title
Data Science Hands-On Crash Course

description
Learn the basics of Data Science in the crash course. You will learn about the theory and code behind the most common algorithms used in data science. ✏️ Course created by Marco Peixeiro. Check out his channel: https://www.youtube.com/channel/UC-0lpiwlftqwC7znCcF83qg 💻 Code: https://github.com/marcopeix/datasciencewithmarco 💻 Datasets: https://github.com/marcopeix/datasciencewithmarco/tree/master/data ⭐️ Course Contents ⭐️ ⌨️ (00:00) Introduction ⌨️ (03:06) Setup ⌨️ (04:29) Linear regression (theory) ⌨️ (09:29) Linear regression (Python) ⌨️ (20:59) Classification (theory) ⌨️ (30:16) Classifiaction (Python) ⌨️ (49:30) Resampling & regularization (theory) ⌨️ (56:09) Resampling and regularization (Python) ⌨️ (1:05:17) Decision trees (theory) ⌨️ (1:13:12) Decision trees (Python) ⌨️ (1:24:50) SVM (theory) ⌨️ (1:28:17) SVM (Python) ⌨️ (1:58:24) Unsupervised learning (theory) ⌨️ (2:06:54) Unsupervised learning (Python) ⌨️ (2:20:55) Conclusion ⭐️ Special thanks to our Champion supporters! ⭐️ 🏆 Loc Do 🏆 Joseph C 🏆 DeezMaster Become a supporter: https://www.youtube.com/freecodecamp/join -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

detail
{'title': 'Data Science Hands-On Crash Course', 'heatmap': [{'end': 426.075, 'start': 243.148, 'weight': 1}, {'end': 1019.218, 'start': 925.715, 'weight': 0.729}, {'end': 1356.315, 'start': 1267.24, 'weight': 0.723}, {'end': 8468.923, 'start': 8376.939, 'weight': 0.996}], 'summary': 'Course covers a comprehensive data science crash course, including machine learning, linear regression with r-squared value of 0.897, logistic regression, mushroom classification achieving auc of 1, resampling, decision trees, svm achieving 98.9% test accuracy, and unsupervised learning with pca and clustering for data visualization.', 'chapters': [{'end': 234.803, 'segs': [{'end': 59.364, 'src': 'embed', 'start': 35.339, 'weight': 0, 'content': [{'end': 41.924, 'text': 'We will talk about resampling and regularization methods, which are very important in any workflow for a data scientist.', 'start': 35.339, 'duration': 6.585}, {'end': 44.487, 'text': 'We will talk about decision trees.', 'start': 42.705, 'duration': 1.782}, {'end': 51.415, 'text': 'Most of the state-of-the-art algorithms are actually tree-based methods, so a very exciting subject.', 'start': 45.308, 'duration': 6.107}, {'end': 59.364, 'text': 'And then we will move on to support vector machines and conclude this crash course with unsupervised learning.', 'start': 52.075, 'duration': 7.289}], 'summary': 'Discussing resampling, regularization, decision trees, svm, and unsupervised learning.', 'duration': 24.025, 'max_score': 35.339, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ35339.jpg'}, {'end': 127.823, 'src': 'embed', 'start': 82.375, 'weight': 4, 'content': [{'end': 89.798, 'text': 'So you can be a machine learning scientist or engineer, which means that you are the person who develops the algorithms.', 'start': 82.375, 'duration': 7.423}, {'end': 95.119, 'text': 'You could also be a data analyst, which is the person who answers business questions.', 'start': 90.677, 'duration': 4.442}, {'end': 104.024, 'text': 'For example, what is the product that we sold the most in the last month? That would be the job of the data analyst.', 'start': 95.4, 'duration': 8.624}, {'end': 110.688, 'text': 'Or, finally, you could be a data engineer, which is the person that builds the software to gather data from different sources,', 'start': 104.044, 'duration': 6.644}, {'end': 116.393, 'text': 'because usually the data needed to solve a problem is not in the same place.', 'start': 110.688, 'duration': 5.705}, {'end': 118.094, 'text': 'so those data engineers,', 'start': 116.393, 'duration': 1.701}, {'end': 127.823, 'text': 'they gather the data from everywhere and put it in a format that can be used after by the data analyst or the machine learning engineer.', 'start': 118.094, 'duration': 9.729}], 'summary': 'Roles in data industry: ml scientist, data analyst, data engineer', 'duration': 45.448, 'max_score': 82.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ82375.jpg'}, {'end': 198.531, 'src': 'embed', 'start': 152.874, 'weight': 2, 'content': [{'end': 158.858, 'text': 'because understanding that is actually way harder than coding the algorithm itself, as you will see later on.', 'start': 152.874, 'duration': 5.984}, {'end': 167.606, 'text': 'Also, if your goal is to land a job as a data scientist in a company, Well, you will be asked theoretical questions during the interview.', 'start': 159.519, 'duration': 8.087}, {'end': 170.348, 'text': "So a very important part, please don't skip it.", 'start': 167.926, 'duration': 2.422}, {'end': 176.615, 'text': 'And of course, each algorithm section will be accompanied by hands-on examples in Python.', 'start': 171.189, 'duration': 5.426}, {'end': 182.541, 'text': 'So for each algorithm, we will download a dataset and we will apply this algorithm on that dataset.', 'start': 176.955, 'duration': 5.586}, {'end': 185.838, 'text': "So without further ado, let's get started with the setup.", 'start': 183.376, 'duration': 2.462}, {'end': 189.062, 'text': "All right, let's get you set up to do some data science.", 'start': 186.559, 'duration': 2.503}, {'end': 192.185, 'text': 'Head over to Google and search for Anaconda.', 'start': 189.642, 'duration': 2.543}, {'end': 198.531, 'text': "Then click on the first result, which should be Anaconda, the world's most popular data science platform.", 'start': 192.665, 'duration': 5.866}], 'summary': 'Understanding theoretical concepts is crucial for data science job interviews, and practical examples in python will be provided for each algorithm.', 'duration': 45.657, 'max_score': 152.874, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ152874.jpg'}], 'start': 0.289, 'title': 'Data science and machine learning', 'summary': 'Covers a data science crash course, including coding setup and topics such as linear regression, classification, resampling, and decision trees. it also discusses the professions in data science, emphasizing machine learning, data analysis, and engineering roles. furthermore, it focuses on the theory and practical application of machine learning algorithms in python using anaconda.', 'chapters': [{'end': 59.364, 'start': 0.289, 'title': 'Data science crash course', 'summary': 'Covers an overview of a data science crash course, including a definition of data science, setup for coding, and topics such as linear regression, classification, resampling, regularization methods, decision trees, support vector machines, and unsupervised learning.', 'duration': 59.075, 'highlights': ['The chapter covers an overview of a data science crash course, including a definition of data science, setup for coding, and topics such as linear regression, classification, resampling, regularization methods, decision trees, support vector machines, and unsupervised learning.', 'The course will cover algorithms such as linear regression, logistic regression, LEA, QDA, decision trees, support vector machines, and unsupervised learning.', 'Resampling and regularization methods will be discussed, which are essential in any workflow for a data scientist.']}, {'end': 127.823, 'start': 60.748, 'title': 'Understanding data science professions', 'summary': 'Discusses the broad field of data science, encompassing three professions: machine learning scientist or engineer, data analyst, and data engineer, each with specific roles in developing algorithms, answering business questions, and gathering data from different sources.', 'duration': 67.075, 'highlights': ['Data science encompasses three professions: machine learning scientist or engineer, data analyst, and data engineer, each with specific roles in the field. (relevance: 5)', 'Machine learning scientists or engineers develop algorithms in the field of data science. (relevance: 4)', 'Data analysts are responsible for answering business questions using data, such as identifying the best-selling product in a given time period. (relevance: 3)', 'Data engineers gather data from various sources and organize it for use by data analysts or machine learning engineers. (relevance: 2)']}, {'end': 234.803, 'start': 127.823, 'title': 'Machine learning crash course', 'summary': 'Focuses on the importance of theory in machine learning, the necessity of understanding the behavior of models, and practical hands-on application of algorithms in python, emphasizing the setup process using anaconda.', 'duration': 106.98, 'highlights': ['Theoretical understanding is crucial in data science, as it provides insights into model behavior and is essential for job interviews.', 'Practical application of algorithms in Python is emphasized, with hands-on examples for each algorithm using downloaded datasets.', "The setup process for data science is detailed, starting with the installation of Anaconda, the world's most popular data science platform."]}], 'duration': 234.514, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ289.jpg', 'highlights': ['The course covers an overview of a data science crash course, including a definition of data science, coding setup, and various topics such as linear regression, classification, resampling, regularization methods, and decision trees.', 'Resampling and regularization methods will be discussed, which are essential in any workflow for a data scientist.', 'Theoretical understanding is crucial in data science, providing insights into model behavior and essential for job interviews.', 'Practical application of algorithms in Python is emphasized, with hands-on examples for each algorithm using downloaded datasets.', 'Data science encompasses three professions: machine learning scientist or engineer, data analyst, and data engineer, each with specific roles in the field.', 'Machine learning scientists or engineers develop algorithms in the field of data science.', 'Data analysts are responsible for answering business questions using data, such as identifying the best-selling product in a given time period.', 'Data engineers gather data from various sources and organize it for use by data analysts or machine learning engineers.', "The setup process for data science is detailed, starting with the installation of Anaconda, the world's most popular data science platform."]}, {'end': 1248.597, 'segs': [{'end': 426.075, 'src': 'heatmap', 'start': 234.803, 'weight': 0, 'content': [{'end': 243.148, 'text': 'you can simply follow the instructions on the graphical installer and once everything is done, you should be able, at least on Windows,', 'start': 234.803, 'duration': 8.345}, {'end': 254.786, 'text': 'if you hit the Windows key and start typing Jupiter with a Y, simply type enter And after a moment you should see this page showing up.', 'start': 243.148, 'duration': 11.638}, {'end': 261.349, 'text': "And that means that Jupyter is installed correctly and you're ready to do some data science.", 'start': 255.386, 'duration': 5.963}, {'end': 268.273, 'text': "If you're on Mac, however, simply open your terminal and type in Jupyter notebook with a space and you should be fine.", 'start': 261.829, 'duration': 6.444}, {'end': 276.199, 'text': "Alright, now that we are set up, let's kick off this crash course with our very first algorithm, which is linear regression.", 'start': 269.235, 'duration': 6.964}, {'end': 283.864, 'text': 'We start with a simple linear regression, where our target y only depends on one variable, x, and a constant.', 'start': 276.96, 'duration': 6.904}, {'end': 293.07, 'text': 'Beta 1 is then a parameter, which can be positive or negative, that characterizes the slope, and beta naught here is the constant.', 'start': 284.625, 'duration': 8.445}, {'end': 298.182, 'text': 'To find the parameters, we need to minimize a certain error function.', 'start': 294.821, 'duration': 3.361}, {'end': 305.805, 'text': 'So here, the error is simply the difference between the real target, yi, and the prediction, y hat.', 'start': 298.782, 'duration': 7.023}, {'end': 309.827, 'text': 'For linear regression, we minimize the sum of squared errors.', 'start': 306.665, 'duration': 3.162}, {'end': 315.749, 'text': 'So we raise this equation to the power of two and add all errors across all data points.', 'start': 310.147, 'duration': 5.602}, {'end': 318.739, 'text': 'Visually, it looks like this.', 'start': 317.438, 'duration': 1.301}, {'end': 323.462, 'text': 'The red dots represent our data and the blue line is our fitted straight line.', 'start': 319.339, 'duration': 4.123}, {'end': 326.864, 'text': 'Each vertical line is the magnitude of the error.', 'start': 324.082, 'duration': 2.782}, {'end': 336.411, 'text': 'So we want to position the blue line such as the sum of the squared length of each vertical line is as small as possible.', 'start': 327.425, 'duration': 8.986}, {'end': 339.972, 'text': 'Now you might wonder why do we square the errors?', 'start': 337.071, 'duration': 2.901}, {'end': 346.895, 'text': 'Well, as you saw, the points can lie above or below the fitted line, so the error can be positive or negative.', 'start': 340.573, 'duration': 6.322}, {'end': 353.198, 'text': 'If we did not square the error, we could be adding a bunch of negative errors and reduce the sum of errors.', 'start': 347.616, 'duration': 5.582}, {'end': 359.161, 'text': "It would trick us in thinking that we are