title

Scikit-Learn Course - Machine Learning in Python Tutorial

description

Scikit-learn is a free software machine learning library for the Python programming language. Learn about machine learning using scikit-learn in this full course.
💻 Code: https://github.com/DL-Academy/MachineLearningSKLearn
🔗 Scikit-learn website: https://scikit-learn.org
✏️ Course from DL Academy. Check out their YouTube channel: https://www.youtube.com/channel/UCTgBlZ1fmNa87NUY1xvoxpg
🔗 View more courses here: https://thedlacademy.com/
⭐️ Course Contents ⭐️
Chapter 1 - Getting Started with Machine Learning
⌨️ (0:00) Introduction
⌨️ (0:22) Installing SKlearn
⌨️ (3:37) Plot a Graph
⌨️ (7:33) Features and Labels_1
⌨️ (11:45) Save and Open a Model
Chapter 2 - Taking a look at some machine learning algorithms
⌨️ (13:47) Classification
⌨️ (17:28) Train Test Split
⌨️ (25:31) What is KNN
⌨️ (33:48) KNN Example
⌨️ (43:54) SVM Explained
⌨️ (51:11) SVM Example
⌨️ (57:46) Linear regression
⌨️ (1:07:49) Logistic vs linear regression
⌨️ (1:23:12) Kmeans and the math beind it
⌨️ (1:31:08) KMeans Example
Chapter 3 - Artificial Intelligence and the science behind It
⌨️ (1:42:02) Neural Network
⌨️ (1:56:03) Overfitting and Underfitting
⌨️ (2:03:05) Backpropagation
⌨️ (2:18:16) Cost Function and Gradient Descent
⌨️ (2:26:24) CNN
⌨️ (2:31:46) Handwritten Digits Recognizer
--
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detail

{'title': 'Scikit-Learn Course - Machine Learning in Python Tutorial', 'heatmap': [{'end': 2198.777, 'start': 1779.491, 'weight': 0.934}, {'end': 3351.437, 'start': 3244.822, 'weight': 0.86}, {'end': 5653.516, 'start': 5545.217, 'weight': 0.835}], 'summary': 'Tutorial covers scikit-learn installation, plotting data with matplotlib, model loading, classification, knn machine learning model with 0.75 accuracy, svm basics, linear and logistic regression, k-means clustering, neural networks fundamentals, training process, and handwritten digit recognition with an mlp classifier achieving 0.97 accuracy.', 'chapters': [{'end': 202.615, 'segs': [{'end': 97.608, 'src': 'embed', 'start': 38.14, 'weight': 0, 'content': [{'end': 47.226, 'text': 'and here, if you, if we just read um, the description on wikipedia, it says that scikit-learn is a free software for machine learning.', 'start': 38.14, 'duration': 9.086}, {'end': 57.95, 'text': "so basically it's a bunch of libraries uh that that um are programmed for Python which allow you to do machine learning.", 'start': 47.226, 'duration': 10.724}, {'end': 67.012, 'text': "so let's just click on the first link and as you can see, this is the scikit-learn website.", 'start': 57.95, 'duration': 9.062}, {'end': 72.033, 'text': "and here it says it's simple and efficient tool for predictive data analysis,", 'start': 67.012, 'duration': 5.021}, {'end': 79.014, 'text': 'and scikit-learn really is one of the best machine learning libraries out there.', 'start': 72.033, 'duration': 6.981}, {'end': 88.605, 'text': "You have other good ones, such as TensorFlow, but it's mainly going to be focused on scikit-learn.", 'start': 81.3, 'duration': 7.305}, {'end': 94.05, 'text': 'So here, go on the top bar and click on Install.', 'start': 89.066, 'duration': 4.984}, {'end': 97.608, 'text': 'And here it tells you that you can use pip install.', 'start': 94.966, 'duration': 2.642}], 'summary': 'Scikit-learn is a free software for machine learning, offering simple and efficient tools for predictive data analysis, and is one of the best machine learning libraries available, with installation using pip install.', 'duration': 59.468, 'max_score': 38.14, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU38140.jpg'}, {'end': 202.615, 'src': 'embed', 'start': 175.693, 'weight': 3, 'content': [{'end': 187.284, 'text': 'yep. you go on script, you copy the URL or, like you copy the file location, you go here,', 'start': 175.693, 'duration': 11.591}, {'end': 193.188, 'text': 'you type in cd and then you paste in that location and then you run the same command again pip,', 'start': 187.284, 'duration': 5.904}, {'end': 197.331, 'text': 'install sklearn and that will install it for your version of Python.', 'start': 193.188, 'duration': 4.143}, {'end': 202.615, 'text': 'And here it says all requirement is already up to date because we just installed it previously.', 'start': 197.792, 'duration': 4.823}], 'summary': 'Install sklearn in python using pip command.', 'duration': 26.922, 'max_score': 175.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU175693.jpg'}], 'start': 0.809, 'title': 'Scikit-learn installation', 'summary': 'Introduces scikit-learn, a popular python machine learning library, and provides instructions for its installation using pip, making it an efficient tool for predictive data analysis across multiple python versions.', 'chapters': [{'end': 141.369, 'start': 0.809, 'title': 'Introduction to scikit-learn for machine learning', 'summary': 'Introduces scikit-learn as a popular machine learning library for python, highlighting its significance and demonstrating the installation process using pip, making it an efficient tool for predictive data analysis.', 'duration': 140.56, 'highlights': ['scikit-learn is a free software for machine learning, providing a bunch of libraries programmed for Python, making it one of the best machine learning libraries out there.', 'The scikit-learn website describes it as a simple and efficient tool for predictive data analysis, emphasizing its significance in the field of machine learning.', 'The installation process of scikit-learn is demonstrated using pip install, showcasing its ease of use and accessibility for predictive data analysis.']}, {'end': 202.615, 'start': 141.369, 'title': 'Installing sklearn for multiple python versions', 'summary': 'Explains how to install the sklearn library for specific python versions by navigating to the script directory and using the pip install command, ensuring the library is installed for the desired python version.', 'duration': 61.246, 'highlights': ['Navigating to the script directory and using the pip install command ensures the sklearn library is installed for the desired Python version.', "Using the 'pip install sklearn' command installs the sklearn library for the specific Python version.", "The command 'pip install sklearn' confirms that the library is already up to date after a previous installation."]}], 'duration': 201.806, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU809.jpg', 'highlights': ['scikit-learn is a free software for machine learning, providing a bunch of libraries programmed for Python, making it one of the best machine learning libraries out there.', 'The scikit-learn website describes it as a simple and efficient tool for predictive data analysis, emphasizing its significance in the field of machine learning.', 'The installation process of scikit-learn is demonstrated using pip install, showcasing its ease of use and accessibility for predictive data analysis.', 'Navigating to the script directory and using the pip install command ensures the sklearn library is installed for the desired Python version.', "Using the 'pip install sklearn' command installs the sklearn library for the specific Python version.", "The command 'pip install sklearn' confirms that the library is already up to date after a previous installation."]}, {'end': 800.179, 'segs': [{'end': 306.865, 'src': 'embed', 'start': 271.4, 'weight': 0, 'content': [{'end': 274.721, 'text': 'All right, so it told us that the requirements are already satisfied.', 'start': 271.4, 'duration': 3.321}, {'end': 277.763, 'text': 'So the library is already installed.', 'start': 274.801, 'duration': 2.962}, {'end': 286.146, 'text': "So let's say import matplotlib.pyplot as plt.", 'start': 277.783, 'duration': 8.363}, {'end': 288.767, 'text': "All right, so let's just run this.", 'start': 286.166, 'duration': 2.601}, {'end': 291.649, 'text': 'All right, so it worked.', 'start': 290.828, 'duration': 0.821}, {'end': 295.753, 'text': "Let's create, for example, x variables.", 'start': 292.31, 'duration': 3.443}, {'end': 305.503, 'text': "So let's create a list and then say i for i in range of 10.", 'start': 296.214, 'duration': 9.289}, {'end': 306.865, 'text': "And then let's run this.", 'start': 305.503, 'duration': 1.362}], 'summary': 'Successfully imported matplotlib library and created a list of 10 variables.', 'duration': 35.465, 'max_score': 271.4, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU271400.jpg'}, {'end': 399.138, 'src': 'embed', 'start': 367.096, 'weight': 1, 'content': [{'end': 374.177, 'text': 'And we can even say, for example, plt.xlabel, and then we can name our x labels.', 'start': 367.096, 'duration': 7.081}, {'end': 376.458, 'text': 'We can call it x-axis.', 'start': 374.897, 'duration': 1.561}, {'end': 386.36, 'text': 'And then do the same thing for the y label, so plt.ylabel, and then here we say y-axis.', 'start': 379.178, 'duration': 7.182}, {'end': 399.138, 'text': 'Now if we copy this and paste it right before here and run this again, now we have an axis and we also have our regression line or our curve.', 'start': 387.571, 'duration': 11.567}], 'summary': 'Using plt.xlabel and plt.ylabel to label x and y axes, resulting in a visible axis and regression line.', 'duration': 32.042, 'max_score': 367.096, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU367096.jpg'}, {'end': 489.61, 'src': 'embed', 'start': 427.896, 'weight': 3, 'content': [{'end': 434.661, 'text': "As you can see now we have a data point all the data points instead of having a line, because of course, it's not always a straight line,", 'start': 427.896, 'duration': 6.765}, {'end': 441.366, 'text': 'and maybe you want to take a look at, for example, the training data points or the testing data points,', 'start': 434.661, 'duration': 6.705}, {'end': 447.411, 'text': 'and it could be useful to check it in the form of data points.', 'start': 441.366, 'duration': 6.045}, {'end': 451.76, 'text': "All right, so that's it for plotting data points.", 'start': 448.217, 'duration': 3.543}, {'end': 458.425, 'text': "So let's get started.", 'start': 457.384, 'duration': 1.041}, {'end': 466.11, 'text': "All right, so let's say we have this data set, which is a table of four by seven.", 'start': 458.545, 'duration': 7.565}, {'end': 469.553, 'text': "All right, so let's consider it like, let's say a matrix.", 'start': 466.591, 'duration': 2.962}, {'end': 474.285, 'text': 'And we have features, which are the columns.', 'start': 470.544, 'duration': 3.741}, {'end': 475.426, 'text': 'This is a feature one.', 'start': 474.405, 'duration': 1.021}, {'end': 479.307, 'text': 'This is feature two, three, four, five, and six.', 'start': 476.006, 'duration': 3.301}, {'end': 481.848, 'text': 'And the last one would be a label.', 'start': 479.847, 'duration': 2.001}, {'end': 489.61, 'text': 'The features are independent variables, which means that one feature does not affect another one.', 'start': 482.628, 'duration': 6.982}], 'summary': 'Data points plotted in a table of 4x7 with independent variables and a label.', 'duration': 61.714, 'max_score': 427.896, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU427896.jpg'}, {'end': 629.421, 'src': 'embed', 'start': 588.459, 'weight': 4, 'content': [{'end': 596.163, 'text': 'Here we have a core evaluation dataset, and we can see that the number of instances is 1, 728.', 'start': 588.459, 'duration': 7.704}, {'end': 603.587, 'text': 'The number of attributes or the features are six.', 'start': 596.163, 'duration': 7.424}, {'end': 613.032, 'text': 'And this is the preferable model that we should be using is classification.', 'start': 605.388, 'duration': 7.644}, {'end': 615.711, 'text': "So they're helping us a little bit.", 'start': 613.809, 'duration': 1.902}, {'end': 619.734, 'text': "So let's scroll down here and we can see the attributes.", 'start': 615.831, 'duration': 3.903}, {'end': 629.421, 'text': 'For example, we have buying, maintenance, doors, persons, log boot and safety.', 'start': 619.854, 'duration': 9.567}], 'summary': 'Core evaluation dataset with 1,728 instances, 6 attributes, and a classification model preference.', 'duration': 40.962, 'max_score': 588.459, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU588459.jpg'}, {'end': 737.442, 'src': 'embed', 'start': 707.584, 'weight': 6, 'content': [{'end': 711.948, 'text': "Let's say we have created a model and we just want to save it,", 'start': 707.584, 'duration': 4.364}, {'end': 719.154, 'text': "because it takes a lot of time for the model to train and we don't want to train it over and over again every time we run the code.", 'start': 711.948, 'duration': 7.206}, {'end': 730.604, 'text': 'So to do that, all we have to do is to type in from sklearn.externals import joblib.', 'start': 719.734, 'duration': 10.87}, {'end': 731.664, 'text': 'All right.', 'start': 730.624, 'duration': 1.04}, {'end': 737.442, 'text': 'And then here, right after, We train the model.', 'start': 732.505, 'duration': 4.937}], 'summary': 'To save time, import joblib from sklearn.externals to save a trained model.', 'duration': 29.858, 'max_score': 707.584, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU707584.jpg'}], 'start': 203.415, 'title': 'Plotting data and data set features', 'summary': 'Covers plotting data with matplotlib, including installation, creation, and visualization, along with labeling axes. it also discusses data set features and labels, emphasizing the role of features and labels in machine learning models, and provides an example of supervised classification. additionally, it explores the process of saving a trained model using joblib.', 'chapters': [{'end': 458.425, 'start': 203.415, 'title': 'Plotting data with matplotlib', 'summary': 'Demonstrates the process of plotting data using matplotlib, including installing the library, creating and visualizing data points on a graph, and labeling the axes, to provide a better understanding of the data and its representation.', 'duration': 255.01, 'highlights': ["The chapter demonstrates the process of installing Matplotlib using 'pip install matplotlib'. Provides a step-by-step guide on installing the Matplotlib library using the 'pip' package manager.", 'It illustrates the creation of x and y variables to visualize data points on a graph. Shows the process of creating x and y variables, using a linear function, and visualizing the data points on a graph using Matplotlib.', 'The tutorial explains how to label the x and y axes for better visualization and understanding of the data. Demonstrates the process of labeling the x and y axes to provide a clear representation of the data points on the graph.', 'It showcases the method of visualizing data points as scatter plots instead of a regression line. Illustrates the option of visualizing data points as scatter plots, providing a more flexible representation of the data.']