing load.']}, {'end': 1609.578, 'start': 1311.999, 'title': 'Eigenvalues and eigenvectors of 3x3 matrix', 'summary': 'Explains calculating eigenvalues and eigenvectors of a 3x3 matrix, solving the characteristic equation, and maintaining deployed models, including the process of monitoring, evaluating, and rebuilding the best performing model.', 'duration': 297.579, 'highlights': ['The process of calculating eigenvalues and eigenvectors of a 3x3 matrix is explained, including solving the characteristic equation and identifying the eigenvalues as 3, -5, and 6. Eigenvalues of 3x3 matrix: 3, -5, 6; Solving the characteristic equation.', 'The importance of monitoring and evaluating deployed models is emphasized, including the constant monitoring of model performance and the comparison of new algorithms to determine the best performing model. Importance of monitoring and evaluating deployed models; Comparison of new algorithms.', 'An explanation of recommender systems is provided, detailing the prediction of user preferences for products and the two types of filtering methods: collaborative and content-based. Description of recommender systems; Types of filtering methods: collaborative and content-based.']}], 'duration': 361.991, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1247587.jpg', 'highlights': ['Dimension reduction compresses data, reducing storage space and computation time (1.4M features).', 'Less dimensions lead to less computing and remove redundant features (4.8M features).', 'Text analysis example illustrates the need to reduce the list and processing load (1.4M features).', 'Explains calculating eigenvalues and eigenvectors of a 3x3 matrix, identifying eigenvalues as 3, -5, and 6.', 'Emphasizes the importance of monitoring and evaluating deployed models, comparing new algorithms.', 'Details recommender systems, predicting user preferences and two filtering methods: collaborative and content-based.']}, {'end': 1983.244, 'segs': [{'end': 1659.841, 'src': 'embed', 'start': 1630.012, 'weight': 0, 'content': [{'end': 1635.335, 'text': 'The RMSE and the MSE are the two of the most common measures of accuracy for a linear regression model.', 'start': 1630.012, 'duration': 5.323}, {'end': 1639.718, 'text': 'And you can see here we have the root mean square error, RMSE equals,', 'start': 1635.415, 'duration': 4.303}, {'end': 1648.127, 'text': 'and this is the square root of the sum of the predicted minus the actual squared over the total number.', 'start': 1639.718, 'duration': 8.409}, {'end': 1650.61, 'text': "So we're just looking for the average mean.", 'start': 1648.347, 'duration': 2.263}, {'end': 1652.692, 'text': "So we're looking for the average over the n.", 'start': 1650.79, 'duration': 1.902}, {'end': 1659.841, 'text': "And the reason you need to know about the difference between RMSE versus MSE is when you're doing a lot of these models and you're building your own model,", 'start': 1652.692, 'duration': 7.149}], 'summary': 'Rmse and mse are common measures of accuracy for linear regression models, rmse is the square root of the sum of the predicted minus the actual squared over the total number, and understanding the difference is important for building models.', 'duration': 29.829, 'max_score': 1630.012, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1630012.jpg'}, {'end': 1769.863, 'src': 'embed', 'start': 1726.321, 'weight': 1, 'content': [{'end': 1731.945, 'text': 'we end up with 0.68 or 68% chance that it will rain on the weekend.', 'start': 1726.321, 'duration': 5.624}, {'end': 1737.029, 'text': 'And there are a couple other ways to solve this, but this is probably the most traditional way of doing that.', 'start': 1732.445, 'duration': 4.584}, {'end': 1746.313, 'text': 'How can you select k for k-means? So first you better understand what k-means is and that k is the number of different groupings.', 'start': 1737.469, 'duration': 8.844}, {'end': 1750.535, 'text': 'And most commonly we use is the ELBO method to select k for k-means.', 'start': 1746.593, 'duration': 3.942}, {'end': 1755.978, 'text': 'The idea of the ELBO method is to run k-means clustering on the data set where k is the number of clusters.', 'start': 1750.835, 'duration': 5.143}, {'end': 1763.301, 'text': 'Within the sum of squares, WSS is defined as the sum of the squared distance between each member of the cluster and its centroid.', 'start': 1756.278, 'duration': 7.023}, {'end': 1766.442, 'text': 'And you should know all the terms for your k-means on there.', 'start': 1763.701, 'duration': 2.741}, {'end': 1769.863, 'text': "And with the elbow point, and again, here's our iteration in our code.", 'start': 1766.622, 'duration': 3.241}], 'summary': '68% chance of weekend rain, k-means uses elbo method to select k', 'duration': 43.542, 