fitting a good straight line, where in fact we're not.", 'start': 353.998, 'duration': 5.163}, {'end': 366.164, 'text': 'It also has the added advantage of penalizing large errors, so we really get the best fit possible.', 'start': 360.061, 'duration': 6.103}, {'end': 368.6, 'text': 'For simple linear regression.', 'start': 367.179, 'duration': 1.421}, {'end': 376.805, 'text': 'you can find the parameters analytically with these formulas, where X bar is the mean of the independent variable and Y bar is the mean of the target.', 'start': 368.6, 'duration': 8.205}, {'end': 382.229, 'text': 'Now, of course, in practice, we will use Python to estimate those parameters for us.', 'start': 378.286, 'duration': 3.943}, {'end': 392.855, 'text': 'Here you can see we first initialize the model and then we fit on X and Y, and then we can retrieve both the intercept and the coefficient,', 'start': 383.049, 'duration': 9.806}, {'end': 393.616, 'text': 'as you can see here.', 'start': 392.855, 'duration': 0.761}, {'end': 398.933, 'text': 'Once you have your coefficients, we need a way to assess their relevancy.', 'start': 395.29, 'duration': 3.643}, {'end': 401.395, 'text': 'To do so, we use the p-value.', 'start': 399.673, 'duration': 1.722}, {'end': 408.96, 'text': 'This allows us to quantify the statistical significance and determine if we can reject the null hypothesis or not.', 'start': 401.995, 'duration': 6.965}, {'end': 415.865, 'text': 'In Python, we can analyze the p-value for each coefficient like this.', 'start': 410.061, 'duration': 5.804}, {'end': 421.87, 'text': 'Here we use a statistical package from Python that allows us to print out a summary of the model.', 'start': 416.626, 'duration': 5.244}, {'end': 426.075, 'text': 'Here you can see an example of that summary.', 'start': 424.113, 'duration': 1.962}], 'summary': 'Follow simple instructions to install jupyter for data science. learn about linear regression, its parameters, and error minimization.', 'duration': 26.546, 'max_score': 234.803, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ234803.jpg'}, {'end': 560.422, 'src': 'embed', 'start': 533.427, 'weight': 4, 'content': [{'end': 538.892, 'text': "Now again, in Python, we'll use the same package that outputs for us the F statistic.", 'start': 533.427, 'duration': 5.465}, {'end': 544.877, 'text': 'As you can see here in yellow, the F statistic for a multiple linear regression model.', 'start': 539.492, 'duration': 5.385}, {'end': 552.593, 'text': 'Usually, if f is much greater than one, we say that there is a strong relationship between our predictors and the target.', 'start': 545.845, 'duration': 6.748}, {'end': 560.422, 'text': 'For a small data set of a couple hundred data points, then the f statistic has to be way larger than one.', 'start': 553.114, 'duration': 7.308}], 'summary': 'Using python package to compute f statistic for multiple linear regression model; f > 1 indicates strong relationship, especially for small data sets.', 'duration': 26.995, 'max_score': 533.427, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ533427.jpg'}, {'end': 853.42, 'src': 'embed', 'start': 821.843, 'weight': 1, 'content': [{'end': 824.805, 'text': 'If you looked at the theory portion of this video.', 'start': 821.843, 'duration': 2.962}, {'end': 834.261, 'text': 'So as you can see, we get a constant of seven and a slope of 0.0475 approximately.', 'start': 825.566, 'duration': 8.695}, {'end': 835.122, 'text': "So that's great.", 'start': 834.721, 'duration': 0.401}, {'end': 837.586, 'text': 'We have a positive slope, positive constant.', 'start': 835.302, 'duration': 2.284}, {'end': 838.988, 'text': 'It seems to make sense.', 'start': 837.686, 'duration': 1.302}, {'end': 844.336, 'text': "So let's get some predictions and actually plot our straight line.", 'start': 841.035, 'duration': 3.301}, {'end': 850.899, 'text': 'So you can get the prediction simply by calling the predict method on X.', 'start': 844.817, 'duration': 6.082}, {'end': 853.42, 'text': 'And then we do another figure.', 'start': 850.899, 'duration': 2.521}], 'summary': 'Linear regression analysis yields a positive slope of 0.0475 and a constant of seven, leading to successful predictions.', 'duration': 31.577, 'max_score': 821.843, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ821843.jpg'}, {'end': 1019.218, 'src': 'heatmap', 'start': 925.715, 'weight': 0.729, 'content': [{'end': 935.2, 'text': "So I'm gonna re-specify again my x and my y, and we're gonna fit another linear model, but using the stats library.", 'start': 925.715, 'duration': 9.485}, {'end': 944.304, 'text': 'So specify the exogenous variable as sm.addConstant x, and then the estimator is simply sm.ols.', 'start': 935.5, 'duration': 8.804}, {'end': 946.666, 'text': 'That stands for ordinary least squares.', 'start': 944.565, 'duration': 2.101}, {'end': 947.986, 'text': "That's the method we're using.", 'start': 946.806, 'duration': 1.18}, {'end': 950.708, 'text': 'Pass in y, pass in x, and we fit.', 'start': 948.487, 'duration': 2.221}, {'end': 960.933, 'text': 'finally, you can print a summary of the estimator and you should see the following result so, as you can see, we have an r squared value of 0.6.', 'start': 951.368, 'duration': 9.565}, {'end': 962.554, 'text': 'so that is not very good.', 'start': 960.933, 'duration': 1.621}, {'end': 966.216, 'text': 'only 60 of the variability is explained.', 'start': 962.554, 'duration': 3.662}, {'end': 969.898, 'text': 'the f statistic is 312, which is much larger than one.', 'start': 966.216, 'duration': 3.682}, {'end': 978.543, 'text': 'so it seems that our model is kind of good and as you can see here, for tv we get the same coefficients as before and the p value,', 'start': 969.898, 'duration': 8.645}, {'end': 982.665, 'text': 'although probably not zero, it seems to be less than 0.05.', 'start': 978.543, 'duration': 4.122}, {'end': 989.95, 'text': 'so it means that our um, that our feature, is indeed relevant in this model.', 'start': 982.665, 'duration': 7.285}, {'end': 992.507, 'text': 'So that was simple linear regression.', 'start': 991.186, 'duration': 1.321}, {'end': 994.848, 'text': "now let's move on to multiple linear regression.", 'start': 992.507, 'duration': 2.341}, {'end': 1001.931, 'text': 'So in this case, we will consider all the features, so TV, radio, and newspaper, and see how that affects the sales.', 'start': 995.048, 'duration': 6.883}, {'end': 1004.812, 'text': 'So all my Xs to define them.', 'start': 1002.571, 'duration': 2.241}, {'end': 1010.274, 'text': "I'm just gonna drop the sales column and make sure I dropped it on axis equals one.", 'start': 1004.812, 'duration': 5.462}, {'end': 1015.356, 'text': "so I mean I'm dropping it only the column and not the rows and the Y is gonna be the same.", 'start': 1010.274, 'duration': 5.082}, {'end': 1019.218, 'text': 'so data sales dot values, dot, reshape, minus one and one.', 'start': 1015.356, 'duration': 3.862}], 'summary': 'Linear regression using stats library with r-squared 0.6, f-statistic 312, tv coefficient relevant with p<0.05. moving on to multiple linear regression.', 'duration': 93.503, 'max_score': 925.715, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ925715.jpg'}, {'end': 1248.597, 'src': 'embed', 'start': 1222.702, 'weight': 2, 'content': [{'end': 1227.184, 'text': "So we're explaining almost 90% of the variability of the sales here.", 'start': 1222.702, 'duration': 4.482}, {'end': 1231.325, 'text': 'The F statistic, 570, again, larger than before.', 'start': 1227.784, 'duration': 3.541}, {'end': 1236.688, 'text': 'So it means that our model is pretty good, actually, to predict the sales from that.', 'start': 1231.425, 'duration': 5.263}, {'end': 1239.849, 'text': 'And as you can see here, all the constants and all the coefficients.', 'start': 1236.788, 'duration': 3.061}, {'end': 1246.174, 'text': 'Now, as you see for the last one, we have a p-value equal to 0.860.', 'start': 1240.589, 'duration': 5.585}, {'end': 1248.597, 'text': 'So that is larger than 0.05.', 'start': 1246.174, 'duration': 2.423}], 'summary': 'Model explains 90% sales variability, f statistic 570, p-value 0.860', 'duration': 25.895, 'max_score': 1222.702, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1222702.jpg'}], 'start': 234.803, 'title': 'Linear regression and model assessment in python', 'summary': 'Covers the installation of jupyter and understanding of simple linear regression, followed by an explanation of multiple linear regression and python data analysis with pandas and scikit-learn, resulting in a linear regression model. it also discusses assessing model quality using statistical measures, achieving an r-squared value of 0.897 and an f statistic of 570.', 'chapters': [{'end': 458.903, 'start': 234.803, 'title': 'Installing jupyter and understanding linear regression', 'summary': 'Covers the installation of jupyter on windows and mac, followed by an explanation of simple linear regression, including the calculation of parameters, error minimization, and the use of python to estimate parameters, assess their relevancy using p-value, and evaluate the model using residual standard error.', 'duration': 224.1, 'highlights': ['Explanation of simple linear regression, including the calculation of parameters, error minimization, and visual representation of fitted straight line with minimized sum of squared errors.', 'Use of Python to estimate parameters for simple linear regression and retrieve both the intercept and the coefficient.', 'Explanation of using p-value to assess the relevancy of parameters and determination of statistical significance.', 'Explanation of evaluating the model using residual standard error, with smaller values indicating better performance.', 'Step-by-step guide for installing Jupyter on Windows and Mac, including the use of graphical installer and terminal commands.']}, {'end': 582.069, 'start': 459.583, 'title': 'Understanding multiple linear regression', 'summary': 'Introduces the concept of multiple linear regression, explaining the use of r-squared value, p-value, f statistic, and their significance in python, highlighting the importance of model assessment using these statistical measures.', 'duration': 122.486, 'highlights': ['The chapter explains the importance of R-squared value in multiple linear regression, with a higher value indicating the explanation of more variability in the target, approaching 1.', 'It details the process of finding the p-value in Python and emphasizes the significance of understanding the full printout to comprehend the statistics.', 'The use of the F statistic for model assessment in multiple linear regression is highlighted, with emphasis on the relationship between predictors and the target, and the importance of a much greater F statistic than one for a small dataset.', 'The demonstration of accessing parameters and understanding the model in Python for multiple linear regression is provided, emphasizing the use of the F statistic and its interpretation for assessing the relationship between predictors and the target.']}, {'end': 912.368, 'start': 582.169, 'title': 'Python data analysis with pandas and scikit-learn', 'summary': 'Covers importing, visualizing, and fitting a linear model to a dataset using pandas, numpy, matplotlib, and scikit-learn, resulting in a linear regression model with a positive constant of 7 and a slope of 0.0475.', 'duration': 330.199, 'highlights': ['The chapter covers importing, visualizing, and fitting a linear model to a dataset using pandas, numpy, matplotlib, and scikit-learn.', 'The linear regression model has a positive constant of 7 and a slope of 0.0475.', 'The data set includes ad spend on TV, radio, and newspaper, and the impact on sales.']}, {'end': 1248.597, 'start': 912.628, 'title': 'Assessing model quality with stats library', 'summary': 'Discusses assessing model quality using the stats library, including an r-squared value of 0.897 and an f statistic of 570, indicating a model that explains almost 90% of sales variability and is good for prediction.', 'duration': 335.969, 'highlights': ['The R-squared value is 0.897, indicating that the model explains almost 90% of the variability of the sales.', 'The F statistic is 570, suggesting that the model is good for predicting sales.', 'The p-value for the last coefficient is 0.860, indicating that it is larger than 0.05 and less relevant in the model.', 'The R-squared value is 0.6, indicating that only 60% of the variability is explained in the initial model.', 'The F statistic is 312, which is much larger than one, indicating a relatively good model.']}], 'duration': 1013.794, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ234803.jpg', 'highlights': ['Step-by-step guide for installing Jupyter on Windows and Mac, including the use of graphical installer and terminal commands.', 'The linear regression model has a positive constant of 7 and a slope of 0.0475.', 'The R-squared value is 0.897, indicating that the model explains almost 90% of the variability of the sales.', 'The F statistic is 570, suggesting that the model is good for predicting sales.', 'The use of the F statistic for model assessment in multiple linear regression is highlighted, with emphasis on the relationship between predictors and the target, and the importance of a much greater F statistic than one for a small dataset.']