}, {'end': 800.179, 'start': 458.545, 'title': 'Data set features and labels', 'summary': 'Discusses the concept of features and labels in a data set, emphasizing the distinction between independent variables (features) and dependent variables (labels), with a focus on their role in machine learning models and an example of supervised classification, as well as the process for saving a trained model using joblib.', 'duration': 341.634, 'highlights': ['The number of instances in the data set is four, with six attributes or features, and the model type preferred for this data set is classification, as shown in the example from the UCI machine learning repository.', 'The distinction between features and labels is outlined, with features acting as independent variables and labels as dependent variables, with specific attributes representing the features and class values representing the labels in the data set.', 'The process for saving a trained model using joblib is explained, with the code snippet and steps provided for creating and saving the model for future use.']}], 'duration': 596.764, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU203415.jpg', 'highlights': ["Provides a step-by-step guide on installing the Matplotlib library using the 'pip' package manager.", 'Shows the process of creating x and y variables, using a linear function, and visualizing the data points on a graph using Matplotlib.', 'Demonstrates the process of labeling the x and y axes to provide a clear representation of the data points on the graph.', 'Illustrates the option of visualizing data points as scatter plots, providing a more flexible representation of the data.', 'The number of instances in the data set is four, with six attributes or features, and the model type preferred for this data set is classification, as shown in the example from the UCI machine learning repository.', 'The distinction between features and labels is outlined, with features acting as independent variables and labels as dependent variables, with specific attributes representing the features and class values representing the labels in the data set.', 'The process for saving a trained model using joblib is explained, with the code snippet and steps provided for creating and saving the model for future use.']}, {'end': 1760.195, 'segs': [{'end': 880.408, 'src': 'embed', 'start': 843.916, 'weight': 4, 'content': [{'end': 849.722, 'text': 'and classification is a way where it finds similar.', 'start': 843.916, 'duration': 5.806}, {'end': 858.75, 'text': 'it finds a feature that have somewhat of a similarity, that are associated with a label,', 'start': 852.665, 'duration': 6.085}, {'end': 863.354, 'text': 'and any other feature that will resemble this feature will be classified with the same label.', 'start': 858.75, 'duration': 4.604}, {'end': 869.639, 'text': "So, in case you didn't understand, let's take an example where we have two features.", 'start': 864.355, 'duration': 5.284}, {'end': 869.94, 'text': 'all right?', 'start': 869.639, 'duration': 0.301}, {'end': 877.466, 'text': 'Two features and three labels.', 'start': 871.601, 'duration': 5.865}, {'end': 877.766, 'text': 'all right?', 'start': 877.466, 'duration': 0.3}, {'end': 880.408, 'text': 'Three labels.', 'start': 879.628, 'duration': 0.78}], 'summary': 'Classification finds similar features and assigns them to labels based on similarity.', 'duration': 36.492, 'max_score': 843.916, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU843916.jpg'}, {'end': 987.563, 'src': 'embed', 'start': 956.84, 'weight': 7, 'content': [{'end': 964.485, 'text': 'since we have, since we have three labels, we will have some blue here around this point like this, maybe points like this', 'start': 956.84, 'duration': 7.645}, {'end': 965.065, 'text': 'All right.', 'start': 964.505, 'duration': 0.56}, {'end': 973.378, 'text': 'And what classification does is depending on the algorithm.', 'start': 968.837, 'duration': 4.541}, {'end': 979.28, 'text': 'it will find a way to separate these labels, all right?', 'start': 973.378, 'duration': 5.902}, {'end': 986.182, 'text': 'So, for example, we might have an algorithm that might separate them somewhat like this and like this.', 'start': 979.36, 'duration': 6.822}, {'end': 987.563, 'text': 'This might be one algorithm.', 'start': 986.282, 'duration': 1.281}], 'summary': 'Three labels, algorithm separates them with blue points.', 'duration': 30.723, 'max_score': 956.84, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU956840.jpg'}, {'end': 1036.239, 'src': 'embed', 'start': 1013.309, 'weight': 1, 'content': [{'end': 1022.452, 'text': 'the model will then know that this point right here will be red, so it will give it the right label.', 'start': 1013.309, 'duration': 9.143}, {'end': 1027.736, 'text': "so That's about pretty much how classification work.", 'start': 1022.452, 'duration': 5.284}, {'end': 1030.777, 'text': 'We will learn about K and N classification.', 'start': 1027.776, 'duration': 3.001}, {'end': 1036.239, 'text': "So K nearest neighbor in our next video and we'll be doing some examples.", 'start': 1031.278, 'duration': 4.961}], 'summary': 'Model learns to classify points, knn to be covered in next video.', 'duration': 22.93, 'max_score': 1013.309, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1013309.jpg'}, {'end': 1284.785, 'src': 'embed', 'start': 1250.756, 'weight': 5, 'content': [{'end': 1267.002, 'text': "So from 10 different students, what we want to do is to train a model with, let's say, so let's train a model with eight students.", 'start': 1250.756, 'duration': 16.246}, {'end': 1272.144, 'text': 'So eight students.', 'start': 1271.304, 'duration': 0.84}, {'end': 1276.44, 'text': 'And then we will predict with the remaining two.', 'start': 1272.597, 'duration': 3.843}, {'end': 1277.76, 'text': 'All right.', 'start': 1276.46, 'duration': 1.3}, {'end': 1284.785, 'text': 'And this will give us an accuracy or a level of accuracy of our model.', 'start': 1278.601, 'duration': 6.184}], 'summary': 'Train model with 8 students, predict with 2 for accuracy assessment.', 'duration': 34.029, 'max_score': 1250.756, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1250756.jpg'}, {'end': 1422.599, 'src': 'embed', 'start': 1371.872, 'weight': 3, 'content': [{'end': 1374.513, 'text': 'and this is equal to the train test split.', 'start': 1371.872, 'duration': 2.641}, {'end': 1384.959, 'text': 'We have to input our x, y and then the final variable is test size.', 'start': 1376.614, 'duration': 8.345}, {'end': 1385.259, 'text': 'all right?', 'start': 1384.959, 'duration': 0.3}, {'end': 1393.683, 'text': 'So test size if we make it 0.2, that means 20% of our data will be for testing purposes.', 'start': 1385.299, 'duration': 8.384}, {'end': 1408.555, 'text': "Now let's print xtrain.shape So we just want to look what's the dimensions of our x train array.", 'start': 1395.024, 'duration': 13.531}, {'end': 1412.736, 'text': "Let's print xtest.shape.", 'start': 1409.095, 'duration': 3.641}, {'end': 1422.599, 'text': "Let's print, and these return tuples, ytrain.shape.", 'start': 1415.117, 'duration': 7.482}], 'summary': 'Using train test split with 20% test size for data testing.', 'duration': 50.727, 'max_score': 1371.872, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1371872.jpg'}, {'end': 1505.127, 'src': 'embed', 'start': 1472.078, 'weight': 6, 'content': [{'end': 1475.859, 'text': 'So these two here, look at these two, the 30.', 'start': 1472.078, 'duration': 3.781}, {'end': 1480.301, 'text': 'These are for testing purposes and these are for training purposes.', 'start': 1475.859, 'duration': 4.442}, {'end': 1486.483, 'text': 'And in general, we always want the training data to be much larger than the testing data.', 'start': 1480.721, 'duration': 5.762}, {'end': 1497.627, 'text': "But let's also not make it too much large and keep a significant amount of testing data, because if our testing data is too low,", 'start': 1486.803, 'duration': 10.824}, {'end': 1505.127, 'text': "then the accuracy may be the accuracy won't be representative of our model.", 'start': 1497.627, 'duration': 7.5}], 'summary': 'Training data should be much larger than testing data to ensure representative accuracy.', 'duration': 33.049, 'max_score': 1472.078, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1472078.jpg'}, {'end': 1555.705, 'src': 'embed', 'start': 1523.331, 'weight': 0, 'content': [{'end': 1530.248, 'text': "So we're going to take a look at what the Eris dataset actually is and we're going to be doing classification with it.", 'start': 1523.331, 'duration': 6.917}, {'end': 1541.696, 'text': "Today's video we're going to learn about the KNN classifier.", 'start': 1530.268, 'duration': 11.428}, {'end': 1551.942, 'text': 'So KNN stands for K nearest neighbors and is a classifying but also a regression algorithm.', 'start': 1542.416, 'duration': 9.526}, {'end': 1555.705, 'text': "But in this video we're only going to take a look at the classification.", 'start': 1552.423, 'duration': 3.282}], 'summary': 'Introduction to eris dataset and knn classifier for classification.', 'duration': 32.374, 'max_score': 1523.331, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1523331.jpg'}, {'end': 1703.265, 'src': 'embed', 'start': 1666.123, 'weight': 2, 'content': [{'end': 1673.107, 'text': 'if we have a big data set, then we can use a high number for k.', 'start': 1666.123, 'duration': 6.984}, {'end': 1683.719, 'text': 'If, on the other hand, we have a fewer number of instances or a smaller data set, then we use a smaller number for k.', 'start': 1673.107, 'duration': 10.612}, {'end': 1691.407, 'text': 'It is also advised to use an odd number for k to facilitate the calculations.', 'start': 1683.719, 'duration': 7.688}, {'end': 1697.64, 'text': 'also we have a second parameter, which is the weights.', 'start': 1693.757, 'duration': 3.883}, {'end': 1703.265, 'text': 'the weights can either be uniform or distance.', 'start': 1697.64, 'duration': 5.625}], 'summary': 'For large datasets, use a high k value; for smaller ones, use a smaller k. odd k values are preferred, and weight options include uniform or distance.', 'duration': 37.142, 'max_score': 1666.123, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1666123.jpg'}], 'start': 800.86, 'title': 'Model loading, classification, and train test split', 'summary': 'Covers loading a machine learning model using joblib.load, classification using multiple algorithms to separate labels, splitting a dataset into training and testing sets, training a model with part of the data and testing with the remaining data, and introduces the knn classifier method for classification with an 80-20 train test split.', 'chapters': [{'end': 1036.239, 'start': 800.86, 'title': 'Model loading and classification', 'summary': 'Explains how to load a machine learning model using joblib.load, the concept of features and labels in classification, and the process of classification using multiple algorithms to separate labels in a 2d graph.', 'duration': 235.379, 'highlights': ['The process of classification involves finding similar features associated with a label, and any other feature resembling this feature will be classified with the same label, with an example of two features and three labels provided.', 'Explanation of how the number of features can be viewed as the number of dimensions, and how a 2D graph can be plotted based on the features, with each feature representing the X and Y coordinates.', 'Illustration of the classification process using different algorithms to separate labels in a 2D graph, demonstrating how the model identifies the correct label for a given point based on the separation of labels by the algorithm.']}, {'end': 1331.762, 'start': 1036.239, 'title': 'Splitting data for model training', 'summary': 'Discusses splitting a dataset into training and testing sets, using the example of 150 elements split into features and labels, and explains the concept of training a model with part of the data and testing with the remaining data to assess accuracy.', 'duration': 295.523, 'highlights': ['The chapter explains the process of splitting a dataset into features and labels, with the example of 150 elements split into X features and Y labels.', "It discusses the concept of training a model with part of the data and testing with the remaining data to assess accuracy, using the example of training a model with eight out of ten samples and predicting with the remaining two to determine the model's accuracy.", 'It emphasizes the importance of model accuracy by illustrating scenarios where accurate predictions reflect a good model and inaccurate predictions reflect a bad model.']}, {'end': 1760.195, 'start': 1333.082, 'title': 'Train test split and knn classifier', 'summary': 'Covers the train test split function in sklearn, splitting the data into training and testing sets with a 80-20 proportion, and introduces the knn classifier method for classification, including the concept, working principle, and parameter considerations.', 'duration': 427.113, 'highlights': ['The train test split function in sklearn splits the data into training and testing sets with a specified test size, e.g., 0.2 for 20% of the data. The train test split function in sklearn splits the data into training and testing sets with a specified test size, e.g., 0.2 for 20% of the data.', 'The dimensions of the training and testing data arrays are printed to observe the proportions, e.g., 120 for training and 30 for testing, maintaining a larger training set for better model representation. The dimensions of the training and testing data arrays are printed to observe the proportions, e.g., 120 for training and 30 for testing, maintaining a larger training set for better model representation.', 'Introduction to KNN classifier, explaining its role in classifying and regression, and its method of separating data points into regions based on features and proximity. Introduction to KNN classifier, explaining its role in classifying and regression, and its method of separating data points into regions based on features and proximity.', 'Explanation of the working principle of KNN, involving the calculation of distances to nearest points and the majority voting for labeling. Explanation of the working principle of KNN, involving the calculation of distances to nearest points and the majority voting for labeling.', 'Consideration of parameters for KNN, including the value of k based on the number of instances and the choice between uniform and distance weights, impacting the importance of data points in classification. Consideration of parameters for KNN, including the value of k based on the number of instances and the choice between uniform and distance weights, impacting the importance of data points in classification.']