'max_score': 1726.321, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1726321.jpg'}, {'end': 1821.844, 'src': 'embed', 'start': 1789.329, 'weight': 3, 'content': [{'end': 1792.112, 'text': 'What is the significance of p-value? Oh, good one.', 'start': 1789.329, 'duration': 2.783}, {'end': 1794.975, 'text': "Especially if you're dealing with r, because that's the first thing that pops up.", 'start': 1792.292, 'duration': 2.683}, {'end': 1803.684, 'text': 'p-value, typically less than or equal to 0.05, indicates a strong evidence against the null hypothesis.', 'start': 1795.235, 'duration': 8.449}, {'end': 1808.008, 'text': 'And you should know why we use null hypothesis instead of the hypothesis.', 'start': 1803.884, 'duration': 4.124}, {'end': 1810.371, 'text': 'So you reject the null hypothesis.', 'start': 1808.228, 'duration': 2.143}, {'end': 1816.518, 'text': 'Very important that term null hypothesis in any scientific setup and also in data science.', 'start': 1810.791, 'duration': 5.727}, {'end': 1821.844, 'text': "It doesn't mean that it's true, it means that there's a high correlation that it's true.", 'start': 1816.899, 'duration': 4.945}], 'summary': 'A p-value ≤ 0.05 provides strong evidence against the null hypothesis in statistical analysis, particularly in r and data science.', 'duration': 32.515, 'max_score': 1789.329, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1789329.jpg'}, {'end': 1875.718, 'src': 'embed', 'start': 1853.583, 'weight': 4, 'content': [{'end': 1861.627, 'text': "you can use that p-value on different features to decide whether you're going to include your features as far as something worth exploring in your data science model.", 'start': 1853.583, 'duration': 8.044}, {'end': 1865.356, 'text': 'How can outlier values be treated? Ooh, good one.', 'start': 1861.828, 'duration': 3.528}, {'end': 1868.537, 'text': 'You can drop outliers only if it is a garbage value.', 'start': 1865.576, 'duration': 2.961}, {'end': 1873.238, 'text': "So sometimes you end up with like one outlier that just is probably someone's measurements way off.", 'start': 1868.637, 'duration': 4.601}, {'end': 1875.718, 'text': 'Height of an adult equals ABC feet.', 'start': 1873.458, 'duration': 2.26}], 'summary': 'Using p-value for feature selection, treating outliers, and handling garbage values in data science models.', 'duration': 22.135, 'max_score': 1853.583, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1853583.jpg'}, {'end': 1975.281, 'src': 'embed', 'start': 1945.87, 'weight': 5, 'content': [{'end': 1948.251, 'text': 'And this graphic example is very easy to see.', 'start': 1945.87, 'duration': 2.381}, {'end': 1956.214, 'text': 'The variance is constant with time, so we have our first variable y and x, and x being the time factor and y being the variable.', 'start': 1948.271, 'duration': 7.943}, {'end': 1959.075, 'text': 'As you can see, it goes through the same values all the time.', 'start': 1956.254, 'duration': 2.821}, {'end': 1961.896, 'text': "It's not changing in the long period of time.", 'start': 1959.275, 'duration': 2.621}, {'end': 1962.856, 'text': "So that's stationary.", 'start': 1961.976, 'duration': 0.88}, {'end': 1967.798, 'text': "And then you can see in the second example, the waves get bigger and bigger, so that's non-stationary.", 'start': 1963.036, 'duration': 4.762}, {'end': 1969.479, 'text': 'Here the variance is changing with time.', 'start': 1967.878, 'duration': 1.601}, {'end': 1975.281, 'text': "Again, we have y, which stays constant, so that if you look at the bigger picture, it's the same wave over and over again.", 'start': 1969.659, 'duration': 5.622}], 'summary': 'Two examples: one stationary, one non-stationary, showing constant vs changing variance with time.', 'duration': 29.411, 'max_score': 1945.87, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1945870.jpg'}], 'start': 1609.878, 'title': 'Rmse vs mse and p-value', 'summary': 'Covers the difference between rmse and mse in linear regression, their formulas, significance, and practical applications. it also explains the significance of p-value in rejecting the null hypothesis, treating outlier values, and determining stationary time series data.', 'chapters': [{'end': 1788.949, 'start': 1609.878, 'title': 'Rmse vs mse in linear regression', 'summary': 'Explains the difference between rmse and mse in linear regression, their formulas, significance, and practical applications. it also covers the probability calculation for rain on the weekend and the selection of k for k-means clustering using the elbo method.', 'duration': 179.071, 'highlights': ['The RMSE and the MSE are the two of the most common measures of accuracy for a linear regression model. The RMSE and MSE are widely used to measure the accuracy of linear regression models.', 'The probability of rain on the weekend is 68% using the traditional method of calculation. The traditional method yields a 68% probability of rain on the weekend.', 'The ELBO method is commonly used to select k for k-means clustering. The ELBO method is frequently employed to determine the number of clusters for k-means clustering.']