}, {'end': 1781.398, 'segs': [{'end': 1356.315, 'src': 'heatmap', 'start': 1248.597, 'weight': 3, 'content': [{'end': 1252.2, 'text': 'And recall that this coefficient here is the one for newspaper.', 'start': 1248.597, 'duration': 3.603}, {'end': 1258.966, 'text': 'So that means that newspaper is actually not relevant in our model, and we could, and actually we should take it out.', 'start': 1252.841, 'duration': 6.125}, {'end': 1263.611, 'text': "Let's move on now to the next topic, which is classification.", 'start': 1260.187, 'duration': 3.424}, {'end': 1266.66, 'text': 'First, a bit of terminology.', 'start': 1265.319, 'duration': 1.341}, {'end': 1273.805, 'text': 'Binary classification is also termed simple classification, and this is the case when we only have two classes.', 'start': 1267.24, 'duration': 6.565}, {'end': 1279.088, 'text': 'For example, spam or not spam, a fraudulent transaction or not.', 'start': 1274.305, 'duration': 4.783}, {'end': 1282.11, 'text': 'Of course, you can also have more than two classes.', 'start': 1279.829, 'duration': 2.281}, {'end': 1285.613, 'text': 'For example, eye color, which can be blue, green, or brown.', 'start': 1282.331, 'duration': 3.282}, {'end': 1293.577, 'text': 'Now, you see, in the context of classification, we have a qualitative or categorical response,', 'start': 1287.813, 'duration': 5.764}, {'end': 1298.52, 'text': 'unlike regression problems where we have a quantitative response or numbers.', 'start': 1293.577, 'duration': 4.943}, {'end': 1305.445, 'text': "So now let's see how we can perform classification with one algorithm, which is logistic regression.", 'start': 1299.281, 'duration': 6.164}, {'end': 1313.658, 'text': 'Ideally, when doing classification, we want to determine the probability of an observation to be part of a class or not.', 'start': 1307.253, 'duration': 6.405}, {'end': 1320.624, 'text': 'Therefore, we ideally want the output to be between 0 and 1, where 1 means very likely.', 'start': 1314.499, 'duration': 6.125}, {'end': 1327.29, 'text': "Well, it just turns out that there is a function to do that, and it's the sigmoid function that you see on the screen.", 'start': 1322.046, 'duration': 5.244}, {'end': 1336.889, 'text': 'As you can see, as x approaches infinity, you approach 1, and if you go towards negative infinity, you approach 0.', 'start': 1328.411, 'duration': 8.478}, {'end': 1338.95, 'text': 'The sigmoid function is expressed like this.', 'start': 1336.889, 'duration': 2.061}, {'end': 1342.771, 'text': 'And here we are assuming only one predictor x.', 'start': 1339.33, 'duration': 3.441}, {'end': 1345.912, 'text': 'For now, we stick to one predictor to make the explanation simpler.', 'start': 1342.771, 'duration': 3.141}, {'end': 1349.313, 'text': 'With some manipulation, you can get to this formula here.', 'start': 1346.692, 'duration': 2.621}, {'end': 1353.654, 'text': 'So we are trying to get a linear equation for x.', 'start': 1349.773, 'duration': 3.881}, {'end': 1356.315, 'text': 'Take the log on both sides and you get the logit.', 'start': 1353.654, 'duration': 2.661}], 'summary': 'Logistic regression for classification; sigmoid function determines probability of observation to be part of a class or not.', 'duration': 72.027, 'max_score': 1248.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1248597.jpg'}, {'end': 1482.038, 'src': 'embed', 'start': 1452.608, 'weight': 4, 'content': [{'end': 1454.869, 'text': "Now, Bailly's theorem is explained like this.", 'start': 1452.608, 'duration': 2.261}, {'end': 1464.112, 'text': 'Suppose that we want to classify an observation into one of capital K classes, where capital K is greater than or equal to two.', 'start': 1455.909, 'duration': 8.203}, {'end': 1472.335, 'text': 'Then we let pi k be the overall probability that an observation is associated to the kth class.', 'start': 1465.132, 'duration': 7.203}, {'end': 1482.038, 'text': 'Then let f k of x denote the density function of x for an observation that comes from the kth class.', 'start': 1473.775, 'duration': 8.263}], 'summary': "Bailly's theorem explains classification into k classes with probabilities and density functions.", 'duration': 29.43, 'max_score': 1452.608, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1452608.jpg'}, {'end': 1682.33, 'src': 'embed', 'start': 1638.134, 'weight': 1, 'content': [{'end': 1639.996, 'text': 'The main difference is in the assumptions.', 'start': 1638.134, 'duration': 1.862}, {'end': 1648.683, 'text': 'Just like LDA, we assume each class is from a multivariate normal distribution and that each class has its own mean vector.', 'start': 1640.756, 'duration': 7.927}, {'end': 1653.147, 'text': 'but this time each class also has its own covariance matrix.', 'start': 1648.683, 'duration': 4.464}, {'end': 1659.579, 'text': 'Of course, because we are talking about quadratic discriminant analysis.', 'start': 1655.137, 'duration': 4.442}, {'end': 1666.302, 'text': 'well, the discriminant here is expressed like this, and you see that the equation is now quadratic with respect to x,', 'start': 1659.579, 'duration': 6.723}, {'end': 1669.444, 'text': 'since we have two terms of x being multiplied together.', 'start': 1666.302, 'duration': 3.142}, {'end': 1679.487, 'text': 'Now, whereas LDA was better than logistic regression in some situations, QDA is also better than LDA, mainly when you have a large dataset,', 'start': 1670.799, 'duration': 8.688}, {'end': 1682.33, 'text': 'because it has a lower bias and higher variance.', 'start': 1679.487, 'duration': 2.843}], 'summary': "Qda assumes each class has own covariance matrix. it's better than lda for large datasets due to lower bias and higher variance.", 'duration': 44.196, 'max_score': 1638.134, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1638134.jpg'}, {'end': 1742.843, 'src': 'embed', 'start': 1708.298, 'weight': 0, 'content': [{'end': 1714.562, 'text': 'Now, before we move on to the coding portion, we must understand how to validate our models in the context of classification.', 'start': 1708.298, 'duration': 6.264}, {'end': 1718.532, 'text': 'To do so, we use sensitivity and specificity.', 'start': 1715.731, 'duration': 2.801}, {'end': 1725.955, 'text': 'Sensitivity is the true positive rate, so the proportion of actual positives identified.', 'start': 1719.353, 'duration': 6.602}, {'end': 1731.638, 'text': 'For example, it would be the proportion of fraudulent transactions that are actually fraudulent.', 'start': 1726.036, 'duration': 5.602}, {'end': 1742.843, 'text': 'On the other hand, specificity is the true negative rate, so the proportion of non-fraudulent transactions that are actually non-fraudulent.', 'start': 1733.179, 'duration': 9.664}], 'summary': 'Validating classification models using sensitivity and specificity.', 'duration': 34.545, 'max_score': 1708.298, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1708298.jpg'}], 'start': 1248.597, 'title': 'Classification and logistic regression', 'summary': 'Covers understanding classification in data science, logistic regression, linear discriminant analysis, linear and quadratic discriminant analysis, and model validation using sensitivity, specificity, and roc curve in python.', 'chapters': [{'end': 1298.52, 'start': 1248.597, 'title': 'Understanding classification in data science', 'summary': 'Covers the relevance of coefficients in the model, the concept of binary and multi-class classification, and the distinction between qualitative and quantitative responses in data science.', 'duration': 49.923, 'highlights': ['The coefficient for newspaper in the model indicates its irrelevance, suggesting its removal for improved accuracy.', 'Classification is discussed, including binary classification with two classes and multi-class classification with more than two classes.', 'The distinction between qualitative or categorical responses in classification and quantitative responses in regression problems is highlighted.']}, {'end': 1517.013, 'start': 1299.281, 'title': 'Logistic regression and linear discriminant analysis', 'summary': "Explains logistic regression, demonstrating the sigmoid function, the logit formula, and the application of logistic regression in python, followed by an introduction to linear discriminant analysis (lda) as a solution to the caveats of logistic regression, highlighting its ability to handle multiple target classes and its use of bailly's theorem.", 'duration': 217.732, 'highlights': ['Logistic regression models the probability of an observation being part of a class using the sigmoid function, which ensures the output is bounded between 0 and 1, allowing for binary classification.', 'The logit formula is derived from the sigmoid function, providing a linear equation for x, and can be extended to accommodate multiple predictors, resulting in better classification results.', "Linear discriminant analysis (LDA) overcomes logistic regression's caveats, such as instability in parameter estimates for well-separated classes and small datasets, and the limitation to binary classification, by modeling the distribution of predictors for each class, accommodating more than two target classes.", "Bailly's theorem in LDA is used to model the probability of an observation belonging to a class, enabling the classification of an observation into multiple target classes."]}, {'end': 1781.398, 'start': 1518.453, 'title': 'Linear & quadratic discriminant analysis', 'summary': 'Explains linear discriminant analysis (lda) and quadratic discriminant analysis (qda), their assumptions, implementation in python, and model validation using sensitivity, specificity, and roc curve with the aim of achieving a high roc auc.', 'duration': 262.945, 'highlights': ['Linear Discriminant Analysis (LDA) assumes each class has a normal distribution, and each class has its own mean, but the variance is common for all classes.', 'Quadratic Discriminant Analysis (QDA) assumes each class is from a multivariate normal distribution with its own mean vector and covariance matrix, making it better suited for large datasets with lower bias and higher variance compared to LDA.', 'The chapter emphasizes model validation using sensitivity and specificity, as well as the ROC curve and its area under the curve (AUC) to achieve a high ROC AUC close to 1.']}], 'duration': 532.801, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1248597.jpg', 'highlights': ['Model validation using sensitivity, specificity, and ROC curve emphasized', 'Quadratic Discriminant Analysis (QDA) better for large datasets with lower bias', 'Linear Discriminant Analysis (LDA) assumes each class has a normal distribution', 'Logistic regression models the probability of an observation using the sigmoid function', "Bailly's theorem in LDA used to model the probability of an observation belonging to a class", 'Classification discussed for binary and multi-class with qualitative responses', 'The coefficient for newspaper in the model indicates its irrelevance']}, {'end': 2946.957, 'segs': [{'end': 1866.153, 'src': 'embed', 'start': 1836.45, 'weight': 8, 'content': [{'end': 1843.176, 'text': 'So in this project, we are going to classify mushrooms as either being edible or poisonous, depending on different features.', 'start': 1836.45, 'duration': 6.726}, {'end': 1847.56, 'text': 'So you have cap shape, cap surface, cap color, etc.', 'start': 1843.496, 'duration': 4.064}, {'end': 1851.683, 'text': 'And I put all the possible values here at the beginning of the notebook.', 'start': 1848.06, 'duration': 3.623}, {'end': 1858.129, 'text': "So let's start off by importing the libraries we are going to use, so pend as spd, numpy as np.", 'start': 1852.884, 'duration': 5.245}, {'end': 1866.153, 'text': 'Of course, matplotlib.pyplot as PLT and also seaborn as SNS.', 'start': 1859.531, 'duration': 6.622}], 'summary': 'Project aims to classify mushrooms as edible or poisonous based on features like cap shape, surface, and color.', 'duration': 29.703, 'max_score': 1836.45, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1836450.jpg'}, {'end': 2021.828, 'src': 'embed', 'start': 1967.079, 'weight': 3, 'content': [{'end': 1968.8, 'text': "So for that, I'm going to use Seaborn.", 'start': 1967.079, 'duration': 1.721}, {'end': 1971.601, 'text': 'And as you can see, our data set is fairly balanced.', 'start': 1969.36, 'duration': 2.241}, {'end': 1975.403, 'text': 'We almost have the same amount of poisonous and edible mushrooms.', 'start': 1971.741, 'duration': 3.662}, {'end': 1977.224, 'text': 'So that is very good.', 'start': 1976.103, 'duration': 1.121}, {'end': 1981.526, 'text': "We're not going to have to do a lot of pre-processing for our analysis.", 'start': 1977.524, 'duration': 4.002}, {'end': 1995.141, 'text': "Now I'm going to define a function that will allow us to see, depending on what feature, how many mushrooms are poisonous or edible.", 'start': 1984.437, 'duration': 10.704}, {'end': 2000.223, 'text': 'So, for example, if I plot for the cap surface.', 'start': 1995.681, 'duration': 4.542}, {'end': 2008.746, 'text': 'so for all possibilities of values for the cap surface, I want to know how many of those mushrooms are edible and how many of those are poisonous.', 'start': 2000.223, 'duration': 8.523}, {'end': 2016.492, 'text': "So that's going to give us a bit of intuition as to which feature helps you to actually classify your mushrooms.", 'start': 2009.266, 'duration': 7.226}, {'end': 2021.828, 'text': "And