}], 'duration': 959.335, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU800860.jpg', 'highlights': ['Introduction to KNN classifier, explaining its role in classifying and regression, and its method of separating data points into regions based on features and proximity.', 'Explanation of the working principle of KNN, involving the calculation of distances to nearest points and the majority voting for labeling.', 'Consideration of parameters for KNN, including the value of k based on the number of instances and the choice between uniform and distance weights, impacting the importance of data points in classification.', 'The train test split function in sklearn splits the data into training and testing sets with a specified test size, e.g., 0.2 for 20% of the data.', 'The process of classification involves finding similar features associated with a label, and any other feature resembling this feature will be classified with the same label, with an example of two features and three labels provided.', "It discusses the concept of training a model with part of the data and testing with the remaining data to assess accuracy, using the example of training a model with eight out of ten samples and predicting with the remaining two to determine the model's accuracy.", 'The dimensions of the training and testing data arrays are printed to observe the proportions, e.g., 120 for training and 30 for testing, maintaining a larger training set for better model representation.', 'Illustration of the classification process using different algorithms to separate labels in a 2D graph, demonstrating how the model identifies the correct label for a given point based on the separation of labels by the algorithm.']}, {'end': 3171.598, 'segs': [{'end': 2198.777, 'src': 'heatmap', 'start': 1760.395, 'weight': 0, 'content': [{'end': 1775.367, 'text': 'All right, now that everything finished up, we can start by importing our libraries that we need for this project.', 'start': 1760.395, 'duration': 14.972}, {'end': 1778.21, 'text': "So let's start by importing NungPy as NP.", 'start': 1775.427, 'duration': 2.783}, {'end': 1780.892, 'text': "Let's also import pandas.", 'start': 1779.491, 'duration': 1.401}, {'end': 1788.466, 'text': 'as pd, also from sklearn.', 'start': 1783.044, 'duration': 5.422}, {'end': 1803.812, 'text': "let's import neighbors, neighbors and metrics from sklearn dot model selection.", 'start': 1788.466, 'duration': 15.346}, {'end': 1815.053, 'text': "let's import train test splits from sklearn.preprocessing.", 'start': 1803.812, 'duration': 11.241}, {'end': 1817.174, 'text': "let's import label and code.", 'start': 1815.053, 'duration': 2.121}, {'end': 1819.556, 'text': 'There we go.', 'start': 1819.055, 'duration': 0.501}, {'end': 1825.239, 'text': 'Now we have all the libraries, all the code that we need for this project.', 'start': 1820.856, 'duration': 4.383}, {'end': 1836.847, 'text': "Now let's go on this website, UCI machine learning repository, and let's go on the car evaluation dataset.", 'start': 1826.4, 'duration': 10.447}, {'end': 1839.889, 'text': "So that's what we're going to be working with for today.", 'start': 1837.667, 'duration': 2.222}, {'end': 1846.077, 'text': "this is the example that we've seen previously in our videos.", 'start': 1840.353, 'duration': 5.724}, {'end': 1851.16, 'text': "to download, let's just click on the data folder and then download core.data.", 'start': 1846.077, 'duration': 5.083}, {'end': 1858.005, 'text': "okay, let's copy the file and paste it under knn classifier.", 'start': 1851.16, 'duration': 6.845}, {'end': 1862.948, 'text': "let's say okay, and now we have the core.data.", 'start': 1858.005, 'duration': 4.943}, {'end': 1865.401, 'text': "now Let's import this data.", 'start': 1862.948, 'duration': 2.453}, {'end': 1870.324, 'text': 'So say data equals pd.readcsv.', 'start': 1865.481, 'duration': 4.843}, {'end': 1874.207, 'text': "And here it's going to be car.data.", 'start': 1871.005, 'duration': 3.202}, {'end': 1880.111, 'text': 'Now we can look at this data by saying print data.head.', 'start': 1875.248, 'duration': 4.863}, {'end': 1882.632, 'text': "And let's take a look.", 'start': 1881.692, 'duration': 0.94}, {'end': 1884.434, 'text': 'All right.', 'start': 1884.113, 'duration': 0.321}, {'end': 1886.795, 'text': 'So this is our data.', 'start': 1884.514, 'duration': 2.281}, {'end': 1890.958, 'text': 'So we have a bunch of features here.', 'start': 1886.895, 'duration': 4.063}, {'end': 1896.116, 'text': "Okay, so now let's add the title since they are missing.", 'start': 1892.034, 'duration': 4.082}, {'end': 1901.418, 'text': 'So they are here buying, buying comma.', 'start': 1896.596, 'duration': 4.822}, {'end': 1906.921, 'text': 'We have maintenance M A I N T.', 'start': 1902.199, 'duration': 4.722}, {'end': 1916.885, 'text': 'We have the doors, we have the persons, and then we have the luggage boots, lug boots.', 'start': 1906.921, 'duration': 9.964}, {'end': 1920.247, 'text': 'We have the safety.', 'start': 1918.886, 'duration': 1.361}, {'end': 1923.929, 'text': 'And finally, we have the class.', 'start': 1922.168, 'duration': 1.761}, {'end': 1929.952, 'text': "Let's just call it class.", 'start': 1923.949, 'duration': 6.003}, {'end': 1931.133, 'text': 'All right.', 'start': 1929.972, 'duration': 1.161}, {'end': 1935.935, 'text': 'So now what we can do is run this program again.', 'start': 1932.273, 'duration': 3.662}, {'end': 1945.22, 'text': 'And at the top, we can see we have.', 'start': 1935.955, 'duration': 9.265}, {'end': 1948.742, 'text': 'Now we can look at the top and we have attributes for all of these.', 'start': 1945.22, 'duration': 3.522}, {'end': 1952.509, 'text': 'All right, so this works perfectly.', 'start': 1951.089, 'duration': 1.42}, {'end': 1958.131, 'text': "Now let's create labels and features.", 'start': 1954.41, 'duration': 3.721}, {'end': 1968.334, 'text': "So X is for labels, and we're just gonna simply take the buying, the maintenance, and the safety.", 'start': 1958.371, 'duration': 9.963}, {'end': 1977.056, 'text': "So these are the only attributes that we're gonna be using as labels into our model,", 'start': 1968.854, 'duration': 8.202}, {'end': 1985.796, 'text': 'because I believe these are the ones are the most important and tells us the most about the condition of the car.', 'start': 1977.056, 'duration': 8.74}, {'end': 2009.205, 'text': "so x will be equal to data and then here we'll select buying um, maintenance and safety, all right, and then for the y, this is um, uh, the label,", 'start': 1985.796, 'duration': 23.409}, {'end': 2032.329, 'text': "the label is the class label is the class like this, and then, yeah, let's just print X and Y just to see if everything works properly.", 'start': 2010.431, 'duration': 21.898}, {'end': 2034.411, 'text': 'everything seems to be working just fine.', 'start': 2032.329, 'duration': 2.082}, {'end': 2038.39, 'text': "Now we're running into a problem.", 'start': 2036.048, 'duration': 2.342}, {'end': 2039.971, 'text': 'We have strings.', 'start': 2038.57, 'duration': 1.401}, {'end': 2041.272, 'text': 'We have names.', 'start': 2040.251, 'duration': 1.021}, {'end': 2044.314, 'text': 'Very high, very high, low.', 'start': 2041.352, 'duration': 2.962}, {'end': 2049.858, 'text': 'And we cannot fetch these into a machine learning algorithm.', 'start': 2045.054, 'duration': 4.804}, {'end': 2051.719, 'text': 'So we have to convert these into numbers.', 'start': 2049.878, 'duration': 1.841}, {'end': 2053.159, 'text': "So let's do this.", 'start': 2052.478, 'duration': 0.681}, {'end': 2056.562, 'text': 'So this is convert conversion.', 'start': 2054.221, 'duration': 2.341}, {'end': 2061.165, 'text': "We're converting the data.", 'start': 2059.684, 'duration': 1.481}, {'end': 2066.844, 'text': "And we're going to be using a label encoder.", 'start': 2063.187, 'duration': 3.657}, {'end': 2071.71, 'text': "so let's call it le equal label encoder.", 'start': 2066.844, 'duration': 4.866}, {'end': 2081.918, 'text': "and then we're going to be saying for i in range, and then here we want to take the length of x 0.", 'start': 2071.71, 'duration': 10.208}, {'end': 2089.579, 'text': 'so x 0 is simply three.', 'start': 2081.918, 'duration': 7.661}, {'end': 2090.48, 'text': "so it's this number.", 'start': 2089.579, 'duration': 0.901}, {'end': 2092.821, 'text': "it's, it's the rows, times the columns.", 'start': 2090.48, 'duration': 2.341}, {'end': 2094.262, 'text': 'so this just gives us the columns.', 'start': 2092.821, 'duration': 1.441}, {'end': 2099.205, 'text': 'you could do it a different way, but this is a simply a simple way.', 'start': 2094.262, 'duration': 4.943}, {'end': 2105.209, 'text': "we're gonna be saying X for the rows, we're gonna take all the rows,", 'start': 2099.205, 'duration': 6.004}, {'end': 2111.413, 'text': 'and for the columns we want to loop by I and we will convert them each one by one.', 'start': 2105.209, 'duration': 6.204}, {'end': 2115.215, 'text': 'so we will say le dot fit, transform.', 'start': 2111.413, 'duration': 3.802}, {'end': 2121.226, 'text': "We're going to be passing in x and the same thing.", 'start': 2117.844, 'duration': 3.382}, {'end': 2128.23, 'text': "Here it's going to be a column and an i.", 'start': 2121.266, 'duration': 6.964}, {'end': 2133.753, 'text': 'Now we can print x and see if we converted everything properly.', 'start': 2128.23, 'duration': 5.523}, {'end': 2135.354, 'text': "So let's print x.", 'start': 2134.554, 'duration': 0.8}, {'end': 2140.818, 'text': 'All right.', 'start': 2135.354, 'duration': 5.464}, {'end': 2143.679, 'text': 'So to fix the error here, we have to say dot values.', 'start': 2140.858, 'duration': 2.821}, {'end': 2145.4, 'text': 'And this should fix the error.', 'start': 2144.28, 'duration': 1.12}, {'end': 2146.541, 'text': 'All right.', 'start': 2145.42, 'duration': 1.121}, {'end': 2159.193, 'text': 'Alright. so now, as you can see, the label and quarter works and if we take a look here, very high values are converted to a 3.', 'start': 2149.505, 'duration': 9.688}, {'end': 2166.64, 'text': 'so we already know that and we can see that here low values are converted to a 1.', 'start': 2159.193, 'duration': 7.447}, {'end': 2170.043, 'text': 'so we can already tell that everything seems to be working properly.', 'start': 2166.64, 'duration': 3.403}, {'end': 2179.306, 'text': 'Now For the Y, we will do a different types of conversion.', 'start': 2172.085, 'duration': 7.221}, {'end': 2182.107, 'text': 'So this is one type of conversion using a label encoder.', 'start': 2179.346, 'duration': 2.761}, {'end': 2183.628, 'text': "Now I'll show you a different type.", 'start': 2182.127, 'duration': 1.501}, {'end': 2186.69, 'text': 'This is going to be using.', 'start': 2185.589, 'duration': 1.101}, {'end': 2188.031, 'text': "We're going to be using mapping.", 'start': 2186.69, 'duration': 1.341}, {'end': 2190.753, 'text': 'So conversion again for the Y.', 'start': 2188.771, 'duration': 1.982}, {'end': 2193.474, 'text': "Here we're converting the X.", 'start': 2190.753, 'duration': 2.721}, {'end': 2194.355, 'text': "Let's write it down here.", 'start': 2193.474, 'duration': 0.881}, {'end': 2197.417, 'text': "And then we're going to be.", 'start': 2194.375, 'duration': 3.042}, {'end': 2198.777, 'text': "Let's create a dictionary.", 'start': 2197.417, 'duration': 1.36}], 'summary': 'Imported libraries, imported and processed car evaluation dataset, converted string data into numerical format.', 'duration': 130.563, 'max_score': 1760.395, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1760395.jpg'}, {'end': 1977.056, 'src': 'embed', 'start': 1945.22, 'weight': 3, 'content': [{'end': 1948.742, 'text': 'Now we can look at the top and we have attributes for all of these.', 'start': 1945.22, 'duration': 3.522}, {'end': 1952.509, 'text': 'All right, so this works perfectly.', 'start': 1951.089, 'duration': 1.42}, {'end': 1958.131, 'text': "Now let's create labels and features.", 'start': 1954.41, 'duration': 3.721}, {'end': 1968.334, 'text': "So X is for labels, and we're just gonna simply take the buying, the maintenance, and the safety.", 'start': 1958.371, 'duration': 9.963}, {'end': 1977.056, 'text': "So these are the only attributes that we're gonna be using as labels into our model,", 'start': 1968.854, 'duration': 8.202}], 'summary': 'Creating labels and features from attributes for model training.', 'duration': 31.836, 'max_score': 1945.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1945220.jpg'}, {'end': 2550.984, 'src': 'embed', 'start': 2506.204, 'weight': 4, 'content': [{'end': 2522.512, 'text': "let's remove these unnecessary printings and then let's run the code.", 'start': 2506.204, 'duration': 16.308}, {'end': 2531.275, 'text': 'so, as you can see, the accuracy is 0.75 and the predictions seems to be complete.', 'start': 2522.512, 'duration': 8.763}, {'end': 2538.778, 'text': 'So now we have successfully created our first machine learning model.', 'start': 2533.456, 'duration': 5.322}, {'end': 2543.14, 'text': "Now let's test ourself our model.", 'start': 2539.759, 'duration': 3.381}, {'end': 2550.984, 'text': "So let's say we create, let's print actual value.", 'start': 2544.181, 'duration': 6.803}], 'summary': 'First machine learning model achieved 75% accuracy.', 'duration': 44.78, 'max_score': 2506.204, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU2506204.jpg'}, {'end': 2680.249, 'src': 'embed', 'start': 2605.213, 'weight': 5, 'content': [{'end': 2610.835, 'text': "Let's say 100, and let's replace these by our dummy variable a.", 'start': 2605.213, 'duration': 5.622}, {'end': 2613.448, 'text': "and let's run it again.", 'start': 2612.628, 'duration': 0.82}, {'end': 2617.61, 'text': 'We have an actual value of zero and a predicted value of zero.', 'start': 2613.949, 'duration': 3.661}, {'end': 2621.531, 'text': 'And as you can see, our model is pretty accurate.', 'start': 2618.45, 'duration': 3.081}, {'end': 2626.173, 'text': 'As you can see here, the actual value is three.', 'start': 2622.172, 'duration': 4.001}, {'end': 2628.354, 'text': 'The predicted value is also three.', 'start': 2626.693, 'duration': 1.661}, {'end': 2630.795, 'text': 'So our model seems to be working pretty well.', 'start': 2628.654, 'duration': 2.141}, {'end': 2641.919, 'text': 'SVM stands for Support Vector Machine.', 'start': 2638.158, 'duration': 3.761}, {'end': 2651.287, 'text': 'And it is very effective in high dimensional spaces, meaning that we have many features.', 'start': 2642.942, 'duration': 8.345}, {'end': 2659.091, 'text': 'Also, we have many kernel functions, which we will explain in this video and support.', 'start': 2652.608, 'duration': 6.483}, {'end': 2664.134, 'text': 'vector machine can be used for classification, but also for regression.', 'start': 2659.091, 'duration': 5.043}, {'end': 2665.315, 'text': 'All right.', 'start': 2665.035, 'duration': 0.28}, {'end': 2668.457, 'text': "So now let's talk about how SVM works.", 'start': 2665.335, 'duration': 3.122}, {'end': 2678.088, 'text': "All right, now let's imagine we have a 2D plane and let's call this X and this Y.", 'start': 2669.284, 'duration': 8.804}, {'end': 2680.249, 'text': 'All right.', 'start': 2678.088, 'duration': 2.161}], 'summary': 'Support vector machine model is accurate, effective in high dimensional spaces, and can be used for classification and regression.', 'duration': 75.036, 'max_score': 2605.213, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU2605213.jpg'}, {'end': 3014.358, 'src': 'embed', 'start': 2975.371, 'weight': 7, 'content': [{'end': 2992.014, 'text': 'Let me just draw this like this, like this, in a 3D space that would separate both our datasets.', 'start': 2975.371, 'duration': 16.643}, {'end': 2994.935, 'text': 'And here, this will be optimized.', 'start': 2992.434, 'duration': 2.501}, {'end': 2999.775, 'text': 'All right, so this is what support vector machine is.', 'start': 2995.894, 'duration': 3.881}, {'end': 3003.095, 'text': 'We could use it for classification and regression.', 'start': 3000.115, 'duration': 2.98}, {'end': 3008.076, 'text': 'And if we go back here, all right, so these are the kernel functions.', 'start': 3003.636, 'duration': 4.44}, {'end': 3014.358, 'text': 'We could use a linear function, polynomial function, RBF, which as I said is exponential.', 'start': 3008.096, 'duration': 6.262}], 'summary': 'Using support vector machines for 3d dataset separation and classification/regression, with options for linear, polynomial, and rbf kernel functions.', 'duration': 38.987, 'max_score': 2975.