}, {'end': 1983.244, 'start': 1789.329, 'title': 'Understanding p-value and outlier treatment', 'summary': 'Explains the significance of p-value in rejecting the null hypothesis, treating outlier values, and determining stationary time series data.', 'duration': 193.915, 'highlights': ['The significance of p-value is explained, with a p-value typically less than or equal to 0.05 indicating strong evidence against the null hypothesis. p-value less than or equal to 0.05', 'Treatment of outlier values is discussed, including the option to drop outliers if they are extreme or do not align with the data distribution. outliers with extreme values', 'Determining stationary time series data is defined as having constant variance and mean with time. constant variance and mean']}], 'duration': 373.366, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1609878.jpg', 'highlights': ['The RMSE and the MSE are the two of the most common measures of accuracy for a linear regression model.', 'The probability of rain on the weekend is 68% using the traditional method of calculation.', 'The ELBO method is commonly used to select k for k-means clustering.', 'The significance of p-value is explained, with a p-value typically less than or equal to 0.05 indicating strong evidence against the null hypothesis.', 'Treatment of outlier values is discussed, including the option to drop outliers if they are extreme or do not align with the data distribution.', 'Determining stationary time series data is defined as having constant variance and mean with time.']}, {'end': 2404.38, 'segs': [{'end': 2114.787, 'src': 'embed', 'start': 2081.06, 'weight': 0, 'content': [{'end': 2084.561, 'text': 'Write the equation and calculate precision and recall rate.', 'start': 2081.06, 'duration': 3.501}, {'end': 2089.763, 'text': 'And so continuing with our confusion matrix, I was just talking about the different domains.', 'start': 2084.94, 'duration': 4.823}, {'end': 2093.664, 'text': 'We have the precision equals 262 over 277.', 'start': 2089.922, 'duration': 3.742}, {'end': 2098.266, 'text': 'So your precision is the true positive over the true positive plus false positive.', 'start': 2093.664, 'duration': 4.602}, {'end': 2102.647, 'text': 'And the recall rate is your true positive over the total positive plus false negative.', 'start': 2098.446, 'duration': 4.201}, {'end': 2104.228, 'text': 'And you can see here we have the 262 over 277 equals a 94%.', 'start': 2102.807, 'duration': 1.421}, {'end': 2114.787, 'text': 'And the recall over here is the 262 over 280, which equals 0.9 or 90%.', 'start': 2104.228, 'duration': 10.559}], 'summary': 'Precision rate is 94% and recall rate is 90%.', 'duration': 33.727, 'max_score': 2081.06, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2081060.jpg'}, {'end': 2215.106, 'src': 'embed', 'start': 2187.112, 'weight': 2, 'content': [{'end': 2190.654, 'text': 'Recommendation engine is done with collaborative filtering.', 'start': 2187.112, 'duration': 3.542}, {'end': 2197.017, 'text': 'Collaborative filtering exploits the behavior of other users and their purchase history in terms of ratings, selection, etc.', 'start': 2190.914, 'duration': 6.103}, {'end': 2201.839, 'text': 'It makes predictions on what you might interest a person based on the preference of many other users.', 'start': 2197.137, 'duration': 4.702}, {'end': 2205.021, 'text': 'In this algorithm, features of the items are not known.', 'start': 2202.12, 'duration': 2.901}, {'end': 2208.723, 'text': 'And we have a nice example here where they took a snapshot of a sales page.', 'start': 2205.141, 'duration': 3.582}, {'end': 2215.106, 'text': 'It says, for example, suppose X number of people buy a new phone and then also buy tempered glass with it.', 'start': 2208.763, 'duration': 6.343}], 'summary': "Recommendation engine uses collaborative filtering to predict user interests based on others' behavior and purchase history.", 'duration': 27.994, 'max_score': 2187.112, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2187112.jpg'}, {'end': 2272.951, 'src': 'embed', 'start': 2244.588, 'weight': 4, 'content': [{'end': 2250.552, 'text': 'I remember back in the 90s it was so important to know SQL query and only a few people got it.', 'start': 2244.588, 'duration': 5.964}, {'end': 2252.454, 'text': "Nowadays it's just part of your kit.", 'start': 2250.812, 'duration': 1.642}, {'end': 2254.475, 'text': 'You have to know some basic SQL.', 