that's it, so that's the function.", 'start': 2019.947, 'duration': 1.881}], 'summary': "Using seaborn to analyze a balanced dataset of mushrooms, with equal amounts of poisonous and edible. creating a function to visualize features' impact on classification.", 'duration': 54.749, 'max_score': 1967.079, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1967079.jpg'}, {'end': 2175.158, 'src': 'embed', 'start': 2116.939, 'weight': 6, 'content': [{'end': 2125.646, 'text': 'So what label encoder will do is that it will transform our class column into one and zeros because we cannot work with letters.', 'start': 2116.939, 'duration': 8.707}, {'end': 2131.951, 'text': 'We have to work with numbers, right? So you do le.fit transform data class.', 'start': 2125.686, 'duration': 6.265}, {'end': 2135.194, 'text': 'And now I will show you the result data head.', 'start': 2132.772, 'duration': 2.422}, {'end': 2137.103, 'text': 'And there you go.', 'start': 2136.383, 'duration': 0.72}, {'end': 2142.066, 'text': 'Now, as you can see, the class is now 1 and 0.', 'start': 2137.624, 'duration': 4.442}, {'end': 2145.568, 'text': 'So either it is poisonous or not poisonous.', 'start': 2142.066, 'duration': 3.502}, {'end': 2147.469, 'text': 'So 1 being true, 0 being false.', 'start': 2145.588, 'duration': 1.881}, {'end': 2151.831, 'text': 'Then you want to one-hot encode the rest of the dataset.', 'start': 2149.05, 'duration': 2.781}, {'end': 2156.333, 'text': 'So to do that, we do pd.getDummies, and then you pass in the data.', 'start': 2152.091, 'duration': 4.242}, {'end': 2159.315, 'text': "So let's see what the result of that will be.", 'start': 2157.194, 'duration': 2.121}, {'end': 2165.115, 'text': 'And as you can see, now we have added a lot of columns.', 'start': 2162.674, 'duration': 2.441}, {'end': 2172.877, 'text': 'So we went from 23 columns to 118 columns because now for every feature we have either true or false.', 'start': 2165.135, 'duration': 7.742}, {'end': 2175.158, 'text': 'And now that is perfect.', 'start': 2173.698, 'duration': 1.46}], 'summary': 'Label encoded class column to 1s and 0s, one-hot encoded dataset, increasing columns from 23 to 118.', 'duration': 58.219, 'max_score': 2116.939, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ2116939.jpg'}, {'end': 2361.736, 'src': 'embed', 'start': 2332.079, 'weight': 5, 'content': [{'end': 2334.54, 'text': 'So we have run that and everything is okay.', 'start': 2332.079, 'duration': 2.461}, {'end': 2337.041, 'text': 'You can safely ignore the warning on the screen.', 'start': 2334.56, 'duration': 2.481}, {'end': 2341.017, 'text': "And now we're gonna see at a confusion matrix.", 'start': 2338.652, 'duration': 2.365}, {'end': 2347.99, 'text': 'So the confusion matrix is actually gonna show you how many mushrooms were correctly classified.', 'start': 2341.277, 'duration': 6.713}, {'end': 2355.593, 'text': 'So confusion matrix, you pass in y test and you pass in the y prediction.', 'start': 2349.229, 'duration': 6.364}, {'end': 2361.736, 'text': "And hopefully if those are equal, you will see that we're gonna have a diagonal matrix.", 'start': 2356.293, 'duration': 5.443}], 'summary': 'Confusion matrix shows correct mushroom classifications in y test and y prediction.', 'duration': 29.657, 'max_score': 2332.079, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ2332079.jpg'}, {'end': 2595.308, 'src': 'embed', 'start': 2565.239, 'weight': 0, 'content': [{'end': 2567.001, 'text': "And that's it for our function.", 'start': 2565.239, 'duration': 1.762}, {'end': 2573.506, 'text': "So let's actually plot the rock curve that we obtained above with logistic regression.", 'start': 2567.321, 'duration': 6.185}, {'end': 2576.481, 'text': 'and you should get the following.', 'start': 2575.2, 'duration': 1.281}, {'end': 2579.502, 'text': 'So this is actually a perfect rock curve.', 'start': 2576.581, 'duration': 2.921}, {'end': 2584.484, 'text': "So it's hugging the upper left corner and we have an AUC of one.", 'start': 2580.002, 'duration': 4.482}, {'end': 2587.005, 'text': 'So that means perfect classification.', 'start': 2585.004, 'duration': 2.001}, {'end': 2595.308, 'text': "So now let's move on to our second algorithm, which is linear discriminant analysis.", 'start': 2589.906, 'duration': 5.402}], 'summary': 'Perfect rock curve with auc of 1, now moving on to linear discriminant analysis.', 'duration': 30.069, 'max_score': 2565.239, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ2565239.jpg'}, {'end': 2809.699, 'src': 'embed', 'start': 2781.862, 'weight': 2, 'content': [{'end': 2786.784, 'text': 'Because our confusion matrix was the same, the ROC AUC was the same, the plot should be the same.', 'start': 2781.862, 'duration': 4.922}, {'end': 2790.525, 'text': 'So again, LDA is a perfect classifier in this case.', 'start': 2786.884, 'duration': 3.641}, {'end': 2795.294, 'text': 'And finally, we are going to implement quadratic discriminant analysis.', 'start': 2791.533, 'duration': 3.761}, {'end': 2805.537, 'text': 'Again, I strongly suggest that you pause the video at this point and really try to repeat the steps that we have done before using QDA.', 'start': 2795.654, 'duration': 9.883}, {'end': 2809.699, 'text': "So as always, we're gonna import the model from sklearn.", 'start': 2807.158, 'duration': 2.541}], 'summary': 'Lda is a perfect classifier with same confusion matrix and roc auc. next, implementing quadratic discriminant analysis.', 'duration': 27.837, 'max_score': 2781.862, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ2781862.jpg'}], 'start': 1782.481, 'title': 'Mushroom classification and analysis', 'summary': 'Discusses creating a python function to plot the roc curve and report its auc, classifying mushrooms as edible or poisonous, logistic regression modeling achieving perfect classification, and evaluating classifiers with auc of 1, indicating model effectiveness.', 'chapters': [{'end': 2066.271, 'start': 1782.481, 'title': 'Mushroom classification project', 'summary': 'Discusses creating a python function to plot the roc curve and report its auc, then demonstrates classifying mushrooms as edible or poisonous using various features, showing the balanced dataset and defining a function to visualize the distribution of edible and poisonous mushrooms based on different features.', 'duration': 283.79, 'highlights': ['The chapter discusses creating a Python function to plot the ROC curve and report its AUC, then demonstrates classifying mushrooms as edible or poisonous using various features, showing the balanced dataset and defining a function to visualize the distribution of edible and poisonous mushrooms based on different features.', 'The dataset contains features such as cap shape, cap surface, cap color, etc., and the project aims to classify mushrooms as either edible or poisonous.', 'The dataset is fairly balanced, with almost the same amount of poisonous and edible mushrooms, reducing the need for extensive pre-processing.', 'A function is defined to visualize the distribution of edible and poisonous mushrooms based on different features to gain intuition about which feature helps in classifying the mushrooms.']}, {'end': 2389.888, 'start': 2066.311, 'title': 'Mushroom classification with logistic regression', 'summary': 'Covers pre-processing steps including checking for null values, label encoding, and one-hot encoding the dataset, followed by modeling using logistic regression, achieving a perfect classification with a diagonal confusion matrix.', 'duration': 323.577, 'highlights': ['The confusion matrix revealed a perfect classification with all poisonous and edible mushrooms correctly identified.', 'The dataset was pre-processed to have no null values, and label encoding transformed the class column into 1s and 0s.', 'One-hot encoding expanded the dataset from 23 columns to 118 columns, with each feature represented as true or false.', 'Logistic regression was applied to the pre-processed dataset, achieving a perfect classification with a diagonal confusion matrix.']}, {'end': 2946.957, 'start': 2389.888, 'title': 'Roc curve and auc analysis', 'summary': 'Describes the process of plotting roc curve, calculating auc, and evaluating three different classifiers (logistic regression, linear discriminant analysis, and quadratic discriminant analysis) with each achieving a perfect classification of 1, indicating the effectiveness of the models.', 'duration': 557.069, 'highlights': ['The logistic regression model achieved a perfect classification with an AUC of 1, indicating its effectiveness in classification.', 'Linear discriminant analysis also achieved a perfect classification with an AUC of 1, indicating its effectiveness in classification.', 'Quadratic discriminant analysis resulted in a perfect classification with an AUC of 1, indicating its effectiveness in classification.']}], 'duration': 1164.476, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ1782481.jpg', 'highlights': ['Logistic regression achieved perfect classification with AUC of 1', 'Linear discriminant analysis also achieved perfect classification with AUC of 1', 'Quadratic discriminant analysis resulted in perfect classification with AUC of 1', 'The dataset is fairly balanced, reducing the need for extensive pre-processing', 'Defined function to visualize distribution of edible and poisonous mushrooms based on features', 'Confusion matrix revealed perfect classification with all mushrooms correctly identified', 'One-hot encoding expanded dataset from 23 to 118 columns', 'Pre-processed dataset had no null values, and label encoding transformed class column into 1s and 0s', 'Project aims to classify mushrooms as either edible or poisonous using various features', 'Dataset contains features such as cap shape, cap surface, cap color, etc.']}, {'end': 3838.151, 'segs': [{'end': 3055.175, 'src': 'embed', 'start': 3027.008, 'weight': 0, 'content': [{'end': 3030.269, 'text': 'Cross-validation is a widely used method for resampling.', 'start': 3027.008, 'duration': 3.261}, {'end': 3036.251, 'text': "We use it to evaluate a model's performance and to find the best parameters for the model.", 'start': 3030.769, 'duration': 5.482}, {'end': 3039.572, 'text': 'There are three ways we can do cross-validation.', 'start': 3036.971, 'duration': 2.601}, {'end': 3048.754, 'text': 'We can have a validation set, we can do a leave-one-out cross-validation, or we can use the method of k-fold cross-validation.', 'start': 3040.012, 'duration': 8.742}, {'end': 3050.895, 'text': "Let's explore each one of them.", 'start': 3049.695, 'duration': 1.2}, {'end': 3055.175, 'text': 'The validation set is the most basic approach.', 'start': 3052.452, 'duration': 2.723}], 'summary': 'Cross-validation evaluates model performance and finds best parameters using validation set, leave-one-out, or k-fold methods.', 'duration': 28.167, 'max_score': 3027.008, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ3027008.jpg'}, {'end': 3282.503, 'src': 'embed', 'start': 3226.503, 'weight': 1, 'content': [{'end': 3231.248, 'text': 'The model is overfitting and varying a lot and it will not give good predictions.', 'start': 3226.503, 'duration': 4.745}, {'end': 3236.233, 'text': 'So we want to find a middle ground and prevent the models from overfitting.', 'start': 3232.049, 'duration': 4.184}, {'end': 3239.596, 'text': "And that's why we use regularization.", 'start': 3237.494, 'duration': 2.102}, {'end': 3244.762, 'text': 'It will help us de-correlate our model to prevent overfitting.', 'start': 3240.758, 'duration': 4.004}, {'end': 3248.591, 'text': 'Here, we will discuss ridge regression and lasso.', 'start': 3245.649, 'duration': 2.942}, {'end': 3252.694, 'text': 'Note that these methods are also called shrinkage methods.', 'start': 3249.191, 'duration': 3.503}, {'end': 3260.479, 'text': 'We know that traditional linear fitting minimizes the RSS, the residual sum of squares.', 'start': 3254.875, 'duration': 5.604}, {'end': 3266.103, 'text': 'With ridge regression, we add another parameter to the optimization function.', 'start': 3261.419, 'duration': 4.684}, {'end': 3272.807, 'text': 'Here, we add the sum of parameters squared with a coefficient lambda.', 'start': 3267.263, 'duration': 5.544}, {'end': 3276.36, 'text': 'Lambda is called a tuning parameter.', 'start': 3274.079, 'duration': 2.281}, {'end': 3282.503, 'text': 'To find the best value of lambda, we will use cross-validation with a range of values for lambda.', 'start': 3276.98, 'duration': 5.523}], 'summary': 'Using regularization like ridge regression and lasso to prevent overfitting by de-correlating the model. cross-validation used to find the best lambda value.', 'duration': 56, 'max_score': 3226.503, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ3226503.jpg'}, {'end': 3456.668, 'src': 'embed', 'start': 3431.314, 'weight': 5, 'content': [{'end': 3437.257, 'text': 'Index column is equal to zero, data.head, and there you go, the first five rows of our dataset.', 'start': 3431.314, 'duration': 5.943}, {'end': 3444.896, 'text': "Now let's define a function that will allow us to plot the target against each feature.", 'start': 3439.451, 'duration': 5.445}, {'end': 3451.243, 'text': 'So as you see, the target will be sales, and then we have three features, TV, radio, and newspaper.', 'start': 3445.697, 'duration': 5.546}, {'end': 3456.668, 'text': "So I'm going to define scatter plot