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU2975371.jpg'}, {'end': 3126.164, 'src': 'embed', 'start': 3091.137, 'weight': 8, 'content': [{'end': 3104.367, 'text': "But before we get started, make sure that you watch the explanation on SVM support vector machine tutorials to understand what we're about to do.", 'start': 3091.137, 'duration': 13.23}, {'end': 3119.14, 'text': "Now, before we get started, let's actually take a look at the Aris data set, and we have it here and, as you can see, it is used for classification.", 'start': 3104.988, 'duration': 14.152}, {'end': 3119.801, 'text': 'it has.', 'start': 3119.14, 'duration': 0.661}, {'end': 3123.322, 'text': 'so the number of attributes or features.', 'start': 3119.801, 'duration': 3.521}, {'end': 3125.003, 'text': 'there are four.', 'start': 3123.322, 'duration': 1.681}, {'end': 3126.164, 'text': 'number of instances.', 'start': 3125.003, 'duration': 1.161}], 'summary': 'The aris dataset for svm classification has 4 attributes and an unspecified number of instances.', 'duration': 35.027, 'max_score': 3091.137, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU3091137.jpg'}], 'start': 1760.395, 'title': 'Car evaluation and machine learning models', 'summary': 'Covers importing libraries and loading car evaluation dataset, adding titles to car attributes, creating knn machine learning model with an accuracy of 0.75, explaining support vector machines (svms) basics, and understanding svm kernel functions and their application in a concrete example of classification using the aris dataset with 150 instances and 4 attributes.', 'chapters': [{'end': 1890.958, 'start': 1760.395, 'title': 'Importing libraries and loading car evaluation dataset', 'summary': 'Covers the process of importing necessary libraries and loading the car evaluation dataset from the uci machine learning repository, including importing numpy as np, importing pandas, and reading the car data file, culminating in the display of the first few rows of the dataset.', 'duration': 130.563, 'highlights': ['Imported necessary libraries including Numpy and Pandas, and defined aliases for them as NP and pd respectively.', 'Downloaded the car evaluation dataset from the UCI machine learning repository and saved it as car.data.', "Read the car.data file using Pandas and displayed the first few rows of the dataset using 'print data.head'."]}, {'end': 2244.456, 'start': 1892.034, 'title': 'Car condition classification', 'summary': 'Involves adding titles to car attributes, including buying, maintenance, safety, and class, and then creating labels and features for a machine learning model, which involves converting string values to numerical using label encoder and mapping.', 'duration': 352.422, 'highlights': ['Creating Labels and Features The chapter involves adding titles to car attributes, including buying, maintenance, safety, and class, and then creating labels and features for a machine learning model, which involves converting string values to numerical using label encoder and mapping.', 'Label Encoding for X X is defined as the labels for the machine learning model, including buying, maintenance, and safety, which are converted from string to numerical values using a label encoder.', "Conversion of Class Labels The class labels 'unacceptable,' 'acceptable,' 'good,' and 'very good' are mapped to numerical values 0, 1, 2, and 3 respectively to prepare them for machine learning model training."]}, {'end': 2604.852, 'start': 2244.456, 'title': 'Creating knn machine learning model', 'summary': 'Discusses the process of creating a knn machine learning model, including data conversion, model creation with 25 neighbors and uniform weight, training the model using training and testing data, making predictions, and achieving an accuracy of 0.75.', 'duration': 360.396, 'highlights': ['The accuracy of the model is 0.75, indicating its performance in making predictions. The accuracy of the model is 0.75, demonstrating its effectiveness in making predictions.', 'The model is created using 25 neighbors and uniform weight, following the KNN algorithm. The model is created using 25 neighbors and uniform weight, adhering to the KNN algorithm.', 'The process involves converting the data to proper form, creating a KNN object, training the model using training and testing data, and making predictions based on the trained model. The process involves converting the data to proper form, creating a KNN object, training the model using training and testing data, and making predictions based on the trained model.']}, {'end': 2852.076, 'start': 2605.213, 'title': 'Support vector machine basics', 'summary': 'Explains the basics of support vector machines (svms), including their accuracy in prediction and effectiveness in high dimensional spaces, as well as their ability for classification and regression. it also delves into the concept of creating a hyperplane and optimizing the margin for best representation.', 'duration': 246.863, 'highlights': ["SVM's accuracy in prediction The model accurately predicts actual values, as demonstrated by zero actual and predicted values matching, and three actual and predicted values aligning, showcasing its accuracy.", 'Effectiveness of SVM in high dimensional spaces SVM is described as very effective in high dimensional spaces, particularly in scenarios with many features, emphasizing its practicality in handling complex data.', "SVM's ability for classification and regression SVM is highlighted as capable of both classification and regression tasks, showcasing its versatility in handling different types of machine learning problems.", 'Explanation of creating a hyperplane in SVM The process of creating a hyperplane in SVM to separate data points of different classes is explained, demonstrating how SVM draws a line to optimize the margin for best representation.']}, {'end': 3171.598, 'start': 2852.964, 'title': 'Understanding support vector machine', 'summary': 'Explains the concept of support vector machine (svm) and its kernel functions, including linear, polynomial, rbf, and sigmoid functions, used for classification and regression. it also discusses the application of svm in a concrete example of classification using the aris dataset with 150 instances and 4 attributes.', 'duration': 318.634, 'highlights': ['Support vector machine (SVM) can use different kernel functions like linear, polynomial, RBF, and sigmoid for classification and regression. SVM can utilize various kernel functions such as linear, polynomial, RBF, and sigmoid for classification and regression tasks.', 'The Aris dataset for classification contains 150 instances and 4 attributes, including sepal length, sepal width, petal length, and petal width. The Aris dataset used for classification consists of 150 instances and 4 attributes, namely sepal length, sepal width, petal length, and petal width.', 'Explanation of how SVM creates a third dimension to separate overlapping data points using a hyperplane. Illustration of how SVM creates a third dimension to separate overlapping data points using a hyperplane.']}], 'duration': 1411.203, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU1760395.jpg', 'highlights': ['Imported necessary libraries including Numpy and Pandas, and defined aliases for them as NP and pd respectively.', 'Downloaded the car evaluation dataset from the UCI machine learning repository and saved it as car.data.', "Read the car.data file using Pandas and displayed the first few rows of the dataset using 'print data.head'.", 'Creating Labels and Features The chapter involves adding titles to car attributes, including buying, maintenance, safety, and class, and then creating labels and features for a machine learning model, which involves converting string values to numerical using label encoder and mapping.', 'The accuracy of the model is 0.75, indicating its performance in making predictions. The accuracy of the model is 0.75, demonstrating its effectiveness in making predictions.', "SVM's accuracy in prediction The model accurately predicts actual values, as demonstrated by zero actual and predicted values matching, and three actual and predicted values aligning, showcasing its accuracy.", "SVM's ability for classification and regression SVM is highlighted as capable of both classification and regression tasks, showcasing its versatility in handling different types of machine learning problems.", 'Support vector machine (SVM) can use different kernel functions like linear, polynomial, RBF, and sigmoid for classification and regression. SVM can utilize various kernel functions such as linear, polynomial, RBF, and sigmoid for classification and regression tasks.', 'The Aris dataset for classification contains 150 instances and 4 attributes, including sepal length, sepal width, petal length, and petal width.']}, {'end': 4453.635, 'segs': [{'end': 3351.437, 'src': 'heatmap', 'start': 3244.822, 'weight': 0.86, 'content': [{'end': 3249.265, 'text': 'uh, anyway, now, once we have uh splits our data set.', 'start': 3244.822, 'duration': 4.443}, {'end': 3251.787, 'text': 'uh, we actually need a model first.', 'start': 3249.265, 'duration': 2.522}, {'end': 3262.994, 'text': "so from um sklearn import svm and then here let's create our model.", 'start': 3251.787, 'duration': 11.207}, {'end': 3267.677, 'text': 'so model equals svm dot svc.', 'start': 3262.994, 'duration': 4.683}, {'end': 3269.632, 'text': "That's it.", 'start': 3269.212, 'duration': 0.42}, {'end': 3270.892, 'text': 'Now we created a model.', 'start': 3269.792, 'duration': 1.1}, {'end': 3272.873, 'text': 'We have to train our model.', 'start': 3270.932, 'duration': 1.941}, {'end': 3275.773, 'text': "So we'll say model dot fit.", 'start': 3272.913, 'duration': 2.86}, {'end': 3284.976, 'text': 'And then here we will pass it the X train comma and then the Y train.', 'start': 3277.214, 'duration': 7.762}, {'end': 3295.898, 'text': 'And then all we have to do is print our model and run the program to see if everything works properly.', 'start': 3287.916, 'duration': 7.982}, {'end': 3304.44, 'text': 'now, as you can see, the model ran.', 'start': 3301.678, 'duration': 2.762}, {'end': 3317.872, 'text': "so to determine the accuracy, uh, we'll have to go here and say from sklearn dot metrics, import accuracy, score, and then, um,", 'start': 3304.44, 'duration': 13.432}, {'end': 3319.513, 'text': 'here we actually have to make predictions.', 'start': 3317.872, 'duration': 1.641}, {'end': 3330.287, 'text': 'so predictions equals, and then it will say model dot, predict, And then we need to do the prediction with the X testing data.', 'start': 3319.513, 'duration': 10.774}, {'end': 3339.711, 'text': 'And then our accuracy ACC is simply going to be equal to accuracy score.', 'start': 3330.607, 'duration': 9.104}, {'end': 3344.353, 'text': 'And then here is Y test and comma predictions.', 'start': 3340.271, 'duration': 4.082}, {'end': 3351.437, 'text': 'And then here we can print our predictions and we can also print our accuracy.', 'start': 3345.274, 'duration': 6.163}], 'summary': 'Using svm model to train and test data, achieving accuracy score.', 'duration': 106.615, 'max_score': 3244.822, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU3244822.jpg'}, {'end': 3409.191, 'src': 'embed', 'start': 3376.093, 'weight': 0, 'content': [{'end': 3380.814, 'text': 'And as you can see, give us a pretty good accuracy of 0.93.', 'start': 3376.093, 'duration': 4.721}, {'end': 3382.394, 'text': 'And these are the predictions.', 'start': 3380.814, 'duration': 1.58}, {'end': 3386.035, 'text': 'So we could actually print them one next to the other.', 'start': 3382.994, 'duration': 3.041}, {'end': 3397.204, 'text': "So, for example, here we'll say actual and then here all we have to do is print white test.", 'start': 3386.598, 'duration': 10.606}, {'end': 3400.126, 'text': "So we'll have one on top of the other so we can compare.", 'start': 3397.805, 'duration': 2.321}, {'end': 3409.191, 'text': 'So here, as you can see, it was actually a two and no, we predicted a two was actually a two.', 'start': 3403.288, 'duration': 5.903}], 'summary': 'Achieved an accuracy of 93% in making predictions.', 'duration': 33.098, 'max_score': 3376.093, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU3376093.jpg'}, {'end': 3532.119, 'src': 'embed', 'start': 3478.957, 'weight': 2, 'content': [{'end': 3487.621, 'text': 'which says that it contains information collected by the u.s census service concerning housing in the area of boston.', 'start': 3478.957, 'duration': 8.664}, {'end': 3496.725, 'text': 'and we could, we can take a look at the features, and here we have 13 feature and one label.', 'start': 3487.621, 'duration': 9.104}, {'end': 3507.511, 'text': 'so this is cr am per capita, crime rates and all kinds of um information regarding housing, full value property.', 'start': 3496.725, 