'start': 2252.514, 'duration': 1.961}, {'end': 2259.299, 'text': 'So write a basic SQL query to list all orders with customer information.', 'start': 2254.555, 'duration': 4.744}, {'end': 2261.58, 'text': 'And you can kind of make up your own name for the database.', 'start': 2259.499, 'duration': 2.081}, {'end': 2263.922, 'text': 'And you can pause it here if you want to write that down on a paper.', 'start': 2261.86, 'duration': 2.062}, {'end': 2264.623, 'text': "and let's go ahead.", 'start': 2264.082, 'duration': 0.541}, {'end': 2265.063, 'text': 'look at this.', 'start': 2264.623, 'duration': 0.44}, {'end': 2272.951, 'text': 'we have to list all orders with customer information, and so usually you have an order table and a customer table and you have an order ID,', 'start': 2265.063, 'duration': 7.888}], 'summary': "In the 90s, sql was important; now it's basic knowledge. a sql query is needed to list all orders with customer information.", 'duration': 28.363, 'max_score': 2244.588, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2244588.jpg'}, {'end': 2397.698, 'src': 'heatmap', 'start': 2317.544, 'weight': 1, 'content': [{'end': 2320.688, 'text': "That's one of the standard data sets on there is for cancer detection.", 'start': 2317.544, 'duration': 3.144}, {'end': 2323.729, 'text': 'Cancer detection results in imbalanced data.', 'start': 2320.848, 'duration': 2.881}, {'end': 2325.549, 'text': 'In an imbalanced data set.', 'start': 2324.049, 'duration': 1.5}, {'end': 2332.011, 'text': 'accuracy should not be based as a measure of performance, because it is important to focus on the remaining 4%,', 'start': 2325.549, 'duration': 6.462}, {'end': 2334.492, 'text': 'which are the people who were wrongly diagnosed.', 'start': 2332.011, 'duration': 2.481}, {'end': 2336.112, 'text': 'We talked a little bit about this earlier.', 'start': 2334.792, 'duration': 1.32}, {'end': 2337.392, 'text': 'You have to know your domain.', 'start': 2336.132, 'duration': 1.26}, {'end': 2341.313, 'text': 'This is the medical cancer domain versus weather domain.', 'start': 2337.932, 'duration': 3.381}, {'end': 2343.834, 'text': 'Weather channel, they can get by with 50% wrong.', 'start': 2341.593, 'duration': 2.241}, {'end': 2347.755, 'text': "In cancer, you don't want 4% of the people being wrongly diagnosed.", 'start': 2344.054, 'duration': 3.701}, {'end': 2354.422, 'text': 'Wrong diagnosis is of a major concern because there can be people who have cancer but were not predicted so.', 'start': 2347.995, 'duration': 6.427}, {'end': 2358.365, 'text': 'In an in-balance data set, accuracy should not be used as a measurement performance.', 'start': 2354.662, 'duration': 3.703}, {'end': 2366.594, 'text': 'Which of the following machine learning algorithm can be used for inputting missing values of both categorical and continuous variables?', 'start': 2358.766, 'duration': 7.828}, {'end': 2368.356, 'text': 'And so we have a couple choices here.', 'start': 2367.074, 'duration': 1.282}, {'end': 2374.524, 'text': 'We have k-means clustering, we have linear regression, we have the k-NN, nearest neighbor, and decision tree.', 'start': 2368.396, 'duration': 6.128}, {'end': 2382.075, 'text': 'And which of the following machine learning algorithms can be used for inputting missing values of both categorical and continuous variables?', 'start': 2374.725, 'duration': 7.35}, {'end': 2385.316, 'text': 'Now, certainly you can use some pre-processing to do some of that,', 'start': 2382.415, 'duration': 2.901}, {'end': 2391.557, 'text': "but you should have gone with the K nearest neighbor because it can compute the nearest neighbor and if it doesn't have the value,", 'start': 2385.316, 'duration': 6.241}, {'end': 2397.698, 'text': "it just computes the nearest neighbor based on all the other features where, when you're dealing with K-means, clustering or linear regression,", 'start': 2391.557, 'duration': 6.141}], 'summary': 'In cancer detection, accuracy is not the best measure; k-nearest neighbor can handle missing values effectively.', 'duration': 80.154, 'max_score': 2317.544, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2317544.jpg'}, {'end': 2382.075, 'src': 'embed', 'start': 2358.766, 'weight': 3, 'content': [{'end': 2366.594, 'text': 'Which of the following machine learning algorithm can be used for inputting missing values of both categorical and continuous variables?', 'start': 2358.766, 'duration': 7.828}, {'end': 2368.356, 'text': 'And so we have a couple choices here.', 'start': 2367.074, 'duration': 1.282}, {'end': 2374.524, 'text': 'We have k-means clustering, we have linear regression, we have the k-NN, nearest neighbor, and decision tree.', 'start': 2368.396, 'duration': 6.128}, {'end': 2382.075, 'text': 'And which of the following machine learning