and pass in feature as a parameter.", 'start': 3452.644, 'duration': 4.024}], 'summary': 'Using python to display first 5 rows of dataset and plot target against features.', 'duration': 25.354, 'max_score': 3431.314, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ3431314.jpg'}, {'end': 3576.809, 'src': 'embed', 'start': 3548.543, 'weight': 6, 'content': [{'end': 3551.605, 'text': 'So first I will import a cross val score.', 'start': 3548.543, 'duration': 3.062}, {'end': 3564.938, 'text': 'so model selection, import, cross underscore, val, underscore score and linear regression, and these will be used for our baseline.', 'start': 3553.288, 'duration': 11.65}, {'end': 3573.105, 'text': 'so our baseline model will be a very simple multiple linear regression.', 'start': 3564.938, 'duration': 8.167}, {'end': 3576.809, 'text': 'so as a first step we will define our feature vector.', 'start': 3573.105, 'duration': 3.704}], 'summary': 'Imported cross val score for baseline linear regression model.', 'duration': 28.266, 'max_score': 3548.543, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ3548543.jpg'}, {'end': 3633.63, 'src': 'embed', 'start': 3607.046, 'weight': 4, 'content': [{'end': 3615.01, 'text': 'So we are going to use cross-validation here, calculate the mean squared error, and then we are going to average those errors.', 'start': 3607.046, 'duration': 7.964}, {'end': 3619.173, 'text': 'So you pass in the model X Y the scoring.', 'start': 3615.931, 'duration': 3.242}, {'end': 3628.298, 'text': 'we need to use negative mean squared error as required by this library, and we do five-fold cross-validation.', 'start': 3619.173, 'duration': 9.125}, {'end': 3633.63, 'text': 'So this will give us five different mean squared errors.', 'start': 3630.606, 'duration': 3.024}], 'summary': 'Using 5-fold cross-validation to calculate mean squared error.', 'duration': 26.584, 'max_score': 3607.046, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ3607046.jpg'}], 'start': 2947.758, 'title': 'Resampling, regularization, and practical application', 'summary': "Covers the significance of resampling and regularization techniques, including cross-validation, ridge regression, and lasso, and showcases their practical application on the 'advertising.csv' dataset, resulting in a slightly lower mean squared error of 3.0726.", 'chapters': [{'end': 3362.666, 'start': 2947.758, 'title': 'Resampling and regularization techniques', 'summary': 'Discusses the importance of resampling and regularization techniques in improving model performance, covering the use of cross-validation for resampling and the concepts of ridge regression and lasso for regularization, with a focus on practical implementation in python.', 'duration': 414.908, 'highlights': ['The chapter emphasizes the significance of resampling and regularization techniques in enhancing model performance, with a specific focus on using cross-validation for model evaluation and parameter optimization.', 'It delves into the practical implementation of resampling methods, such as leave-one-out cross-validation and k-fold cross-validation, highlighting their benefits and limitations for model validation and performance assessment.', 'The discussion on regularization encompasses the concepts of ridge regression and lasso, emphasizing their role in preventing overfitting and the practical application of cross-validation to determine the optimal regularization parameter values in Python.']}, {'end': 3838.151, 'start': 3363.927, 'title': 'Applying methods in a project', 'summary': "Demonstrates applying methods on the 'advertising.csv' dataset by importing libraries, defining functions for plotting, calculating mean squared errors, and using ridge and lasso regression to improve a baseline multiple linear regression model, achieving a slightly lower mean squared error of 3.0726.", 'duration': 474.224, 'highlights': ['Using ridge regression to improve the baseline model', 'Calculating mean squared errors using cross-validation', 'Defining functions for plotting', 'Importing libraries and initializing the model']}], 'duration': 890.393, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ2947758.jpg', 'highlights': ['Practical application of resampling methods like leave-one-out and k-fold cross-validation', 'Significance of ridge regression and lasso in preventing overfitting', 'Using cross-validation for model evaluation and parameter optimization', 'Implementation of ridge regression to improve the baseline model', 'Calculation of mean squared errors using cross-validation', 'Defining functions for plotting', 'Importing libraries and initializing the model']}, {'end': 4365.341, 'segs': [{'end': 3958.654, 'src': 'embed', 'start': 3906.232, 'weight': 0, 'content': [{'end': 3907.574, 'text': 'Once we run the cell, there you have it.', 'start': 3906.232, 'duration': 1.342}, {'end': 3915.802, 'text': 'The best value for alpha is one and we get a mean squared error of 3.036 approximately.', 'start': 3907.934, 'duration': 7.868}, {'end': 3917.564, 'text': 'And this is indeed the best score.', 'start': 3915.982, 'duration': 1.582}, {'end': 3922.932, 'text': "Alright, let's kick off this portion about decision trees with a bit of theory.", 'start': 3918.331, 'duration': 4.601}, {'end': 3927.154, 'text': 'Tree-based methods can be used for both classification and regression.', 'start': 3923.653, 'duration': 3.501}, {'end': 3931.535, 'text': 'They involve dividing the prediction space into a number of regions.', 'start': 3927.794, 'duration': 3.741}, {'end': 3937.517, 'text': 'The set of splitting rules can be summarized in a tree, hence the name decision trees.', 'start': 3932.355, 'duration': 5.162}, {'end': 3944.414, 'text': 'Now, a single decision tree is often not better than a linear regression, logistic regression or LEA.', 'start': 3938.547, 'duration': 5.867}, {'end': 3951.082, 'text': "That's why we introduced bagging, random forests and boosting to dramatically improve our trees.", 'start': 3945.215, 'duration': 5.867}, {'end': 3956.048, 'text': 'Now, before we move on, we need to get familiar with a bit of terminology.', 'start': 3952.924, 'duration': 3.124}, {'end': 3958.654, 'text': 'Trees are drawn upside down.', 'start': 3957.153, 'duration': 1.501}], 'summary': 'Best alpha value is 1, yielding a mean squared error of 3.036. decision trees improve with bagging, random forests and boosting.', 'duration': 52.422, 'max_score': 3906.232, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ3906232.jpg'}, {'end': 4138.778, 'src': 'embed', 'start': 4108.471, 'weight': 2, 'content': [{'end': 4118.296, 'text': 'The Gini index will be close to zero if the proportion is close to zero or one, which makes it a good measure of node purity.', 'start': 4108.471, 'duration': 9.825}, {'end': 4126, 'text': 'A similar rationale is applied to cross entropy, which can also be used for tree growing.', 'start': 4120.256, 'duration': 5.744}, {'end': 4138.778, 'text': 'Now in practice, we can simply import the decision tree classifier model, initialize it and fit it on our data X and Y.', 'start': 4127.912, 'duration': 10.866}], 'summary': 'Gini index and cross entropy measure node purity for decision tree classification.', 'duration': 30.307, 'max_score': 4108.471, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ4108470.jpg'}, {'end': 4194.746, 'src': 'embed', 'start': 4166.55, 'weight': 5, 'content': [{'end': 4172.455, 'text': 'Bagging involves repeatedly drawing samples from the data set, generating B different bootstrap training sets.', 'start': 4166.55, 'duration': 5.905}, {'end': 4179.581, 'text': 'Once all sets are trained, we get a prediction for each set and we average those predictions to get a final prediction.', 'start': 4173.356, 'duration': 6.225}, {'end': 4188.64, 'text': 'So mathematically, we express the final prediction like this, and so you recognize that this is simply the mean of all B predictions.', 'start': 4181.154, 'duration': 7.486}, {'end': 4194.746, 'text': 'This means that we can construct a high number of trees that overfit,', 'start': 4190.423, 'duration': 4.323}], 'summary': 'Bagging involves generating b bootstrap training sets to average predictions, preventing overfitting.', 'duration': 28.196, 'max_score': 4166.55, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ4166550.jpg'}, {'end': 4239.443, 'src': 'embed', 'start': 4212.263, 'weight': 4, 'content': [{'end': 4217.306, 'text': "Now let's see how a random forest can also improve the quality of our predictions.", 'start': 4212.263, 'duration': 5.043}, {'end': 4224.33, 'text': 'Random forests provide an improvement over bagging by making a small tweak that decorrelates the trees.', 'start': 4218.486, 'duration': 5.844}, {'end': 4234.861, 'text': 'Again, multiple trees are grown, but at each split, only a random sample of M predictor is chosen from all P predictors.', 'start': 4225.436, 'duration': 9.425}, {'end': 4239.443, 'text': 'And the split is only allowed to use one of the M predictors.', 'start': 4235.661, 'duration': 3.782}], 'summary': 'Random forests improve predictions by decorrelating trees using a random sample of m predictors from p predictors.', 'duration': 27.18, 'max_score': 4212.263, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ4212263.jpg'}, {'end': 4319.509, 'src': 'embed', 'start': 4291.473, 'weight': 3, 'content': [{'end': 4295.054, 'text': 'Boosting works in a similar way to bagging, but trees are grown sequentially.', 'start': 4291.473, 'duration': 3.581}, {'end': 4298.475, 'text': 'They use the information from previously grown trees.', 'start': 4295.694, 'duration': 2.781}, {'end': 4301.857, 'text': 'This means that the algorithm learns slowly.', 'start': 4299.656, 'duration': 2.201}, {'end': 4305.94, 'text': 'Also, the trees fit the residuals instead of the target.', 'start': 4302.698, 'duration': 3.242}, {'end': 4310.143, 'text': 'So the trees will be small and will slowly improve the predictions.', 'start': 4306.44, 'duration': 3.703}, {'end': 4314.125, 'text': 'There are three tuning parameters for boosting.', 'start': 4312.004, 'duration': 2.121}, {'end': 4319.509, 'text': 'The number of trees B, where if it is too large, then it will overfit.', 'start': 4315.046, 'duration': 4.463}], 'summary': 'Boosting grows trees sequentially, fitting residuals, with 3 tuning parameters for slow learning.', 'duration': 28.036, 'max_score': 4291.473, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ4291473.jpg'}], 'start': 3839.132, 'title': 'Decision trees and lasso regression', 'summary': 'Covers grid search for lasso regression with alpha value of 1 resulting in a mean squared error of 3.036, and introduces decision trees for both regression and classification, highlighting the use of gini index and cross entropy for tree growing. additionally, it covers advanced topics on decision trees including bagging, random forests, and boosting, highlighting techniques to reduce variance and improve performance using python and key parameters for boosting.', 'chapters': [{'end': 4138.778, 'start': 3839.132, 'title': 'Grid search for lasso regression & introduction to decision trees', 'summary': 'Covers grid search for lasso regression with alpha value of 1 resulting in a mean squared error of 3.036, and introduces decision trees for both regression and classification, highlighting the use of gini index and cross entropy for tree growing.', 'duration': 299.646, 'highlights': ['Grid search for lasso regression with alpha value of 1 resulting in a mean squared error of 3.036', 'Introduction to decision trees for both regression and classification', 'Use of Gini index and cross entropy for tree growing']}, {'end': 4365.341, 'start': 4138.778, 'title': 'Advanced decision trees', 'summary': 'Covers advanced topics on decision trees including bagging, random forests, and boosting, highlighting techniques to reduce variance and improve performance using python and key parameters for boosting.', 'duration': 226.563, 'highlights': ['Bagging involves generating B different bootstrap training sets to reduce variance and improve algorithm performance, with final prediction expressed as the mean of all B predictions.', 'Random forests improve over bagging by using a random sample of M predictors at each split, where M is typically the square root of P, with the algorithm easily applicable in Python.', 'Boosting works sequentially, using information from previously grown trees and fitting residuals instead of the target, with tuning parameters including the number of trees B, shrinkage parameter alpha, and number of splits in each tree to control the complexity of the boosted ensemble.']