'duration': 10.786}, {'end': 3514.593, 'text': 'so these are all features and the last one will be um the label, all right.', 'start': 3507.511, 'duration': 7.082}, {'end': 3521.075, 'text': 'so here we have 506 cases and the data set seems legit.', 'start': 3514.593, 'duration': 6.482}, {'end': 3529.258, 'text': "so let's let's download the data set so you could find the links in the description, and let's open pycharm, create a new project,", 'start': 3521.075, 'duration': 8.183}, {'end': 3532.119, 'text': "and here we're going to call it linear regression.", 'start': 3529.258, 'duration': 2.861}], 'summary': 'The dataset contains 13 features, 1 label, and 506 cases related to housing in boston.', 'duration': 53.162, 'max_score': 3478.957, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU3478957.jpg'}, {'end': 3748.759, 'src': 'embed', 'start': 3713.597, 'weight': 5, 'content': [{'end': 3713.977, 'text': "That's it.", 'start': 3713.597, 'duration': 0.38}, {'end': 3715.299, 'text': 'We just created our model.', 'start': 3714.038, 'duration': 1.261}, {'end': 3723.367, 'text': "So this is the algorithm that we're using.", 'start': 3716.28, 'duration': 7.087}, {'end': 3734.514, 'text': "Algorithm Now let's try and visualize some of this data.", 'start': 3726.25, 'duration': 8.264}, {'end': 3737.155, 'text': "So we'll show you how to use PyPlot.", 'start': 3734.574, 'duration': 2.581}, {'end': 3740.717, 'text': 'So PyPlot, we need a relationship between an X and a Y.', 'start': 3737.816, 'duration': 2.901}, {'end': 3748.759, 'text': 'We already have a Y, but here in the X, we can only make a relationship between one feature at a time.', 'start': 3740.717, 'duration': 8.042}], 'summary': 'A model was created using a specific algorithm and pyplot was used to visualize the data.', 'duration': 35.162, 'max_score': 3713.597, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU3713597.jpg'}, {'end': 4078.015, 'src': 'embed', 'start': 4051.37, 'weight': 4, 'content': [{'end': 4060.618, 'text': "For example, if you look at this model, let's say you had a data point that is about here, or an X value, that is, let's say, 10,, 11, 12,", 'start': 4051.37, 'duration': 9.248}, {'end': 4062.7, 'text': 'we could make predictions for that as well.', 'start': 4060.618, 'duration': 2.082}, {'end': 4066.283, 'text': 'So this is it for linear regression model.', 'start': 4064.141, 'duration': 2.142}, {'end': 4078.015, 'text': "So let me just open up Paint real quick and let's start exploring linear regression.", 'start': 4071.748, 'duration': 6.267}], 'summary': 'Introducing linear regression model for making predictions.', 'duration': 26.645, 'max_score': 4051.37, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU4051370.jpg'}, {'end': 4193.546, 'src': 'embed', 'start': 4153.733, 'weight': 7, 'content': [{'end': 4160.776, 'text': 'And the closer the data points are to the regression line, the bigger the r-squared value is.', 'start': 4153.733, 'duration': 7.043}, {'end': 4166.379, 'text': 'The regression line is a simple linear function.', 'start': 4161.817, 'duration': 4.562}, {'end': 4173.116, 'text': 'So why? equals mx plus b.', 'start': 4166.479, 'duration': 6.637}, {'end': 4176.417, 'text': 'b is the intercept.', 'start': 4173.116, 'duration': 3.301}, {'end': 4182.72, 'text': "so it's here this is b and m.", 'start': 4176.417, 'duration': 6.303}, {'end': 4191.484, 'text': 'so m is equal to delta y over delta x.', 'start': 4182.72, 'duration': 8.764}, {'end': 4193.546, 'text': 'so the change in y over the change in x.', 'start': 4191.484, 'duration': 2.062}], 'summary': 'The closer data points are to the regression line, the bigger the r-squared value, determined by the simple linear function y=mx+b with m=δy/δx.', 'duration': 39.813, 'max_score': 4153.733, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU4153733.jpg'}, {'end': 4285.216, 'src': 'embed', 'start': 4255.44, 'weight': 8, 'content': [{'end': 4257.482, 'text': "Now let's take a look at logistic regression.", 'start': 4255.44, 'duration': 2.042}, {'end': 4266.568, 'text': "Now for logistic regression, let's say, for example, our data points are scattered in a way that looks something like this.", 'start': 4258.102, 'duration': 8.466}, {'end': 4268.649, 'text': 'All right, so we have data points like this.', 'start': 4266.588, 'duration': 2.061}, {'end': 4274.453, 'text': 'Here we have data points, all right, like this.', 'start': 4269.57, 'duration': 4.883}, {'end': 4285.216, 'text': 'then we have data points that look something like this all right, if we had to draw a linear regression function,', 'start': 4276.249, 'duration': 8.967}], 'summary': 'Introduction to logistic regression with data visualization.', 'duration': 29.776, 'max_score': 4255.44, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU4255440.jpg'}], 'start': 3172.638, 'title': 'Machine learning models', 'summary': 'Discusses data splitting, model building, and training, achieving 93% accuracy using svm in python, linear regression analysis on a dataset with 506 cases, and basics of linear and logistic regression with mathematical explanations and visual representations.', 'chapters': [{'end': 3251.787, 'start': 3172.638, 'title': 'Data splitting and model building', 'summary': 'Discusses the process of splitting a dataset into classes and the significance of the classic eris dataset in machine learning, emphasizing the necessity of a model for further analysis.', 'duration': 79.149, 'highlights': ['The Eris dataset is one of the classic datasets first used for machine learning, used in this beginners course.', 'Discussing the process of splitting a dataset into classes and emphasizing the necessity of a model for further analysis.']}, {'end': 3507.511, 'start': 3251.787, 'title': 'Model training and accuracy assessment', 'summary': 'Demonstrates model creation using svm in python, achieving a 93% accuracy score on a dataset, and validating the predictions with the actual values.', 'duration': 255.724, 'highlights': ['The model achieved a pretty good accuracy of 0.93. The accuracy of the model on the dataset was 0.93, indicating a strong performance.', "Validating the predictions with the actual values yielded 100% accuracy. The predictions closely matched the actual values, resulting in a 100% accuracy rate, showcasing the model's effectiveness.", 'The dataset used for the model was the Boston dataset containing 13 features and 1 label. The model was trained and assessed using the Boston dataset, which comprises 13 features and 1 label, providing context for the data used in the demonstration.']}, {'end': 3839.712, 'start': 3507.511, 'title': 'Linear regression data analysis', 'summary': 'Covers the process of importing a dataset containing 506 cases, creating features and labels, and visualizing the relationship between features and pricing of houses using pyplot, aiming to develop a linear regression model.', 'duration': 332.201, 'highlights': ['The dataset contains 506 cases and is formatted properly as a numpy array, suitable for fitting into a linear model. The dataset contains 506 cases and is properly formatted as a numpy array, suitable for fitting into a linear model.', 'Creation of the linear regression model using linear model dot linear regression, serving as the algorithm for the analysis. Creation of the linear regression model using linear model dot linear regression, serving as the algorithm for the analysis.', 'Utilizing PyPlot to visualize the relationship between features and pricing of houses, demonstrating the process of creating relationships between individual features and the pricing of houses. Utilizing PyPlot to visualize the relationship between features and pricing of houses, demonstrating the process of creating relationships between individual features and the pricing of houses.']}, {'end': 4453.635, 'start': 3839.712, 'title': 'Linear and logistic regression', 'summary': 'Discusses the basics of linear and logistic regression, including model training, testing, prediction, r-squared value, coefficient factor, intercept, and the concept of linear and logistic regression with detailed mathematical explanations and visual representations.', 'duration': 613.923, 'highlights': ['The chapter discusses the basics of linear regression, including model training, testing, prediction, R-squared value, coefficient factor, and intercept. It covers model training, testing, prediction, printing R-squared value, coefficient factor, and intercept, demonstrating the process and key metrics for evaluating linear regression models.', 'The concept of linear regression is explained with detailed mathematical explanations and visual representations, including the regression line, R-squared value, and the linear function y=mx+b. Detailed mathematical explanations and visual representations are provided, explaining the regression line, R-squared value, linear function y=mx+b, and the concept of linear regression.', "The chapter also introduces logistic regression, explaining its concept, the sigmoid function, and its role in converting values from 0 to 1. The concept of logistic regression is introduced, explaining the sigmoid function's role in converting values from 0 to 1 and providing a comprehensive understanding of logistic regression."]}], 'duration': 1280.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU3172638.jpg', 'highlights': ['The model achieved a pretty good accuracy of 0.93, indicating a strong performance.', "Validating the predictions with the actual values yielded 100% accuracy, showcasing the model's effectiveness.", 'The dataset used for the model was the Boston dataset containing 13 features and 1 label, providing context for the data used in the demonstration.', 'The dataset contains 506 cases and is properly formatted as a numpy array, suitable for fitting into a linear model.', 'Creation of the linear regression model using linear model dot linear regression, serving as the algorithm for the analysis.', 'Utilizing PyPlot to visualize the relationship between features and pricing of houses, demonstrating the process of creating relationships between individual features and the pricing of houses.', 'The chapter discusses the basics of linear regression, including model training, testing, prediction, R-squared value, coefficient factor, and intercept, demonstrating the process and key metrics for evaluating linear regression models.', 'The concept of linear regression is explained with detailed mathematical explanations and visual representations, including the regression line, R-squared value, and the linear function y=mx+b.', 'The chapter also introduces logistic regression, explaining its concept, the sigmoid function, and its role in converting values from 0 to 1.']}, {'end': 6092.603, 'segs': [{'end': 4693.956, 'src': 'embed', 'start': 4654.378, 'weight': 0, 'content': [{'end': 4655.759, 'text': 'So this is the black label.', 'start': 4654.378, 'duration': 1.381}, {'end': 4662.542, 'text': 'And one of these algorithms for clustering is called k-means.', 'start': 4657.66, 'duration': 4.882}, {'end': 4671.925, 'text': 'And basically, it uses centroids, something called centroids, all right? Centroids.', 'start': 4663.322, 'duration': 8.603}, {'end': 4678.727, 'text': "And our centroids, let's represent them as a green point, all right? Let's make a big green point.", 'start': 4672.765, 'duration': 5.962}, {'end': 4681.388, 'text': 'And this would be a centroid.', 'start': 4679.428, 'duration': 1.96}, {'end': 4684.929, 'text': 'So the algorithm starts by generating.', 'start': 4681.988, 'duration': 2.941}, {'end': 4692.875, 'text': 'two centroids.', 'start': 4689.193, 'duration': 3.682}, {'end': 4693.956, 'text': 'it is.', 'start': 4692.875, 'duration': 1.081}], 'summary': 'The k-means clustering algorithm uses centroids to generate two centroids.', 'duration': 39.578, 'max_score': 4654.378, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU4654378.jpg'}, {'end': 4883.319, 'src': 'embed', 'start': 4855.386, 'weight': 1, 'content': [{'end': 4865.271, 'text': 'it will keep repeating over and over again until it finds the correct plane that separates both of our clusters.', 'start': 4855.386, 'duration': 9.885}, {'end': 4868.63, 'text': 'So this is the red cluster and this is the black cluster.', 'start': 4866.368, 'duration': 2.262}, {'end': 4879.436, 'text': 'And of course, one very important detail about clustering is that the labels are never part of the algorithm.', 'start': 4869.47, 'duration': 9.966}, {'end': 4883.319, 'text': "So the algorithm doesn't actually know that these are reds and these are black.", 'start': 4879.457, 'duration': 3.862}], 'summary': 'Algorithm finds plane to separate clusters without using labels.', 'duration': 27.933, 'max_score': 4855.386, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU4855386.jpg'}, {'end': 5475.089, 'src': 'embed', 'start': 5449.411, 'weight': 2, 'content': [{'end': 5458.036, 'text': 'so you could actually do it manually yourself and create your own k-mean clustering algorithm without using, uh, sk learned.', 'start': 5449.411, 'duration': 8.625}, {'end': 5466.782, 'text': 'but, uh, for now, just understand what k means is, And this is enough for now.', 'start': 5458.036, 'duration': 8.746}, {'end': 5475.089, 'text': "So let's start by importing some of our sklearn libraries.", 'start': 5471.306, 'duration': 3.783}], 'summary': 'Learn to manually create a k-mean clustering algorithm without using scikit-learn and understand the concept of k-means.', 'duration': 25.678, 'max_score': 5449.411, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU5449411.jpg'}, {'end': 5653.516, 'src': 'heatmap', 'start': 5545.217, 'weight': 0.835, 'content': [{'end': 5548.199, 'text': "now let's create our breast cancer data set.", 'start': 5545.217, 'duration': 2.982}, {'end': 5553.221, 'text': 'so BC equals load breast cancer.', 'start': 5548.199, 'duration': 5.022}, {'end': 5553.781, 'text': 'and there we go.', 'start': 5553.221, 'duration': 0.56}, {'end': 5561.385, 'text': 'we can even print it and take a look at our data set, so BC, and see what all of this looks like.', 'start': 5553.781, 'duration': 7.604}, {'end': 5570.81, 'text': "so already we can see this is an umpire a and it says it's our data.", 'start': 5561.385, 'duration': 9.425}, {'end': 5578.395, 'text': 'so it has to be our features And then our targets, in other words our labels.', 'start': 5570.81, 'duration': 7.585}, {'end': 5584.403, 'text': "Here we have a big NumPy array, and it's either a zero or a one.", 'start': 5579.616, 'duration': 4.787}, {'end': 5590.451, 'text': "And then we have our target name, so it tells us it's either malignant or banning.", 'start': 5585.404, 'duration': 5.047}, {'end': 5594.205, 'text': 'And this is breast cancer terms.', 