algorithms can be used for inputting missing values of both categorical and continuous variables?', 'start': 2374.725, 'duration': 7.35}], 'summary': 'Various machine learning algorithms for handling missing values', 'duration': 23.309, 'max_score': 2358.766, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2358766.jpg'}], 'start': 1983.444, 'title': 'Accuracy calculation and collaborative filtering', 'summary': 'Covers accuracy calculation using confusion matrix, precision, and recall rates. it also discusses collaborative filtering for recommendation engines, emphasizes the significance of sql knowledge, and mentions handling missing values with k-nearest neighbor in machine learning.', 'chapters': [{'end': 2186.791, 'start': 1983.444, 'title': 'Calculating accuracy with confusion matrix', 'summary': 'Explains the calculation of accuracy using a confusion matrix, highlighting the importance of precision and recall rates, and concludes with a brain teaser about sock colors. it also emphasizes the significance of accuracy in different domains, such as medical diagnosis and risk assessment.', 'duration': 203.347, 'highlights': ['The chapter emphasizes the importance of precision and recall rates in the context of calculating accuracy using a confusion matrix. Precision and recall rates are calculated to be 94% and 90% respectively, showcasing the significance of these metrics in evaluating the performance of predictive models.', 'The chapter provides insights into the importance of accuracy in different domains, such as medical diagnosis and risk assessment, by highlighting the potential consequences of false positives and false negatives. It underscores the critical impact of accurate predictions, especially in life-critical scenarios like medical diagnoses or nuclear reactor risk assessment.', 'The chapter presents a brain teaser about the minimum number of sock pulls required to ensure a matching pair, illustrating problem-solving skills and critical thinking. It showcases the application of logical deduction in a fun and engaging manner, demonstrating the ability to solve complex problems outside the realm of data analysis.']}, {'end': 2404.38, 'start': 2187.112, 'title': 'Collaborative filtering and sql basics', 'summary': 'Discusses collaborative filtering for recommendation engines, the importance of sql knowledge, and the drawbacks of using accuracy as a measure of performance in imbalanced datasets, along with a mention of k-nearest neighbor for handling missing values in machine learning algorithms.', 'duration': 217.268, 'highlights': ['The importance of SQL knowledge is emphasized, with SQL query writing and its relevance in listing orders and customer information discussed. The chapter highlights the significance of SQL knowledge, emphasizing the importance of writing basic SQL queries and understanding its relevance in listing orders and customer information.', 'The drawbacks of using accuracy as a measure of performance in imbalanced datasets are discussed, with an emphasis on the importance of focusing on the remaining 4% of cases and the implications in the medical domain. It emphasizes the drawbacks of using accuracy as a measure of performance in imbalanced datasets, particularly in the medical domain, where the focus should be on the remaining 4% of cases.', 'Collaborative filtering for recommendation engines is explained, with an example of how it makes predictions based on the behavior of other users and their purchase history. The chapter explains collaborative filtering for recommendation engines, highlighting how it makes predictions based on the behavior of other users and their purchase history.', 'The use of K-nearest neighbor for handling missing values of both categorical and continuous variables in machine learning algorithms is mentioned, with an explanation of its computation of the nearest neighbor based on other features. It mentions the use of K-nearest neighbor for handling missing values in machine learning algorithms, explaining its computation of the nearest neighbor based on other features.']}], 'duration': 420.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE1983444.jpg', 'highlights': ['Precision and recall rates are calculated to be 94% and 90% respectively, showcasing the significance of these metrics in evaluating the performance of predictive models.', 'The chapter emphasizes the drawbacks of using accuracy as a measure of performance in imbalanced datasets, particularly in the medical domain, where the focus should be on the remaining 4% of cases.', 'Collaborative filtering for recommendation engines is explained, with an example of how it makes predictions based on the behavior of other users and their purchase history.', 'The use of K-nearest neighbor for handling missing values of both categorical and continuous variables in machine learning algorithms is mentioned, with an explanation of its computation of the nearest neighbor based on other features.', 'The importance of SQL knowledge is emphasized, with SQL query writing and its relevance in listing orders and customer information discussed.']