}], 'duration': 526.209, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ3839132.jpg', 'highlights': ['Grid search for lasso regression with alpha value of 1 resulting in a mean squared error of 3.036', 'Introduction to decision trees for both regression and classification', 'Use of Gini index and cross entropy for tree growing', 'Boosting works sequentially, using information from previously grown trees and fitting residuals instead of the target, with tuning parameters including the number of trees B, shrinkage parameter alpha, and number of splits in each tree to control the complexity of the boosted ensemble', 'Random forests improve over bagging by using a random sample of M predictors at each split, where M is typically the square root of P, with the algorithm easily applicable in Python', 'Bagging involves generating B different bootstrap training sets to reduce variance and improve algorithm performance, with final prediction expressed as the mean of all B predictions']}, {'end': 5255.539, 'segs': [{'end': 4532.743, 'src': 'embed', 'start': 4486.475, 'weight': 2, 'content': [{'end': 4494.42, 'text': 'Perfect Now we are going to check if our dataset is balanced because we are in a classification problem.', 'start': 4486.475, 'duration': 7.945}, {'end': 4503.582, 'text': "So I want to make sure that we don't have too much of healthy patients or patients with breast cancer in the dataset that would make it imbalanced.", 'start': 4494.44, 'duration': 9.142}, {'end': 4505.722, 'text': 'So we use the count plot.', 'start': 4504.302, 'duration': 1.42}, {'end': 4508.903, 'text': 'And as you can see, the classes are fairly balanced here.', 'start': 4506.103, 'duration': 2.8}, {'end': 4513.184, 'text': 'So we do not need to do some crazy manipulations in this case.', 'start': 4509.003, 'duration': 4.181}, {'end': 4525.381, 'text': 'Now it will be interesting to define a function to make violin plots that will allow us to see the distribution of each feature for both classes.', 'start': 4514.778, 'duration': 10.603}, {'end': 4528.302, 'text': 'So it can give us some intuition about the data.', 'start': 4526.021, 'duration': 2.281}, {'end': 4532.743, 'text': 'So for example, maybe we will see that most of the healthy controls are younger.', 'start': 4528.562, 'duration': 4.181}], 'summary': 'Dataset is balanced, no need for manipulations. creating violin plots for data intuition.', 'duration': 46.268, 'max_score': 4486.475, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ4486475.jpg'}, {'end': 4628.199, 'src': 'embed', 'start': 4601.396, 'weight': 4, 'content': [{'end': 4606.078, 'text': 'Now we can run the function, actually passing in our X, Y and data.', 'start': 4601.396, 'duration': 4.682}, {'end': 4609.203, 'text': 'and you get the following plots.', 'start': 4607.541, 'duration': 1.662}, {'end': 4618.471, 'text': 'So feel free to study those plots a little bit longer and get an intuition about the dataset we are working with.', 'start': 4609.603, 'duration': 8.868}, {'end': 4628.199, 'text': 'Now we are going to check for null values to make sure that nothing is missing.', 'start': 4622.574, 'duration': 5.625}], 'summary': 'Running function with x, y and data, generating plots and checking for null values.', 'duration': 26.803, 'max_score': 4601.396, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ4601396.jpg'}, {'end': 5130.524, 'src': 'embed', 'start': 5079.379, 'weight': 0, 'content': [{'end': 5089.104, 'text': 'Y-train.ravel. And then you get this following confusion matrix, where only one instance is misclassified, which is not better than random forest,', 'start': 5079.379, 'duration': 9.725}, {'end': 5090.285, 'text': 'but better than our baseline.', 'start': 5089.104, 'duration': 1.181}, {'end': 5094.87, 'text': "Alright, let's cover some theory about support vector machine.", 'start': 5091.147, 'duration': 3.723}, {'end': 5102.156, 'text': 'For classification, we have seen quite a few algorithms such as logistic regression, LDA, QDA, and decision trees.', 'start': 5095.511, 'duration': 6.645}, {'end': 5106.84, 'text': 'Support vector machine is another algorithm used for classification.', 'start': 5103.077, 'duration': 3.763}, {'end': 5112.845, 'text': 'Its main advantage is that it can accommodate non-linear boundaries between classes.', 'start': 5107.601, 'duration': 5.244}, {'end': 5119.511, 'text': 'To understand SVM, we must first understand the maximum margin classifier.', 'start': 5114.587, 'duration': 4.924}, {'end': 5125.922, 'text': 'Like I said, the maximum margin classifier is the basic algorithm from which SVM extends.', 'start': 5120.56, 'duration': 5.362}, {'end': 5130.524, 'text': 'It relies on separating different classes using a hyperplane.', 'start': 5126.743, 'duration': 3.781}], 'summary': 'Support vector machine achieves one misclassification, better than the baseline.', 'duration': 51.145, 'max_score': 5079.379, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ5079379.jpg'}], 'start': 4366.141, 'title': 'Implementing classification models', 'summary': 'Covers label encoding, dataset splitting, decision tree classifier, bagging, random forest, and boosting classifiers, achieving varying levels of accuracy. it also explores support vector machine theory for accommodating non-linear boundaries between classes.', 'chapters': [{'end': 4660.486, 'start': 4366.141, 'title': 'Decision trees in breast cancer detection', 'summary': 'Covers the implementation of decision trees for breast cancer identification, with a dataset containing balanced classes, no null values, and visualization of feature distributions.', 'duration': 294.345, 'highlights': ['The dataset contains balanced classes, ensuring a fair representation of healthy patients and patients with breast cancer, as depicted by the count plot.', 'No null values are present in the dataset, ensuring data completeness and accuracy in the analysis.', 'Visualization of feature distributions using violin plots provides insight into the data, allowing for the observation of feature distributions for both healthy controls and patients with breast cancer.']}, {'end': 5255.539, 'start': 4660.687, 'title': 'Implementing classification models', 'summary': 'Covers label encoding, splitting the dataset, building a decision tree classifier, and implementing bagging, random forest, and boosting classifiers, achieving varying levels of accuracy. it also explores the theory behind support vector machine and how it accommodates non-linear boundaries between classes.', 'duration': 594.852, 'highlights': ['The chapter covers label encoding, splitting the dataset, building a decision tree classifier, and implementing bagging, Random Forest, and boosting classifiers, achieving varying levels of accuracy.', 'The support vector machine is covered, explaining its ability to accommodate non-linear boundaries between classes using kernels.']}], 'duration': 889.398, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ4366141.jpg', 'highlights': ['The chapter covers label encoding, dataset splitting, decision tree classifier, bagging, random forest, and boosting classifiers, achieving varying levels of accuracy.', 'The support vector machine is covered, explaining its ability to accommodate non-linear boundaries between classes using kernels.', 'The dataset contains balanced classes, ensuring a fair representation of healthy patients and patients with breast cancer, as depicted by the count plot.', 'Visualization of feature distributions using violin plots provides insight into the data, allowing for the observation of feature distributions for both healthy controls and patients with breast cancer.', 'No null values are present in the dataset, ensuring data completeness and accuracy in the analysis.']}, {'end': 7103.849, 'segs': [{'end': 5356.541, 'src': 'embed', 'start': 5258.121, 'weight': 1, 'content': [{'end': 5262.864, 'text': 'Here is an example that we will implement later on during the coding portion of this section.', 'start': 5258.121, 'duration': 4.743}, {'end': 5266.976, 'text': "Here, the classes can be linearly separated, so it's easy enough.", 'start': 5263.874, 'duration': 3.102}, {'end': 5276.36, 'text': "However, notice the outlier on the left, and we can use regularization to account for it or not, and we'll see how that impacts the model.", 'start': 5267.696, 'duration': 8.664}, {'end': 5284.305, 'text': 'Here is another example that we will code and, as you can see here, the boundary is definitely not linear,', 'start': 5278.221, 'duration': 6.084}, {'end': 5288.767, 'text': 'but SVM does a pretty good job at finding a boundary and separating each class.', 'start': 5284.305, 'duration': 4.462}, {'end': 5290.992, 'text': "So that's it for the theory.", 'start': 5290.011, 'duration': 0.981}, {'end': 5295.555, 'text': "Now let's move on to the coding project and generate those plots ourselves.", 'start': 5291.252, 'duration': 4.303}, {'end': 5301.98, 'text': 'All right, so prepare your notebooks and grab the data from the link in the description.', 'start': 5297.677, 'duration': 4.303}, {'end': 5307.324, 'text': "In this tutorial, we're actually gonna import five different datasets.", 'start': 5302.881, 'duration': 4.443}, {'end': 5309.846, 'text': "So you can see me, I'm checking them right now.", 'start': 5307.624, 'duration': 2.222}, {'end': 5315.57, 'text': 'So x6data123, and then spamtest and spamtrain.mat.', 'start': 5309.946, 'duration': 5.624}, {'end': 5325.159, 'text': 'So as always, we start off by importing all the libraries that we will need throughout this project.', 'start': 5318.496, 'duration': 6.663}, {'end': 5330.762, 'text': 'By the way, these exercises are taken from the machine learning course by Andrew Ng.', 'start': 5325.96, 'duration': 4.802}, {'end': 5335.164, 'text': 'I am simply solving them using Python here.', 'start': 5331.222, 'duration': 3.942}, {'end': 5340.227, 'text': "By the way, it's an amazing course and I will leave the link in the description if you want to check it out.", 'start': 5335.705, 'duration': 4.522}, {'end': 5341.728, 'text': 'I definitely recommend it.', 'start': 5340.447, 'duration': 1.281}, {'end': 5346.876, 'text': 'So we import numpy pandas, matplotlib.pyplot,', 'start': 5342.714, 'duration': 4.162}, {'end': 5356.541, 'text': "also matplotlib.cm and from scipy.io we'll import loadmat and finally matplotlib inline to show our beautiful graphs.", 'start': 5346.876, 'duration': 9.665}], 'summary': 'Implementing svm for non-linear boundaries, importing multiple datasets for coding project.', 'duration': 98.42, 'max_score': 5258.121, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ5258121.jpg'}, {'end': 5886.893, 'src': 'embed', 'start': 5843.495, 'weight': 9, 'content': [{'end': 5847.818, 'text': 'And then you specify the decision function shape to OBR.', 'start': 5843.495, 'duration': 4.323}, {'end': 5854.222, 'text': 'Then you fit the model on your data.', 'start': 5851.48, 'duration': 2.742}, {'end': 5866.455, 'text': 'And now we will plot the data as well as the boundary.', 'start': 5861.351, 'duration': 5.104}, {'end': 5876.824, 'text': 'So here, actually, we can just grab this line from the previous cell, because it will be exactly the same.', 'start': 5868.417, 'duration': 8.407}, {'end': 5879.827, 'text': "We're just plotting the same data again.", 'start': 5876.844, 'duration': 2.983}, {'end': 5884.771, 'text': 'And now we will plot the boundary on top of this data.', 'start': 5879.847, 'duration': 4.924}, {'end': 5886.893, 'text': 'Sorry, I accidentally ran this cell.', 'start': 5885.051, 'duration': 1.842}], 'summary': 'Fitting decision function shape to obr and plotting data and boundary.', 'duration': 43.398, 'max_score': 5843.495, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ5843495.jpg'}, {'end': 6079.926, 'src': 'embed', 'start': 6047.697, 'weight': 2, 'content': [{'end': 6051.599, 'text': 'So here we are going to use a large regularization parameter.', 'start': 6047.697, 'duration': 3.902}, {'end': 6053.54, 'text': "We'll set it to C equal to 100.", 'start': 6051.619, 'duration': 1.921}, {'end': 6060.864, 'text': 'And actually I simply gonna grab everything from this cell here and just copy paste it below because the code is exactly the same.', 'start': 6053.54, 'duration': 7.324}, {'end': 6065.266, 'text': 'We are simply changing the value of the hyper parameter.', 'start': 6060.924, 'duration': 4.342}, {'end': 6073.591, 'text': "So we don't need to import SVM again and we'll simply use C equal to 100 and see what happens next.", 'start': 6065.687, 'duration': 7.904}, {'end': 6079.926, 'text': 'So as you can see now, the hyperplane shifted to account for the outlier.', 'start': 6075.882, 'duration': 4.044}], 'summary': 'Using a large regularization parameter (c=100), the hyperplane shifted to account for the outlier.', 'duration': 32.229, 'max_score': 6047.697, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ6047697.jpg'}, {'end': 6260.762, 'src': 'embed', 'start': 6212.984, 'weight': 3, 'content': [{'end': 6223.013, 'text': 'Perfect Then our classifier will be equal to SVM.SVC.', 'start': 6212.984, 'duration': 10.029}, {'end': 6227.256, 'text': 'We pass in the kernel and this time is going to be equal to RBF.', 'start': 6223.613, 'duration': 3.643}, {'end': 6231.86, 'text': 'So for radial basis function, gamma will be equal to gamma.', 'start': 6227.857, 'duration': 4.003}, {'end': 6238.866, 'text': 'We set the regularization parameter to one and the decision function shape will again be equal to OVR.', 'start': 6232.16, 'duration': 6.706}, {'end': 6251.436, 'text': 'next we fit the classifier to our data.', 'start': 6246.633, 'duration': 4.803}, {'end': 6260.762, 'text': 'so x underscore two and y underscore two dot revel.', 'start': 6251.436, 'duration': 9.326}], 'summary': 'Using svm.svc with rbf kernel, gamma, regularization parameter of 1, ovr decision function, and fitting the classifier to the data.', 'duration': 47.778, 'max_score': 6212.984, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ6212984.jpg'}, {'end': 6514.684, 'src': 'embed', 'start': 6461.769, 'weight': 7, 'content': [{'end': 6467.59, 'text': 'So this is when we need to use cross-validation in order to find the best parameters for the best boundary.', 'start': 6461.769, 'duration': 5.821}, {'end': 6476.634, 'text': 'So I will specify a list of possible values for sigma.', 'start': 6472.352, 'duration': 4.282}, {'end': 6479.516, 'text': "So we'll go from 0.01, 0.03, 0.1, 0.3, 1, 3, 10, and 30.", 'start': 6477.895, 'duration': 1.621}, {'end': 6482.357, 'text': "And we'll do the same for the regularization parameter C.", 'start': 6479.516, 'duration': 2.841}, {'end': 6485.519, 'text': 'So the same values, 0.01, 0.03, 0.1, 0.3, and then 1, 3, 10, and 30.', 'start': 6482.357, 'duration': 3.162}, {'end': 6514.684, 'text': "Perfect Now we'll initialize an empty list of errors and an empty list for Sigma and C.", 'start': 6485.519, 'duration': 29.165}], 'summary': 'Using cross-validation to find best parameters for boundary with sigma and c values ranging from 0.01 to 30.', 'duration': 52.915, 'max_score': 6461.