'start': 5592.063, 'duration': 2.142}, {'end': 5595.346, 'text': "I'm not really sure what this means.", 'start': 5594.265, 'duration': 1.081}, {'end': 5600.93, 'text': 'But we can even here look at our features.', 'start': 5595.866, 'duration': 5.064}, {'end': 5607.675, 'text': 'And then it has a bunch of them, smoothness areas and all kinds of attributes.', 'start': 5601.57, 'duration': 6.105}, {'end': 5614.08, 'text': "All right, so let's add another bit of code and create our x variable.", 'start': 5609.036, 'duration': 5.044}, {'end': 5618.764, 'text': "And it's going to be equal to bc.data.", 'start': 5615.841, 'duration': 2.923}, {'end': 5621.526, 'text': 'And we can even print it.', 'start': 5619.704, 'duration': 1.822}, {'end': 5625.388, 'text': 'take a look at it.', 'start': 5623.807, 'duration': 1.581}, {'end': 5638.373, 'text': 'and then here this just took this chunk of our data and it gave it to us as a list.', 'start': 5625.388, 'duration': 12.985}, {'end': 5649.584, 'text': "and if we just take a look at the numbers, we can see that the numbers are somewhat big and small and there's a very.", 'start': 5638.373, 'duration': 11.211}, {'end': 5653.516, 'text': "there's a big difference between the numbers, so let's just scale them.", 'start': 5649.584, 'duration': 3.932}], 'summary': 'Creating a breast cancer dataset using numpy array with targets as 0 or 1.', 'duration': 108.299, 'max_score': 5545.217, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU5545217.jpg'}, {'end': 5936.173, 'src': 'embed', 'start': 5903.587, 'weight': 3, 'content': [{'end': 5911.169, 'text': 'And the thing with k-means is that we have to pay attention at the predictions.', 'start': 5903.587, 'duration': 7.582}, {'end': 5917.452, 'text': "Here, we've been lucky enough so that both ones represent the same thing.", 'start': 5912.108, 'duration': 5.344}, {'end': 5924.698, 'text': 'So basically here, cluster zero represents label zero.', 'start': 5919.234, 'duration': 5.464}, {'end': 5926.039, 'text': 'They are the same.', 'start': 5925.239, 'duration': 0.8}, {'end': 5936.173, 'text': 'And then the algorithm came up by saying that cluster zero labeled the first one as zero, and it happened to be also a label of zero.', 'start': 5926.159, 'duration': 10.014}], 'summary': 'K-means resulted in accurate predictions, with cluster 0 representing label 0.', 'duration': 32.586, 'max_score': 5903.587, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU5903587.jpg'}], 'start': 4453.655, 'title': 'K-means clustering', 'summary': 'Explains k-means clustering algorithm for grouping data points, discusses clustering hyperplane equation, and demonstrates implementing k-means clustering in python with a breast cancer dataset, using the sklearn library and creating a k-means model with 2 clusters.', 'chapters': [{'end': 4833.266, 'start': 4453.655, 'title': 'K-means clustering explained', 'summary': 'Explains k-means clustering as an algorithm for grouping data points into clusters based on centroids, where centroids are generated at random positions and the algorithm iteratively finds the average position of points to assign clusters, with an example of two labels and centroids on a 2d graph.', 'duration': 379.611, 'highlights': ['The chapter explains the concept of k-means clustering and its use in grouping data points into clusters based on centroids, with an example of two labels and centroids on a 2D graph.', 'K-means clustering algorithm uses centroids, which are initially generated at random positions, and iteratively finds the average position of points to assign clusters based on the distance to centroids.', "The example illustrates the use of k-means clustering with two labels and centroids on a 2D graph, demonstrating how the algorithm separates data points into clusters based on the centroids' positions."]}, {'end': 5449.411, 'start': 4833.527, 'title': 'Clustering hyperplane equation', 'summary': 'Discusses the algorithm for finding the equation of the hyperplane that separates clusters, including the calculation process and the transition to 3d, emphasizing that clustering labels are not part of the algorithm.', 'duration': 615.884, 'highlights': ['The algorithm repetitively finds the correct hyperplane equation to separate clusters, even when centroids are initially positioned incorrectly, iterating millions of times for accuracy.', 'The clustering algorithm operates without considering cluster labels, focusing solely on identifying clusters and their separation, leaving the labeling to be done externally.', "The process of calculating the hyperplane equation involves determining the slope using centroid coordinates, finding a point, and solving for the constant 'b' to derive the equation.", 'In the transition to a 3D plane, the process of finding the hyperplane equation involves using directional vectors and determining the equation of the plane in a similar manner to the 2D case.']}, {'end': 5756.976, 'start': 5449.411, 'title': 'Implementing k-means clustering in python', 'summary': 'Introduces k-means clustering in python using the sklearn library, preprocessing the breast cancer dataset, splitting the data into training and testing sets, and creating a k-means model with 2 clusters.', 'duration': 307.565, 'highlights': ['Introducing K-means clustering using the sklearn library The chapter covers the introduction of K-means clustering in Python using the sklearn library to create a K-means clustering algorithm.', 'Preprocessing the breast cancer dataset The transcript discusses preprocessing the breast cancer dataset, including importing the dataset, scaling the data to improve the model, and creating the X and Y variables.', 'Splitting the data into training and testing sets The chapter explains the process of splitting the data into training and testing sets using the train_test_split function from the sklearn library with a test size of 0.2.', 'Creating a K-means model with 2 clusters The chapter demonstrates the creation of a K-means model with 2 clusters using the KMeans class from the sklearn library and specifying the number of clusters and a random state for reproducibility.']}, {'end': 6092.603, 'start': 5757.817, 'title': 'K-means clustering in python', 'summary': 'Explains how to fit a k-means clustering model in python, make predictions, calculate accuracy scores, and fix label issues, emphasizing the importance of paying attention to predictions and verifying the order of clusters to ensure accurate results.', 'duration': 334.786, 'highlights': ["The importance of paying attention to predictions in K-means clustering to ensure accurate results, as misinterpretations can lead to significant errors and inaccuracies in the model's performance.", 'The process of fitting a K-means clustering model in Python using the model.fit method and training data, followed by making predictions with testing features and calculating accuracy scores using sklearn.metrics.accuracy_score.', 'The significance of verifying the order of clusters in K-means clustering, as demonstrated by using cross tabulation to fix label issues and ensure the correct interpretation of cluster predictions.', 'Explanation of the process of separating labels and letting the algorithm create different clusters by itself in K-means clustering, highlighting the absence of passing the Y parameter for clustering.', 'The use of cross tabulation to verify the order of clusters and fix label issues in K-means clustering, ensuring the correct alignment of cluster predictions with the actual labels for accurate model performance.']}], 'duration': 1638.948, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU4453655.jpg', 'highlights': ['The chapter explains the concept of k-means clustering and its use in grouping data points into clusters based on centroids.', 'The algorithm repetitively finds the correct hyperplane equation to separate clusters, even when centroids are initially positioned incorrectly, iterating millions of times for accuracy.', 'Introducing K-means clustering using the sklearn library The chapter covers the introduction of K-means clustering in Python using the sklearn library to create a K-means clustering algorithm.', "The importance of paying attention to predictions in K-means clustering to ensure accurate results, as misinterpretations can lead to significant errors and inaccuracies in the model's performance."]}, {'end': 7402.819, 'segs': [{'end': 6174.923, 'src': 'embed', 'start': 6132.936, 'weight': 0, 'content': [{'end': 6147.081, 'text': 'To better understand, let me just open a window here so that I can show you what neural networks look like.', 'start': 6132.936, 'duration': 14.145}, {'end': 6157.18, 'text': 'But before we get started with that, you have to understand that the goal of neural networks is to find patterns.', 'start': 6148.379, 'duration': 8.801}, {'end': 6158.081, 'text': 'All right.', 'start': 6157.761, 'duration': 0.32}, {'end': 6160.641, 'text': 'So let me just illustrate.', 'start': 6158.681, 'duration': 1.96}, {'end': 6164.182, 'text': 'Let me just illustrate this.', 'start': 6163.041, 'duration': 1.141}, {'end': 6174.923, 'text': 'So just like in machine learning, we always have our inputs and our outputs.', 'start': 6164.962, 'duration': 9.961}], 'summary': 'Neural networks find patterns in machine learning.', 'duration': 41.987, 'max_score': 6132.936, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU6132936.jpg'}, {'end': 6636.335, 'src': 'embed', 'start': 6607.018, 'weight': 1, 'content': [{'end': 6613.721, 'text': 'And A is the summation of the W and the X one with all the biases added together.', 'start': 6607.018, 'duration': 6.703}, {'end': 6615.882, 'text': "And basically, the program, when it's running,", 'start': 6614.261, 'duration': 1.621}, {'end': 6627.747, 'text': 'is just gonna adjust the weights and biases just to optimize our neural network and give us really a great, a very accurate output.', 'start': 6615.882, 'duration': 11.865}, {'end': 6631.532, 'text': 'Now, let me just replace here A.', 'start': 6628.951, 'duration': 2.581}, {'end': 6636.335, 'text': "And sometimes in the textbooks, you're not going to be seeing this addition like that.", 'start': 6631.532, 'duration': 4.803}], 'summary': 'Neural network adjusts weights and biases to optimize for accurate output.', 'duration': 29.317, 'max_score': 6607.018, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU6607018.jpg'}, {'end': 6825.043, 'src': 'embed', 'start': 6796.044, 'weight': 4, 'content': [{'end': 6801.488, 'text': 'And now what we did right here is add a level of complexity to our neural network.', 'start': 6796.044, 'duration': 5.444}, {'end': 6802.308, 'text': 'all right?', 'start': 6801.488, 'duration': 0.82}, {'end': 6812.095, 'text': "And this allows the neural network to figure out more patterns or things that it wouldn't have been able to figure out just by doing a normal linear combination.", 'start': 6802.829, 'duration': 9.266}, {'end': 6825.043, 'text': 'And then now we add the number of combinations are augmented and, for example, each of these combinations have its own,', 'start': 6812.495, 'duration': 12.548}], 'summary': "Adding complexity improves neural network's pattern recognition and combinations.", 'duration': 28.999, 'max_score': 6796.044, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU6796044.jpg'}, {'end': 6882.744, 'src': 'embed', 'start': 6850.963, 'weight': 5, 'content': [{'end': 6857.607, 'text': 'And the activation function, just to give you a small idea, is basically we take the input like this.', 'start': 6850.963, 'duration': 6.644}, {'end': 6860.008, 'text': 'Of course, we get an output.', 'start': 6858.007, 'duration': 2.001}, {'end': 6865.191, 'text': 'And depending on the function, so we take this huge number we could convert it.', 'start': 6860.989, 'duration': 4.202}, {'end': 6877.499, 'text': 'For example, we can make it a linear function, a sigmoid function or a sigmoid function, or whatever that may be,', 'start': 6867.032, 'duration': 10.467}, {'end': 6882.744, 'text': 'so that the neural network can actually process this information.', 'start': 6877.499, 'duration': 5.245}], 'summary': 'Activation functions process input data for neural networks to process information efficiently.', 'duration': 31.781, 'max_score': 6850.963, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU6850963.jpg'}, {'end': 6939.986, 'src': 'embed', 'start': 6907.766, 'weight': 2, 'content': [{'end': 6914.527, 'text': 'just so that we can use it on external data and hopefully get meaningful results.', 'start': 6907.766, 'duration': 6.761}, {'end': 6919.968, 'text': 'Neural networks can be used for tests such as classifications.', 'start': 6915.787, 'duration': 4.181}, {'end': 6924.163, 'text': 'Examples of such could be image recognition.', 'start': 6920.962, 'duration': 3.201}, {'end': 6928.444, 'text': 'They can also be used for text classification.', 'start': 6924.763, 'duration': 3.681}, {'end': 6933.705, 'text': 'For example, determining whether an email is a spam or not.', 'start': 6928.904, 'duration': 4.801}, {'end': 6938.206, 'text': 'Neural networks can be used for regression.', 'start': 6934.525, 'duration': 3.681}, {'end': 6939.986, 'text': 'They can be used for regression.', 'start': 6938.366, 'duration': 1.62}], 'summary': 'Neural networks can be used for image and text classification, regression, and spam detection.', 'duration': 32.22, 'max_score': 6907.766, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU6907766.jpg'}, {'end': 7033.001, 'src': 'embed', 'start': 7004.469, 'weight': 3, 'content': [{'end': 7013.845, 'text': "after we train the model, after a lot of iterations, we are able to test our model on data that it's never seen before.", 'start': 7004.469, 'duration': 9.376}, {'end': 7027.939, 'text': 'And this way we can have an accuracy, for example, nine out of 10, nine out of 10 are predicted properly, and that would give us a 90% accuracy.', 'start': 7014.245, 'duration': 13.694}, {'end': 7033.001, 'text': 'And this is one way of calculating the accuracy.', 'start': 7028.279, 'duration': 4.722}], 'summary': 'Model achieves 90% accuracy after training and testing.', 'duration': 28.532, 'max_score': 7004.469, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU7004469.jpg'}, {'end': 7196.015, 'src': 'embed', 'start': 7130.122, 'weight': 7, 'content': [{'end': 7134.826, 'text': "or we're using the wrong kind of activation functions, or whatever the reason may be,", 'start': 7130.122, 'duration': 4.704}, {'end': 7144.972, 'text': 'it might happen that we are in a case of underfitting and the line that the model might have come up with could look something like this', 'start': 7134.826, 'duration': 10.146}, {'end': 7160.601, 'text': "This is an example of underfitting because if we take a look, let's draw our testing data just for it to be a little more clear.", 'start': 7147.294, 'duration': 13.307}, {'end': 7163.022, 'text': "Let's draw our testing data.", 'start': 7161.181, 'duration': 1.841}, {'end': 7165.984, 'text': 'For example, this is our testing data points.', 'start': 7163.562, 'duration': 2.422}, {'end': 7168.435, 'text': 'They look something like this.', 'start': 7166.874, 'duration': 1.561}, {'end': 7184.321, 'text': 'So in the case of underfitting like this, as we can see, we have a high variance on the training data.', 'start': 7173.877, 'duration': 10.444}, {'end': 7192.493, 'text': 'which means that the training data is not predicted properly by our model, which is the straight red line.', 'start': 7185.509, 'duration': 6.984}, {'end': 7196.015, 'text': 'And we also have a high variance on the testing data.', 'start': 7193.093, 'duration': 2.922}], 'summary': 'Underfitting results in high variance on both training and testing data.', 'duration': 65.893, 'max_score': 7130.122, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU7130122.jpg'}], 'start': 6093.443, 'title': 'Neural networks fundamentals', 'summary': 'Provides an introduction to neural networks, explaining inputs, outputs, weights, and activation functions. it also emphasizes the importance of weights and biases, discusses hidden layers, and explains model accuracy, underfitting, and overfitting.', 'chapters': [{'end': 6489.465, 'start': 6093.443, 'title': 'Neural networks basics', 'summary': 'Provides an introduction to neural networks, explaining the concept of inputs, outputs, weights, and activation functions, aiming to find patterns and adjust weights during training.', 'duration': 396.022, 'highlights': ['Neural networks aim to find patterns and adjust weights during training, illustrated by the concept of inputs, outputs, weights, and activation functions.', 'The inputs in neural networks are connected, each with an associated weight, and the output is calculated using a linear combination and an activation function.', 'The program adjusts the weights during training to determine the relevance of inputs, aiding in pattern recognition and effective decision-making.']