}, {'end': 2839.62, 'segs': [{'end': 2475.901, 'src': 'embed', 'start': 2450.087, 'weight': 0, 'content': [{'end': 2456.614, 'text': 'Now light the other end of B also so that the remaining part of B will burn, taking 15 minutes to burn.', 'start': 2450.087, 'duration': 6.527}, {'end': 2458.516, 'text': 'This we have gotten 30 plus 15 equals 45 minutes.', 'start': 2456.774, 'duration': 1.742}, {'end': 2461.117, 'text': 'Excellent solution.', 'start': 2460.417, 'duration': 0.7}, {'end': 2467.399, 'text': "Mine, which I like, was to take one rope, fold it in two, so we know it's a half hour.", 'start': 2461.297, 'duration': 6.102}, {'end': 2475.901, 'text': "take the other rope, fold it in four places, so we know that that one's 15 minutes and then you can just connect the two and burn it straight across.", 'start': 2467.399, 'duration': 8.502}], 'summary': 'One rope burns for 30 minutes, another for 15 minutes, totaling 45 minutes to burn both ends.', 'duration': 25.814, 'max_score': 2450.087, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2450087.jpg'}, {'end': 2512.007, 'src': 'embed', 'start': 2483.863, 'weight': 2, 'content': [{'end': 2489.009, 'text': 'Below are the eight actual values of target variable in the train file.', 'start': 2483.863, 'duration': 5.146}, {'end': 2492.853, 'text': 'So we have a training file, not to be confused with the train on the tracks.', 'start': 2489.309, 'duration': 3.544}, {'end': 2495.689, 'text': 'We have 0, 0, 0, 1, 1, 1, 1, 1.', 'start': 2492.993, 'duration': 2.696}, {'end': 2502.764, 'text': 'What is the entropy of the target variable? We mentioned earlier that you should know your entropy and how to calculate the entropy.', 'start': 2495.696, 'duration': 7.068}, {'end': 2506.105, 'text': 'What is the entropy of the target variable? So we have a couple options here.', 'start': 2503.024, 'duration': 3.081}, {'end': 2512.007, 'text': 'We have minus 5 over 8, logarithm of 5 over 8, plus 3 over 8, logarithm of 3 over 8.', 'start': 2506.165, 'duration': 5.842}], 'summary': 'Entropy of target variable is -0.954.', 'duration': 28.144, 'max_score': 2483.863, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2483863.jpg'}, {'end': 2606.527, 'src': 'embed', 'start': 2581.715, 'weight': 3, 'content': [{'end': 2589.859, 'text': 'We want to predict the probability of death from heart disease based on three risk factors, age, gender, and blood cholesterol level.', 'start': 2581.715, 'duration': 8.144}, {'end': 2596.102, 'text': 'What is the most appropriate algorithm for this case? So we have three features, and we want to know the predictability of death.', 'start': 2589.999, 'duration': 6.103}, {'end': 2598.063, 'text': 'OK, a little morbid there.', 'start': 2596.682, 'duration': 1.381}, {'end': 2599.384, 'text': 'Choose the right algorithm.', 'start': 2598.223, 'duration': 1.161}, {'end': 2606.527, 'text': 'Do we want to use logistic regression for this linear regression, k-means, clustering or the APRORIA algorithm?', 'start': 2599.584, 'duration': 6.943}], 'summary': 'Predict probability of death from heart disease based on 3 risk factors: age, gender, and cholesterol level.', 'duration': 24.812, 'max_score': 2581.715, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2581715.jpg'}, {'end': 2616.835, 'src': 'heatmap', 'start': 2581.715, 'weight': 0.876, 'content': [{'end': 2589.859, 'text': 'We want to predict the probability of death from heart disease based on three risk factors, age, gender, and blood cholesterol level.', 'start': 2581.715, 'duration': 8.144}, {'end': 2596.102, 'text': 'What is the most appropriate algorithm for this case? So we have three features, and we want to know the predictability of death.', 'start': 2589.999, 'duration': 6.103}, {'end': 2598.063, 'text': 'OK, a little morbid there.', 'start': 2596.682, 'duration': 1.381}, {'end': 2599.384, 'text': 'Choose the right algorithm.', 'start': 2598.223, 'duration': 1.161}, {'end': 2606.527, 'text': 'Do we want to use logistic regression for this linear regression, k-means, clustering or the APRORIA algorithm?', 'start': 2599.584, 'duration': 6.943}, {'end': 2611.191, 'text': 'And if you selected logistic regression, then you probably got the right answer.', 'start': 2606.867, 'duration': 4.324}, {'end': 2616.835, 'text': 'Linear regression, remember, deals with, like, you take your line and draw a line through the data.', 'start': 2611.591, 'duration': 5.244}], 'summary': 'Predict probability of death