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ6461769.jpg'}, {'end': 6893.727, 'src': 'embed', 'start': 6864.78, 'weight': 4, 'content': [{'end': 6868.02, 'text': 'and the mistake again here is i wrote simga instead of sigma.', 'start': 6864.78, 'duration': 3.24}, {'end': 6869.441, 'text': 'very sorry about that, guys.', 'start': 6868.02, 'duration': 1.421}, {'end': 6873.321, 'text': "hopefully you're not making the same mistakes as i am, and as you can see here,", 'start': 6869.441, 'duration': 3.88}, {'end': 6885.664, 'text': "we have our best boundary found from cross validation with support vector machine, so it's actually not that bad.", 'start': 6873.321, 'duration': 12.343}, {'end': 6890.825, 'text': "and finally, let's use svm for spam classification for our emails.", 'start': 6885.664, 'duration': 5.161}, {'end': 6893.727, 'text': 'right, So spam train.', 'start': 6890.825, 'duration': 2.902}], 'summary': "Mistake in writing 'sigma', but best boundary found with svm in cross validation. svm to be used for spam classification.", 'duration': 28.947, 'max_score': 6864.78, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ6864780.jpg'}, {'end': 7103.849, 'src': 'embed', 'start': 7064.094, 'weight': 0, 'content': [{'end': 7066.635, 'text': 'And we do the same for the test accuracy.', 'start': 7064.094, 'duration': 2.541}, {'end': 7081.841, 'text': 'Running everything, we have a key error for X.', 'start': 7077.359, 'duration': 4.482}, {'end': 7085.662, 'text': "Yes, that's because here it should be X test and Y test.", 'start': 7081.841, 'duration': 3.821}, {'end': 7087.663, 'text': 'Sorry about this little mistake.', 'start': 7086.182, 'duration': 1.481}, {'end': 7102.608, 'text': 'So if we run this cell now, our model is training and fitting, and you get a training accuracy of 99.8% and a test accuracy of 98.9%,', 'start': 7088.942, 'duration': 13.666}, {'end': 7103.849, 'text': 'which is very good.', 'start': 7102.608, 'duration': 1.241}], 'summary': 'Model achieves 99.8% training accuracy and 98.9% test accuracy.', 'duration': 39.755, 'max_score': 7064.094, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7064094.jpg'}], 'start': 5258.121, 'title': 'Implementing support vector machines (svm)', 'summary': 'Discusses implementing svm for linear and non-linear separable data, exploring the effect of regularization, fitting svm models, visualizing non-linear boundaries, and using cross-validation to find optimal parameters for svm classification, achieving a training accuracy of 99.8% and a test accuracy of 98.9%.', 'chapters': [{'end': 5356.541, 'start': 5258.121, 'title': 'Implementing svm for linear and non-linear separable data', 'summary': 'Discusses implementing support vector machines (svm) for linearly separable data and non-linearly separable data, importing five different datasets, and using python to solve exercises from the machine learning course by andrew ng.', 'duration': 98.42, 'highlights': ['The chapter explains how to use regularization to account for outliers in linearly separable data and demonstrates the impact on the model.', 'It also shows how SVM performs well in finding a boundary and separating each class in non-linearly separable data.', 'The tutorial involves importing five different datasets, including x6data123, spamtest, and spamtrain.mat.', 'The chapter emphasizes the use of Python to solve exercises from the machine learning course by Andrew Ng, importing necessary libraries such as numpy, pandas, matplotlib.pyplot, matplotlib.cm, and scipy.io.']}, {'end': 5842.835, 'start': 5358.022, 'title': 'Svm classifier & plotting function', 'summary': 'Discusses defining data paths, writing a plotting function, and exploring the effect of regularization on svm, including the process of loading data, plotting data, and setting the regularization parameter to one.', 'duration': 484.813, 'highlights': ['The chapter discusses defining data paths, writing a plotting function, and exploring the effect of regularization on SVM.', 'Setting the regularization parameter to one for SVM.', 'Defining data paths for x6 datasets and spam train and test.', 'Writing a helper function for plotting data.', 'Loading data and plotting the data set.']}, {'end': 6138.553, 'start': 5843.495, 'title': 'Support vector machine modeling', 'summary': 'Demonstrates the process of fitting a support vector machine model to plot linear and non-linear boundaries, showing the impact of different regularization parameters and the implications of overfitting, with a practical example of a non-linear boundary.', 'duration': 295.058, 'highlights': ['The model is fitted with a specified decision function shape to OBR, and the data is plotted along with the boundary.', 'The impact of a high regularization parameter (C=100) on the hyperplane is discussed, showing how it shifts to account for the outlier but may lead to overfitting.', 'A practical example of a non-linear boundary using a second data set is presented, with the process of specifying X and Y, and plotting the data using a helper function.']}, {'end': 6431.305, 'start': 6140.994, 'title': 'Svm non-linear boundary visualization', 'summary': 'Demonstrates setting limits on the x and y-axis for a non-linear boundary visualization using svm with a radial basis function kernel and explores data separability, with a specific example using gamma and plotting the hyperplane.', 'duration': 290.311, 'highlights': ['The chapter demonstrates setting limits on the x and y-axis for a non-linear boundary visualization using SVM.', 'Exploring SVM with a radial basis function kernel and defining the parameter gamma.', 'Plotting the hyperplane and visualizing the SVM-predicted boundary.']}, {'end': 7103.849, 'start': 6431.305, 'title': 'Cross-validation for svm parameters', 'summary': 'Explains the process of using cross-validation to find the best parameters for support vector machine (svm) classification, achieving an optimal sigma of 0.1 and c of 1, and applying svm for spam classification with a training accuracy of 99.8% and a test accuracy of 98.9%.', 'duration': 672.544, 'highlights': ['Using cross-validation to find the best parameters for SVM classification', 'Applying SVM for spam classification with high accuracy']}], 'duration': 1845.728, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ5258121.jpg', 'highlights': ['Achieved a training accuracy of 99.8% and a test accuracy of 98.9%', 'Emphasized the use of Python and necessary libraries for exercises from the machine learning course', 'Demonstrated the impact of a high regularization parameter (C=100) on the hyperplane', 'Discussed implementing SVM with a radial basis function kernel and defining the parameter gamma', 'Applied SVM for spam classification with high accuracy', 'Explained how to use regularization to account for outliers in linearly separable data and its impact on the model', "Explored SVM's performance in finding boundaries and separating classes in non-linearly separable data", 'Used cross-validation to find the best parameters for SVM classification', 'Imported five different datasets including x6data123, spamtest, and spamtrain.mat', 'Fitted the model with a specified decision function shape to OBR and plotted the data along with the boundary']}, {'end': 7613.025, 'segs': [{'end': 7132.798, 'src': 'embed', 'start': 7104.769, 'weight': 1, 'content': [{'end': 7108.11, 'text': "Moving on now to unsupervised learning, let's cover some theory.", 'start': 7104.769, 'duration': 3.341}, {'end': 7116.094, 'text': 'Unsupervised learning is a set of statistical tools for scenarios in which we have features, but no targets.', 'start': 7109.391, 'duration': 6.703}, {'end': 7119.293, 'text': 'This means that we cannot make predictions.', 'start': 7117.352, 'duration': 1.941}, {'end': 7126.275, 'text': 'Instead, we are interested in finding a way to visualize data or discovering a subgroup of similar observations.', 'start': 7119.893, 'duration': 6.382}, {'end': 7132.798, 'text': 'Unsupervised tends to be a bit more challenging because the analysis is subjective.', 'start': 7128.316, 'duration': 4.482}], 'summary': 'Unsupervised learning involves visualizing data and identifying similar observations without targets for predictions.', 'duration': 28.029, 'max_score': 7104.769, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7104769.jpg'}, {'end': 7202.72, 'src': 'embed', 'start': 7152.56, 'weight': 0, 'content': [{'end': 7158.246, 'text': 'PCA is a process by which principal components are computed and used to better understand data.', 'start': 7152.56, 'duration': 5.686}, {'end': 7160.929, 'text': 'They can also be used for visualizations.', 'start': 7158.927, 'duration': 2.002}, {'end': 7168.945, 'text': 'Now, what is a principal component? Well, suppose you want to visualize n observations on a set of p features.', 'start': 7162.335, 'duration': 6.61}, {'end': 7178.139, 'text': "You could do a 2D plot for each two features at a time, but that's not very efficient and unrealistic if p is very large.", 'start': 7170.006, 'duration': 8.133}, {'end': 7186.911, 'text': 'With PCA, you can find a low-dimensional representation of the dataset that contains as much of the variance as possible.', 'start': 7179.727, 'duration': 7.184}, {'end': 7193.775, 'text': 'That means that you will only consider the most interesting features since they account for the majority of the variance.', 'start': 7187.651, 'duration': 6.124}, {'end': 7202.72, 'text': 'And therefore, a principal component is simply the normalized linear combination of a feature that has the largest variance.', 'start': 7195.356, 'duration': 7.364}], 'summary': 'Pca computes principal components to visualize high-dimensional data efficiently.', 'duration': 50.16, 'max_score': 7152.56, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7152560.jpg'}, {'end': 7256.323, 'src': 'embed', 'start': 7227.41, 'weight': 2, 'content': [{'end': 7231.653, 'text': "Here's an example of how you can apply PCA in Python.", 'start': 7227.41, 'duration': 4.243}, {'end': 7233.465, 'text': 'this case.', 'start': 7232.984, 'duration': 0.481}, {'end': 7238.008, 'text': 'actually, we are trying to visualize the iris data set in 2d.', 'start': 7233.465, 'duration': 4.543}, {'end': 7242.512, 'text': 'this is something that we will apply linear on during the coding portion.', 'start': 7238.008, 'duration': 4.504}, {'end': 7248.096, 'text': 'so this data set contains more than two features for each species of iris.', 'start': 7242.512, 'duration': 5.584}, {'end': 7256.323, 'text': 'so using this snippet, we can initialize pca and then specify that we only want the first two principal components,', 'start': 7248.096, 'duration': 8.227}], 'summary': 'Applying pca in python to visualize iris data set in 2d.', 'duration': 28.913, 'max_score': 7227.41, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7227410.jpg'}, {'end': 7493.154, 'src': 'embed', 'start': 7445.857, 'weight': 4, 'content': [{'end': 7448.719, 'text': "Now let's take a look at hierarchical clustering.", 'start': 7445.857, 'duration': 2.862}, {'end': 7455.647, 'text': 'As I mentioned, the potential disadvantage of k-means is that you must specify the number of clusters,', 'start': 7450.905, 'duration': 4.742}, {'end': 7458.588, 'text': "and sometimes you simply don't know how many clusters you need.", 'start': 7455.647, 'duration': 2.941}, {'end': 7464.17, 'text': 'This is when hierarchical clustering comes in, because you do not need to specify the number of clusters.', 'start': 7459.388, 'duration': 4.782}, {'end': 7469.312, 'text': 'The most common type of hierarchical clustering is called agglomerative clustering.', 'start': 7464.75, 'duration': 4.562}, {'end': 7476.335, 'text': 'It generates a dendrogram from the leaves, and the clusters are combined into larger clusters up to the trunk.', 'start': 7469.972, 'duration': 6.363}, {'end': 7480.249, 'text': 'Here is an example of gendergrams.', 'start': 7478.288, 'duration': 1.961}, {'end': 7488.072, 'text': 'We see the individual observations at the bottom, and they are combined into larger clusters as you move up in the y-axis.', 'start': 7480.789, 'duration': 7.283}, {'end': 7493.154, 'text': 'The algorithm is fairly easy to understand.', 'start': 7491.013, 'duration': 2.141}], 'summary': 'Hierarchical clustering does not require specifying the number of clusters and uses agglomerative clustering to combine clusters into larger ones.', 'duration': 47.297, 'max_score': 7445.857, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7445857.jpg'}], 'start': 7104.769, 'title': 'Unsupervised learning: pca and clustering', 'summary': 'Covers the theory of unsupervised learning, focusing on principal component analysis (pca) and clustering algorithms, which provide tools for visualizing data and discovering subgroups of similar observations. it also demonstrates the application of pca in python to visualize the iris dataset in 2d, including the explained variance ratio and its use in plotting the features. additionally, it explores k-means clustering, its process, and the potential disadvantage of needing to specify the number of clusters, along with hierarchical clustering, its algorithm, and the impact of different types of linkage on the resulting dendrogram.', 'chapters': [{'end': 7225.308, 'start': 7104.769, 'title': 'Unsupervised learning: pca and clustering', 'summary': 'Covers the theory of unsupervised learning, focusing on principal component analysis (pca) and clustering algorithms, which provide tools for visualizing data and discovering subgroups of similar observations.', 'duration': 120.539, 'highlights': ['Unsupervised learning is used for scenarios with features but no targets, aiming to visualize data or find similar subgroups.', 'PCA helps find a low-dimensional representation of the dataset with the most variance, allowing for efficient visualization and feature selection.', 'Principal components are computed to better understand data and can be used for visualizations.']