}, {'end': 6683.112, 'start': 6489.465, 'title': 'Neural network weights and biases', 'summary': 'Explains the importance of weights and biases in a neural network, highlighting the process of adjusting these parameters to optimize the network for accurate output.', 'duration': 193.647, 'highlights': ['The program adjusts the weights and biases to optimize the neural network for accurate output.', 'The summation of the inputs multiplied by their respective weights, added to the biases, is used as the input for the activation function.', 'The chapter introduces the concept of weights (w) and biases (b) in the context of a neural network, where biases act as constants added to the weighted inputs, and the program adjusts these parameters to optimize network performance.', 'The chapter discusses the mathematical representation of the inputs (x) multiplied by their weights (w) and added to the biases (b) in the context of a neural network.']}, {'end': 6975.004, 'start': 6683.112, 'title': 'Neural networks and hidden layers', 'summary': 'Discusses the concept of neural networks, hidden layers, weights, biases, and activation functions, emphasizing the importance and diverse applications of neural networks in various domains.', 'duration': 291.892, 'highlights': ['Neural networks can be used for tests such as classifications, image recognition, text classification, regression, clustering, chatbot creation, AI, and robotics. Neural networks have diverse applications including classifications, image recognition, text classification, regression, clustering, chatbot creation, AI, and robotics.', 'Explanation of hidden layers and their role in adding complexity to neural networks, allowing the network to figure out more patterns. Hidden layers add complexity and enable neural networks to identify more patterns, enhancing their capabilities for pattern recognition.', "Description of the addition of hidden layers and the impact on the number of combinations, weights, and biases, leading to an increased capacity for pattern recognition. The addition of hidden layers increases the number of combinations, weights, and biases, augmenting the neural network's ability for pattern recognition."]}, {'end': 7402.819, 'start': 6976.05, 'title': 'Model accuracy and underfitting vs overfitting', 'summary': 'Explains the importance of model accuracy, problems of underfitting and overfitting, and their impact on training and testing data with examples, emphasizing the need for a perfect fit.', 'duration': 426.769, 'highlights': ['The importance of model accuracy is emphasized, with an example of achieving 90% accuracy through proper testing on data. The model aims for the highest accuracy possible, with an example of achieving 90% accuracy through proper testing on data.', 'Underfitting is explained, with a focus on the scenarios leading to underfitting such as low training set, wrong activation functions, and oversimplified models. Underfitting scenarios include low training set, wrong activation functions, and oversimplified models.', 'Overfitting is described, highlighting the high variance on testing data and low variance on training data, leading to memorization of training data and poor prediction of testing data. Overfitting leads to high variance on testing data, low variance on training data, and memorization of training data.']}], 'duration': 1309.376, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU6093443.jpg', 'highlights': ['Neural networks aim to find patterns and adjust weights during training, illustrated by the concept of inputs, outputs, weights, and activation functions.', 'The program adjusts the weights and biases to optimize the neural network for accurate output.', 'Neural networks can be used for tests such as classifications, image recognition, text classification, regression, clustering, chatbot creation, AI, and robotics.', 'The importance of model accuracy is emphasized, with an example of achieving 90% accuracy through proper testing on data.', 'Explanation of hidden layers and their role in adding complexity to neural networks, allowing the network to figure out more patterns.', 'The inputs in neural networks are connected, each with an associated weight, and the output is calculated using a linear combination and an activation function.', "The addition of hidden layers increases the number of combinations, weights, and biases, augmenting the neural network's ability for pattern recognition.", 'Underfitting is explained, with a focus on the scenarios leading to underfitting such as low training set, wrong activation functions, and oversimplified models.', 'Overfitting is described, highlighting the high variance on testing data and low variance on training data, leading to memorization of training data and poor prediction of testing data.']}, {'end': 9094.215, 'segs': [{'end': 7676.659, 'src': 'embed', 'start': 7600.599, 'weight': 1, 'content': [{'end': 7607.743, 'text': 'Alright. so now the way that a neural network trains is by adjusting the weights and biases.', 'start': 7600.599, 'duration': 7.144}, {'end': 7615, 'text': "and what I mean by that is, for example, let's say, the first time we We input.", 'start': 7607.743, 'duration': 7.257}, {'end': 7620.042, 'text': "so let's say, for example, we're trying to create a hand digit recognizer.", 'start': 7615, 'duration': 5.042}, {'end': 7625.483, 'text': 'So we take, for example, pixels of a 28 by 28 image.', 'start': 7621.102, 'duration': 4.381}, {'end': 7628.984, 'text': "So let's say you have an image of 28 by 28 pixels.", 'start': 7625.503, 'duration': 3.481}, {'end': 7643.199, 'text': 'And then we convert all of these into a list of 784 pixels.', 'start': 7637.236, 'duration': 5.963}, {'end': 7648.122, 'text': 'And then these, for example, will be our inputs.', 'start': 7644.38, 'duration': 3.742}, {'end': 7656.427, 'text': 'Now, the way that a model would train is by, for example, we have 784 pixels.', 'start': 7649.663, 'duration': 6.764}, {'end': 7658.148, 'text': "And let's say we have 10, 000 pixels.", 'start': 7656.487, 'duration': 1.661}, {'end': 7668.734, 'text': '10, 000 training data, 10, 000 instances of training data.', 'start': 7664.851, 'duration': 3.883}, {'end': 7676.659, 'text': 'And each time that the model trains, it will fetch in the labels here.', 'start': 7669.874, 'duration': 6.785}], 'summary': 'Neural network trains by adjusting weights and biases using 784 pixels and 10,000 training instances.', 'duration': 76.06, 'max_score': 7600.599, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU7600599.jpg'}, {'end': 7738.545, 'src': 'embed', 'start': 7702.268, 'weight': 3, 'content': [{'end': 7707.093, 'text': 'The cost function of a neural network tells the neural network how bad it did.', 'start': 7702.268, 'duration': 4.825}, {'end': 7713.239, 'text': 'And this way you can learn by doing it over and over again and trying to minimize the cost.', 'start': 7707.553, 'duration': 5.686}, {'end': 7731.174, 'text': 'So the cost function that is commonly used is the mean squared error function, and this is by saying y, minus here,', 'start': 7714.66, 'duration': 16.514}, {'end': 7738.545, 'text': 'and then so you take the difference of our predicted y.', 'start': 7731.174, 'duration': 7.371}], 'summary': 'Neural network learns by minimizing mean squared error function.', 'duration': 36.277, 'max_score': 7702.268, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU7702268.jpg'}, {'end': 7985.429, 'src': 'embed', 'start': 7926.162, 'weight': 4, 'content': [{'end': 7935.694, 'text': 'and the goal of our neural network is to minimize this cost functions by adjusting the weights and the biases that each neuron has.', 'start': 7926.162, 'duration': 9.532}, {'end': 7941.822, 'text': 'For example here, as I said in the previous video, the goal of our neural network is to detect patterns.', 'start': 7935.734, 'duration': 6.088}, {'end': 7945.185, 'text': 'and, for example, each hidden layer.', 'start': 7942.843, 'duration': 2.342}, {'end': 7953.029, 'text': "we hope that will detect a pattern and we don't want the model to memorize anything all right.", 'start': 7945.185, 'duration': 7.844}, {'end': 7962.054, 'text': "so now that you understand what's a loss function or a cost function is, let's try and talk about gradient descent,", 'start': 7953.029, 'duration': 9.025}, {'end': 7972.901, 'text': 'and gradient descent is the way that neural networks use to find to minimize the cost function.', 'start': 7962.054, 'duration': 10.847}, {'end': 7977.844, 'text': 'So once we know how to calculate the cost function, we want a way to minimize it.', 'start': 7973.261, 'duration': 4.583}, {'end': 7985.429, 'text': 'And this way, the model can over the time train and improve its accuracy.', 'start': 7979.005, 'duration': 6.424}], 'summary': 'Neural network aims to minimize cost function via gradient descent for pattern detection and accuracy improvement.', 'duration': 59.267, 'max_score': 7926.162, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU7926162.jpg'}, {'end': 8468.949, 'src': 'embed', 'start': 8438.012, 'weight': 0, 'content': [{'end': 8441.414, 'text': 'And this hidden layer may be connected to a second hidden layer.', 'start': 8438.012, 'duration': 3.402}, {'end': 8444.976, 'text': 'And finally, we would always have an output layer.', 'start': 8442.234, 'duration': 2.742}, {'end': 8450.479, 'text': 'And here we would always have connections all right?', 'start': 8446.797, 'duration': 3.682}, {'end': 8459.803, 'text': 'right. so this is what our neural network looks like.', 'start': 8456.241, 'duration': 3.562}, {'end': 8468.949, 'text': 'and if you might, if you remember, we always had our inputs here at the input layer like this,', 'start': 8459.803, 'duration': 9.146}], 'summary': 'Neural network with hidden and output layers connected for processing inputs.', 'duration': 30.937, 'max_score': 8438.012, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU8438012.jpg'}, {'end': 8632.642, 'src': 'embed', 'start': 8594.968, 'weight': 6, 'content': [{'end': 8606.715, 'text': 'multiplied by the derivative of the activation function over the derivative of z, which is the input to that layer,', 'start': 8594.968, 'duration': 11.747}, {'end': 8624.473, 'text': 'which is the sum of all the activation functions coming from the previous node, multiplied by dz over dw, which is basically the change in the weight.', 'start': 8606.715, 'duration': 17.758}, {'end': 8632.642, 'text': 'so so, basically, back propagation is simply the chain rule of calculus,', 'start': 8624.473, 'duration': 8.169}], 'summary': 'Back propagation is the chain rule of calculus.', 'duration': 37.674, 'max_score': 8594.968, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU8594968.jpg'}, {'end': 8842.821, 'src': 'embed', 'start': 8808.655, 'weight': 7, 'content': [{'end': 8818.415, 'text': 'you could imagine it as a normal layer, so here i illustrate it for you as the blue layer,', 'start': 8808.655, 'duration': 9.76}, {'end': 8829.017, 'text': 'and the difference between the convolutional layer and the normal dense layers is that the convolutional layer adds a filter.', 'start': 8818.415, 'duration': 10.602}, {'end': 8838.98, 'text': 'So the filter is what makes the difference, because this filter will actually allow the layer to detect patterns,', 'start': 8829.797, 'duration': 9.183}, {'end': 8842.821, 'text': 'and this is why CNNs are mostly used in image recognition.', 'start': 8838.98, 'duration': 3.841}], 'summary': 'Cnns use filters to detect patterns in images.', 'duration': 34.166, 'max_score': 8808.655, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU8808655.jpg'}], 'start': 7402.819, 'title': 'Neural network training and structure', 'summary': 'Covers the structure and training process of a neural network, including adjusting weights and biases using 784 pixels and 10,000 training instances for a hand digit recognizer. it also delves into the cost function, gradient descent, back propagation, and the role of convolutional layers in cnns for image recognition.', 'chapters': [{'end': 7676.659, 'start': 7402.819, 'title': 'Neural network structure and training', 'summary': 'Explains the structure of a neural network with input, hidden, and output layers, and the training process involving adjusting weights and biases using 784 pixels and 10,000 training instances for a hand digit recognizer.', 'duration': 273.84, 'highlights': ['A neural network consists of input, hidden, and output layers with connections between neurons. The structure of a neural network is explained, showing the arrangement of layers and connections.', 'The training process of a neural network involves adjusting weights and biases. The process of training a neural network is described, emphasizing the adjustment of weights and biases for learning.', 'Example of training a hand digit recognizer using 784 pixels and 10,000 instances of training data. An example of training a hand digit recognizer is provided, highlighting the use of 784 pixels and 10,000 instances of training data.']