from heart disease based on age, gender, and cholesterol level using logistic regression.', 'duration': 35.12, 'max_score': 2581.715, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2581715.jpg'}, {'end': 2697.761, 'src': 'embed', 'start': 2652.556, 'weight': 4, 'content': [{'end': 2655.997, 'text': "But let's take a look at some of the different algorithms we might use on this.", 'start': 2652.556, 'duration': 3.441}, {'end': 2661.619, 'text': 'We have k-means clustering, linear regression, association rules, and decision trees.', 'start': 2656.257, 'duration': 5.362}, {'end': 2663.68, 'text': "And I'll give you a hint.", 'start': 2662.319, 'duration': 1.361}, {'end': 2671.466, 'text': "We're looking for grouping people together by similarities and by four different similarities, so very specific.", 'start': 2663.98, 'duration': 7.486}, {'end': 2674.488, 'text': 'They gave you one of the values, specifically the k value.', 'start': 2671.666, 'duration': 2.822}, {'end': 2677.67, 'text': 'So k means clustering would be great for this particular problem.', 'start': 2674.788, 'duration': 2.882}, {'end': 2690.119, 'text': 'You have run the association rules algorithm on your data set and the two rules banana apple is associated with grape and apple orange is associated with grape have been found to be relevant.', 'start': 2677.915, 'duration': 12.204}, {'end': 2694.98, 'text': 'What else must be true? So this would challenge you to understand association rules.', 'start': 2690.519, 'duration': 4.461}, {'end': 2696.401, 'text': 'You could picture.', 'start': 2695.26, 'duration': 1.141}, {'end': 2697.761, 'text': 'in this particular one,', 'start': 2696.401, 'duration': 1.36}], 'summary': 'Using k-means clustering for grouping people by 4 specific similarities with given k value.', 'duration': 45.205, 'max_score': 2652.556, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2652556.jpg'}, {'end': 2802.632, 'src': 'embed', 'start': 2776.956, 'weight': 6, 'content': [{'end': 2781.84, 'text': "So you want to know which method should you use to see if the coupon's valid for their purchase.", 'start': 2776.956, 'duration': 4.884}, {'end': 2785.223, 'text': "Well, we're not clustering and we're not associating things together.", 'start': 2782.141, 'duration': 3.082}, {'end': 2787.205, 'text': 'We want to know the end result.', 'start': 2785.323, 'duration': 1.882}, {'end': 2791.248, 'text': 'Student T-Test also drawing that little T in boxes and switching them around.', 'start': 2787.425, 'duration': 3.823}, {'end': 2795.089, 'text': "There's really only one answer that works in here, and that's the one-way ANOVA.", 'start': 2791.328, 'duration': 3.761}, {'end': 2796.85, 'text': 'So that draws us to an end.', 'start': 2795.41, 'duration': 1.44}, {'end': 2798.751, 'text': 'I want to thank you for joining us today.', 'start': 2796.87, 'duration': 1.881}, {'end': 2802.632, 'text': 'For more information, visit www.simplylearn.com.', 'start': 2798.951, 'duration': 3.681}], 'summary': 'Use one-way anova to determine coupon validity.', 'duration': 25.676, 'max_score': 2776.956, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2776956.jpg'}], 'start': 2404.6, 'title': 'Measuring time and algorithm selection', 'summary': 'Presents a riddle for measuring 45 minutes with non-uniform ropes, offering two solutions. it also discusses entropy calculation, algorithm selection for predicting death from heart disease, k-means clustering, association rules, and the impact of coupons using one-way anova.', 'chapters': [{'end': 2483.743, 'start': 2404.6, 'title': 'Measure 45 minutes with non-uniform ropes', 'summary': 'Presents a riddle involving measuring 45 minutes using non-uniform ropes, with one rope taking 60 minutes to burn, offering two different solutions including burning ropes from both ends and folding ropes to measure distinct time intervals.', 'duration': 79.143, 'highlights': ['Burning ropes from both ends and then lighting the other end of one rope to measure 45 minutes, with one rope taking 60 minutes to burn and the other 15 minutes, offering a total of 45 minutes.', 'Folding one rope in two to measure 30 minutes and folding the other rope in four places to measure 15 minutes, then connecting and burning them together, providing a solution based on distinct intervals of time.']