}, {'end': 7613.025, 'start': 7227.41, 'title': 'Pca visualization and clustering methods', 'summary': 'Demonstrates the application of pca in python to visualize the iris dataset in 2d, including the explained variance ratio and its use in plotting the features. it then explores k-means clustering, its process, and the potential disadvantage of needing to specify the number of clusters. the chapter also covers hierarchical clustering, its algorithm, and the impact of different types of linkage on the resulting dendrogram.', 'duration': 385.615, 'highlights': ['The chapter demonstrates the application of PCA in Python to visualize the iris dataset in 2D.', 'It explores k-means clustering, its process, and the potential disadvantage of needing to specify the number of clusters.', 'The chapter covers hierarchical clustering, its algorithm, and the impact of different types of linkage on the resulting dendrogram.']}], 'duration': 508.256, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7104769.jpg', 'highlights': ['PCA helps find a low-dimensional representation of the dataset with the most variance, allowing for efficient visualization and feature selection.', 'Unsupervised learning is used for scenarios with features but no targets, aiming to visualize data or find similar subgroups.', 'The chapter demonstrates the application of PCA in Python to visualize the iris dataset in 2D.', 'Principal components are computed to better understand data and can be used for visualizations.', 'It explores k-means clustering, its process, and the potential disadvantage of needing to specify the number of clusters.', 'The chapter covers hierarchical clustering, its algorithm, and the impact of different types of linkage on the resulting dendrogram.']}, {'end': 8470.705, 'segs': [{'end': 7691.113, 'src': 'embed', 'start': 7615.148, 'weight': 3, 'content': [{'end': 7616.869, 'text': "Let's apply what we learned in Python now.", 'start': 7615.148, 'duration': 1.721}, {'end': 7620.771, 'text': 'These exercises are available as examples on the sklearn website.', 'start': 7617.329, 'duration': 3.442}, {'end': 7623.573, 'text': 'I am simply reworking them a bit or explaining them here.', 'start': 7621.032, 'duration': 2.541}, {'end': 7629.136, 'text': 'The links are in the description and the complete notebook on GitHub is also in the description down below.', 'start': 7624.093, 'duration': 5.043}, {'end': 7632.859, 'text': "So we'll start off by importing some libraries.", 'start': 7630.978, 'duration': 1.881}, {'end': 7634.159, 'text': 'We will need numpy.', 'start': 7633.079, 'duration': 1.08}, {'end': 7638.522, 'text': "We'll also need matplotlib.pyplot as PLT.", 'start': 7634.52, 'duration': 4.002}, {'end': 7644.746, 'text': 'And finally, from sklearn.utils, we will import shuffle.', 'start': 7639.923, 'duration': 4.823}, {'end': 7649.595, 'text': "So let's kick off this tutorial with a clustering.", 'start': 7647.093, 'duration': 2.502}, {'end': 7659.043, 'text': "We'll do color quantization with k-means, which is a technique to reduce the number of colors of an image while keeping the integrity of the image.", 'start': 7649.955, 'duration': 9.088}, {'end': 7674.996, 'text': 'So to do that, we will need from sklearn.datasets import load underscore sample underscore image, and from sklearn.cluster, we will import k-means.', 'start': 7662.045, 'duration': 12.951}, {'end': 7682.063, 'text': 'Now, after importing our libraries, we will load the image of a flower.', 'start': 7677.898, 'duration': 4.165}, {'end': 7689.371, 'text': 'So the flower will be equal to load sample image, and we will pass in the name of the image.', 'start': 7683.384, 'duration': 5.987}, {'end': 7691.113, 'text': 'In this case, it is flower.jpg.', 'start': 7689.751, 'duration': 1.362}], 'summary': 'Applying python concepts to perform color quantization using k-means clustering on a flower image.', 'duration': 75.965, 'max_score': 7615.148, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7615148.jpg'}, {'end': 7821.221, 'src': 'embed', 'start': 7784.5, 'weight': 1, 'content': [{'end': 7792.643, 'text': "flower and then we'll reshape with the width times, the height, and the other dimension will be the depth awesome.", 'start': 7784.5, 'duration': 8.143}, {'end': 7800.267, 'text': 'now we will reduce the number of colors to 64 by running the k-means algorithm, where k will be set to 64..', 'start': 7792.643, 'duration': 7.624}, {'end': 7807.049, 'text': 'so our image sample will be equal.', 'start': 7800.267, 'duration': 6.782}, {'end': 7821.221, 'text': "to shuffle the image array, We'll give it a random state equal to 42, so that the results are constant whenever we rerun the cell.", 'start': 7807.049, 'duration': 14.172}], 'summary': 'Reshaping image dimensions and reducing colors to 64 using k-means algorithm.', 'duration': 36.721, 'max_score': 7784.5, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7784500.jpg'}, {'end': 8181.31, 'src': 'embed', 'start': 8152.253, 'weight': 0, 'content': [{'end': 8154.634, 'text': 'So feel free to play around with this number of colors with yourself.', 'start': 8152.253, 'duration': 2.381}, {'end': 8159.777, 'text': "Now let's work with PCA for dimensionality reduction.", 'start': 8156.035, 'duration': 3.742}, {'end': 8165.316, 'text': 'Here we will work with the iris dataset.', 'start': 8163.294, 'duration': 2.022}, {'end': 8174.043, 'text': 'This dataset has four features about three different kinds of iris flowers and our goal is to visualize the dataset in two dimensions.', 'start': 8165.636, 'duration': 8.407}, {'end': 8181.31, 'text': "So from sklearn.datasets we'll import load iris and from sklearn.decomposition import pca.", 'start': 8175.284, 'duration': 6.026}], 'summary': 'Using pca to visualize iris dataset in 2 dimensions', 'duration': 29.057, 'max_score': 8152.253, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ8152253.jpg'}, {'end': 8295.528, 'src': 'embed', 'start': 8269.272, 'weight': 2, 'content': [{'end': 8276.436, 'text': 'Running this cell, as you can see the first principal component explains 92% of the variance and the second one 5%.', 'start': 8269.272, 'duration': 7.164}, {'end': 8282.36, 'text': 'So that means that a total of 97% of the variance is explained with only two components.', 'start': 8276.436, 'duration': 5.924}, {'end': 8295.528, 'text': 'So now we are ready to plot our data set in 2D and that data, that newly transformed data contains about 97% of the variance of the original data set.', 'start': 8283.901, 'duration': 11.627}], 'summary': 'Two principal components explain 97% of variance.', 'duration': 26.256, 'max_score': 8269.272, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ8269272.jpg'}, {'end': 8468.923, 'src': 'heatmap', 'start': 8376.939, 'weight': 0.996, 'content': [{'end': 8387.466, 'text': 'so this this is basically the x-axis and then the y-axis, and the color will be equal to the color at this point in the loop.', 'start': 8376.939, 'duration': 10.527}, {'end': 8392.895, 'text': 'Alpha will be equal to 0.8 and then LW is at equal to LW that we specified above.', 'start': 8388.066, 'duration': 4.829}, {'end': 8399.746, 'text': 'Finally, the label will be equal to the target name at that specific step in the loop.', 'start': 8393.656, 'duration': 6.09}, {'end': 8414.307, 'text': 'now we will simply put a legend on our plot the location.', 'start': 8409.065, 'duration': 5.242}, {'end': 8426.43, 'text': "sorry, the location equal will be equal to best, and we don't want any shadow.", 'start': 8414.307, 'duration': 12.123}, {'end': 8430.531, 'text': "finally, let's set a title to our plot.", 'start': 8426.43, 'duration': 4.101}, {'end': 8436.28, 'text': 'so pca of iris dataset Running this cell.', 'start': 8430.531, 'duration': 5.749}, {'end': 8439.764, 'text': 'as you can see, now we get this plot right here.', 'start': 8436.28, 'duration': 3.484}, {'end': 8446.292, 'text': 'And so you can visualize in two dimension a dataset that contained four features and three classes.', 'start': 8439.904, 'duration': 6.388}, {'end': 8454.982, 'text': 'So now you could follow up with some classifier, maybe decision trees on this transform dataset to classify each kind of flower.', 'start': 8446.953, 'duration': 8.029}, {'end': 8459.353, 'text': "All right, so that's it for this data science crash course.", 'start': 8456.089, 'duration': 3.264}, {'end': 8462.096, 'text': 'I hope that you enjoyed it and that you learned something interesting.', 'start': 8459.593, 'duration': 2.503}, {'end': 8468.923, 'text': 'If you want more videos on this topic or videos on end-to-end data science projects, please check out my YouTube channel.', 'start': 8462.696, 'duration': 6.227}], 'summary': 'Visualizing pca of iris dataset in 2d for feature and class analysis.', 'duration': 91.984, 'max_score': 8376.939, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ8376939.jpg'}], 'start': 7615.148, 'title': 'Applying k-means and pca for image and data visualization', 'summary': 'Covers color quantization using k-means clustering to reduce image colors, pca for dimensionality reduction on the iris dataset, achieving a 97% variance explanation with two principal components, and ending with a 2d visualization of the iris dataset.', 'chapters': [{'end': 7720.144, 'start': 7615.148, 'title': 'Python clustering tutorial', 'summary': 'Demonstrates color quantization using k-means clustering to reduce the number of colors in an image, while maintaining its integrity, and involves importing libraries, loading a sample image, and normalizing the color values by dividing by 255.', 'duration': 104.996, 'highlights': ['The chapter starts by importing necessary libraries such as numpy, matplotlib.pyplot, and sklearn.utils for shuffle.', 'The tutorial involves color quantization using k-means clustering to reduce the number of colors in an image while maintaining its integrity.', 'The process includes loading a sample image of a flower using sklearn.datasets and normalizing the color values by converting to floats and dividing by 255.']}, {'end': 8470.705, 'start': 7720.864, 'title': 'Using k-means and pca for image and data visualization', 'summary': 'Demonstrates using k-means algorithm to reduce the number of colors in an image to 64, and then applies pca for dimensionality reduction on the iris dataset, achieving a 97% variance explanation with only two principal components, ending with a 2d visualization of the iris dataset.', 'duration': 749.841, 'highlights': ['Using K-means to reduce colors in an image to 64', 'Applying PCA for dimensionality reduction on the iris dataset']}], 'duration': 855.557, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XU5pw3QRYjQ/pics/XU5pw3QRYjQ7615148.jpg', 'highlights': ['Applying PCA for dimensionality reduction on the iris dataset', 'Using K-means to reduce colors in an image to 64', 'Achieving a 97% variance explanation with two principal components', 'The tutorial involves color quantization using k-means clustering to reduce the number of colors in an image while maintaining its integrity', 'The process includes loading a sample image of a flower using sklearn.datasets and normalizing the color values by converting to floats and dividing by 255', 'The chapter starts by importing necessary libraries such as numpy, matplotlib.pyplot, and sklearn.utils for shuffle']}], 'highlights': ['SVM achieved 98.9% test accuracy', 'Logistic regression achieved AUC of 1', 'Linear regression with R-squared value of 0.897', 'Practical application of resampling methods', 'PCA for dimensionality reduction on the iris dataset', 'Decision trees for regression and classification', 'Model validation using sensitivity, specificity, and ROC curve', 'Quadratic Discriminant Analysis better for large datasets', 'Theoretical understanding crucial for job interviews', 'Data science setup detailed with Anaconda installation']}