}, {'end': 2839.62, 'start': 2483.863, 'title': 'Entropy calculation and algorithm selection', 'summary': 'Discusses calculating entropy for a target variable, selecting the most appropriate algorithm for predicting death from heart disease, identifying similar user groups using k-means clustering, understanding association rules, and determining the impact of offering coupons using one-way anova.', 'duration': 355.757, 'highlights': ['The chapter explains the process of calculating the entropy of a target variable by considering the number of ones and zeros in the dataset and provides the formula for entropy calculation, emphasizing the importance of knowing the entropy for data analysis.', 'It discusses selecting the most appropriate algorithm for predicting the probability of death from heart disease based on three risk factors, highlighting the suitability of logistic regression for this specific case due to the ability to mix different factors in buckets.', "It emphasizes the use of k-means clustering for identifying users with similar characteristics, particularly in the context of grouping individuals based on specific similarities, and provides a hint about the relevance of the 'k' value in this scenario.", 'The chapter explains the concept of association rules and provides an example related to shopping behavior to illustrate the understanding of relevant rules and frequent item sets, highlighting the importance of comprehending association rules in data analysis.', "It discusses the use of one-way ANOVA as the appropriate analysis method for determining the impact of offering coupons on visitors' purchase decisions and provides a clear explanation for why other methods such as clustering or association rules are not suitable for this specific analysis."]}], 'duration': 435.02, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5JZsSNLXXuE/pics/5JZsSNLXXuE2404600.jpg', 'highlights': ['Burning ropes from both ends to measure 45 minutes, with one rope taking 60 minutes and the other 15 minutes', 'Folding one rope in two to measure 30 minutes and folding the other rope in four places to measure 15 minutes', 'Explaining the process of calculating the entropy of a target variable and providing the formula for entropy calculation', 'Discussing the selection of the most appropriate algorithm for predicting the probability of death from heart disease', 'Emphasizing the use of k-means clustering for identifying users with similar characteristics', 'Explaining the concept of association rules and providing an example related to shopping behavior', "Discussing the use of one-way ANOVA as the appropriate analysis method for determining the impact of offering coupons on visitors' purchase decisions"]}], 'highlights': ['Logical interview question solution: Using two buckets of 3 and 5 liters to measure exactly 4 liters, demonstrating thinking outside the box.', 'Fundamental breakdown of supervised and unsupervised learning: Details key differences, use of labeled and unlabeled data, feedback mechanisms, and common algorithms.', 'Supervised learning algorithms: Lists commonly used algorithms such as decision tree, logistic regression, and support vector machine, highlighting their significance.', 'Unsupervised learning algorithms: Lists commonly used algorithms like k-means and hierarchical clustering, emphasizing their importance and usage.', 'Decision tree creation: Involves calculating entropy and information gain to choose the attribute with the highest information gain as the root node.', 'Building a random forest model: Includes randomly selecting K features from a total of M features, calculating the node D using the best split point, and building forests to create n number of trees.', 'Methods to avoid overfitting: Keeping model simple, using cross-validation techniques, using regularization techniques.', 'Describing the data using mean, median, mode, dispersion, and range to predict house prices based on features like number of bedrooms, floors, and square footage.', 'The data involves three or more variables, categorized as multivariate, similar to bivariate but with more than one dependent variable.', "The chapter discusses feature selection methods including filter and wrapper methods, and how to handle missing data values using Python's Panda data frame.", 'Dimension reduction compresses data, reducing storage space and computation time (1.4M features).', 'Less dimensions lead to less computing and remove redundant features (4.8M features).', 'Text analysis example illustrates the need to reduce the list and processing load (1.4M features).', 'The RMSE and the MSE are the two of the most common measures of accuracy for a linear regression model.', 'Precision and recall rates are calculated to be 94% and 90% respectively, showcasing the significance of these metrics in evaluating the performance of predictive models.', 'The chapter emphasizes the drawbacks of using accuracy as a measure of performance in imbalanced datasets, particularly in the medical domain, where the focus should be on the remaining 4% of cases.', 'Collaborative filtering for recommendation engines is explained, with an example of how it makes predictions based on the behavior of other users and their purchase history.']}