}, {'end': 8081.014, 'start': 7677.97, 'title': 'Neural network cost function & gradient descent', 'summary': "Discusses the cost function of a neural network, particularly the mean squared error function, and explains the concept of gradient descent for minimizing the cost function to improve the model's accuracy over time.", 'duration': 403.044, 'highlights': ["The cost function of a neural network is commonly the mean squared error function, which measures the difference between predicted output and actual output, allowing the model to learn and improve by minimizing this cost. The cost function of a neural network is commonly the mean squared error function, where the difference between predicted output and actual output is squared to measure the model's performance and facilitate learning and improvement.", "Gradient descent is utilized by neural networks to minimize the cost function, involving finding the gradient of the cost function and taking the negative of the gradient to determine the direction in which the function decreases the fastest. Gradient descent is utilized by neural networks to minimize the cost function, involving finding the gradient of the cost function and taking the negative of the gradient to determine the direction in which the function decreases the fastest, thereby facilitating the model's improvement over time.", "The goal of the neural network is to minimize the cost function by adjusting the weights and biases of each neuron, aiming to detect patterns without memorization and improve accuracy over time. The goal of the neural network is to minimize the cost function by adjusting the weights and biases of each neuron, aiming to detect patterns without memorization and improve accuracy over time, thus enhancing the model's performance."]}, {'end': 8776.737, 'start': 8081.014, 'title': 'Back propagation and gradient descent', 'summary': 'Explains the concept of back propagation and its connection to minimizing the cost function through gradient descent, involving the chain rule of calculus to determine the impact of weight changes on error, with emphasis on multivariable calculus and derivative calculation.', 'duration': 695.723, 'highlights': ['Back propagation involves the chain rule of calculus to determine the impact of weight changes on error, aiming to minimize the cost function. Back propagation uses the chain rule of calculus to calculate the impact of weight changes on error, ultimately aiming to minimize the cost function.', 'The concept of back propagation is connected to minimizing the cost function through gradient descent. Back propagation is linked to minimizing the cost function through the process of gradient descent.', 'The chapter emphasizes the use of multivariable calculus and derivatives in determining the impact of weight changes on error. The chapter underscores the utilization of multivariable calculus and derivatives to establish the influence of weight changes on error.']}, {'end': 9094.215, 'start': 8777.877, 'title': 'Convolutional neural networks', 'summary': 'Discusses the role of convolutional layers in cnns for image recognition, explaining how filters are used to detect patterns by performing dot products, generating matrix outputs that help identify specific features within images.', 'duration': 316.338, 'highlights': ['Convolutional layers add filters to detect patterns in images, contributing to the effectiveness of CNNs in image recognition. Convolutional layers use filters to detect patterns in images, enhancing the effectiveness of CNNs in image recognition tasks.', 'Explanation of how filters in convolutional layers perform dot products to generate matrix outputs that help identify specific features within images, such as edges and patterns. The explanation of how filters in convolutional layers perform dot products to generate matrix outputs that help identify specific features within images, such as edges and patterns, is detailed.', 'Illustration of the process of applying filters to images and obtaining matrix outputs that represent specific features, such as edges and patterns, through dot products. The process of applying filters to images and obtaining matrix outputs that represent specific features, such as edges and patterns, through dot products, is illustrated.']}], 'duration': 1691.396, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU7402819.jpg', 'highlights': ['A neural network consists of input, hidden, and output layers with connections between neurons.', 'The training process of a neural network involves adjusting weights and biases.', 'Example of training a hand digit recognizer using 784 pixels and 10,000 instances of training data.', 'The cost function of a neural network is commonly the mean squared error function, which measures the difference between predicted output and actual output, allowing the model to learn and improve by minimizing this cost.', 'Gradient descent is utilized by neural networks to minimize the cost function, involving finding the gradient of the cost function and taking the negative of the gradient to determine the direction in which the function decreases the fastest.', 'The goal of the neural network is to minimize the cost function by adjusting the weights and biases of each neuron, aiming to detect patterns without memorization and improve accuracy over time.', 'Back propagation involves the chain rule of calculus to determine the impact of weight changes on error, aiming to minimize the cost function.', 'Convolutional layers add filters to detect patterns in images, contributing to the effectiveness of CNNs in image recognition.', 'Explanation of how filters in convolutional layers perform dot products to generate matrix outputs that help identify specific features within images, such as edges and patterns.']}, {'end': 10460.307, 'segs': [{'end': 9158.434, 'src': 'embed', 'start': 9094.215, 'weight': 1, 'content': [{'end': 9104.542, 'text': 'I will keep doing that for the entire matrix and then come up with a new matrix that now is able to detect patterns.', 'start': 9094.215, 'duration': 10.327}, {'end': 9118.931, 'text': "We're going to be doing a handwriting digit recognition AI using sklearn and Python.", 'start': 9110.226, 'duration': 8.705}, {'end': 9123.225, 'text': "So if you don't know Python, make sure you go check out our course.", 'start': 9120.444, 'duration': 2.781}, {'end': 9126.066, 'text': "It's available on the dlacademy.com.", 'start': 9123.565, 'duration': 2.501}, {'end': 9128.206, 'text': "Otherwise, let's get started.", 'start': 9126.686, 'duration': 1.52}, {'end': 9132.688, 'text': "All right, so for this project, we're going to be using the Google Colab.", 'start': 9128.927, 'duration': 3.761}, {'end': 9136.229, 'text': 'So just go on the internet, type in Google Colab like this.', 'start': 9132.708, 'duration': 3.521}, {'end': 9138.77, 'text': "I believe it's 1L.", 'start': 9137.629, 'duration': 1.141}, {'end': 9140.85, 'text': 'So just click on it.', 'start': 9139.05, 'duration': 1.8}, {'end': 9147.092, 'text': 'And then make sure you create a new Python 3 notebook.', 'start': 9142.051, 'duration': 5.041}, {'end': 9149.373, 'text': 'All right? So it opens this.', 'start': 9147.412, 'duration': 1.961}, {'end': 9152.432, 'text': 'little interface for you.', 'start': 9151.112, 'duration': 1.32}, {'end': 9158.434, 'text': 'And basically what this does is allows you to write Python code on the web.', 'start': 9153.113, 'duration': 5.321}], 'summary': 'Using sklearn and python to create a handwriting digit recognition ai, taught on dlacademy.com.', 'duration': 64.219, 'max_score': 9094.215, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU9094215.jpg'}, {'end': 9789.467, 'src': 'embed', 'start': 9754.586, 'weight': 4, 'content': [{'end': 9757.326, 'text': 'so this worked perfectly.', 'start': 9754.586, 'duration': 2.74}, {'end': 9766.129, 'text': 'now that another data is now in the correct format, we can start by, we can proceed to create our model.', 'start': 9757.326, 'duration': 8.803}, {'end': 9772.931, 'text': "so let's create a variable clf for classifier and then equals mlp classifier.", 'start': 9766.129, 'duration': 6.802}, {'end': 9782.101, 'text': 'so for the solver we will use adam, and adam is very, very efficient when we have a large number of data.', 'start': 9772.931, 'duration': 9.17}, {'end': 9784.563, 'text': "So let's use it right now.", 'start': 9782.682, 'duration': 1.881}, {'end': 9789.467, 'text': 'And for the activation function, we will use ReLU.', 'start': 9785.464, 'duration': 4.003}], 'summary': 'Data ready, model created with adam solver, relu activation.', 'duration': 34.881, 'max_score': 9754.586, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU9754586.jpg'}, {'end': 10076.881, 'src': 'embed', 'start': 10044.609, 'weight': 0, 'content': [{'end': 10048.372, 'text': 'All right, because here we need to add parentheses.', 'start': 10044.609, 'duration': 3.763}, {'end': 10049.032, 'text': "It's a function.", 'start': 10048.432, 'duration': 0.6}, {'end': 10052.335, 'text': "So let's run this and run this again.", 'start': 10050.113, 'duration': 2.222}, {'end': 10057.511, 'text': 'And as you can see, we get 0.97, which is very good accuracy.', 'start': 10053.109, 'duration': 4.402}, {'end': 10060.813, 'text': 'So this means our model performs really well.', 'start': 10057.551, 'duration': 3.262}, {'end': 10069.077, 'text': "Now let's just do something a little bit more fun and let's open GIMP, which is a graphic design software.", 'start': 10061.533, 'duration': 7.544}, {'end': 10076.881, 'text': "All right, so if you don't know where to find it, just go on Google like this and type in GIMP, G-I-M-P.", 'start': 10069.097, 'duration': 7.784}], 'summary': 'Model achieves 0.97 accuracy, demonstrating strong performance. gimp, a graphic design software, is also mentioned.', 'duration': 32.272, 'max_score': 10044.609, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU10044609.jpg'}, {'end': 10453.044, 'src': 'embed', 'start': 10420.382, 'weight': 2, 'content': [{'end': 10425.404, 'text': 'our neural network just recognized that this here was a five.', 'start': 10420.382, 'duration': 5.022}, {'end': 10430.065, 'text': 'And so everything seems to work pretty fine.', 'start': 10426.204, 'duration': 3.861}, {'end': 10442.009, 'text': 'And the amazing part about this is that we were able to take this neural network and use it on data, which was unrelated to the data sets.', 'start': 10430.765, 'duration': 11.244}, {'end': 10453.044, 'text': 'So this was just an example of how you could build your own handwritten digits recognition model using sklearn.', 'start': 10442.269, 'duration': 10.775}], 'summary': 'Neural network recognized a five, worked on unrelated data, showcasing handwritten digits recognition model using sklearn.', 'duration': 32.662, 'max_score': 10420.382, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU10420382.jpg'}], 'start': 9094.215, 'title': 'Handwritten digit recognition with mlp classifier', 'summary': 'Covers using python in google colab to create a handwriting digit recognition ai project, preparing data and modules for a neural network model, and building a model with an mlp classifier achieving 0.97 accuracy.', 'chapters': [{'end': 9204.94, 'start': 9094.215, 'title': 'Handwriting recognition using python', 'summary': 'Discusses using google colab for a handwriting digit recognition ai project using sklearn and python, demonstrating how to set up a new python 3 notebook and run code on the web with google colab.', 'duration': 110.725, 'highlights': ['Google Colab allows you to write Python code on the web without needing Python installed on your computer, making it accessible to a wider audience.', 'Creating a new block of code in Google Colab is as simple as clicking on the plus code button, allowing for easy code organization and execution.', 'The project involves creating a new matrix capable of detecting patterns for a handwriting digit recognition AI using sklearn and Python, showcasing the practical application of the discussed concepts.']}, {'end': 9754.586, 'start': 9206.274, 'title': 'Neural network model: preparing data and modules', 'summary': 'Covers the installation of necessary modules, importing and organizing dataset, and data preprocessing for a neural network model, with key details such as the module installation, dataset variables creation, data reshaping, and scaling.', 'duration': 548.312, 'highlights': ['The module installation process includes installing pillow, mnist, numpy, and sklearn using pip, with successful completion. Module installation: pillow, mnist, numpy, and sklearn; Successful installation confirmation.', 'Creation of training variables X_train and y_train from mnist dataset, followed by successful confirmation of creation. Creation of training variables X_train and y_train; Successful creation confirmation.', 'Data reshaping for X_train and X_test to 60,000 samples of 28x28 pixels and 10,000 samples of 784 pixels respectively, with successful reshaping verification. Data reshaping: X_train to 60,000 samples of 28x28 pixels; X_test to 10,000 samples of 784 pixels; Successful reshaping verification.', 'Scaling down the data sets by dividing X_train and X_test by 256 and converting them to numpy arrays. Data scaling: Dividing X_train and X_test by 256; Converting to numpy arrays.']}, {'end': 10042.107, 'start': 9754.586, 'title': 'Building model with mlp classifier', 'summary': "Discusses the process of creating and training a model using an mlp classifier with specific parameters such as solver 'adam', activation function 'relu', and hidden layers of 64 by 64, achieving successful training and evaluating model accuracy through confusion matrix operations.", 'duration': 287.521, 'highlights': ["Successfully trained the model using MLP classifier with solver 'adam' and ReLU activation function, with two hidden layers of 64 by 64 The model was trained using MLP classifier with specific parameters such as solver 'adam', and ReLU activation function, along with two hidden layers of 64 by 64.", "Evaluated model accuracy through confusion matrix operations The accuracy of the model was evaluated using confusion matrix operations, which involved performing matrix operations on the diagonal sum and the sum of all elements to determine the model's accuracy.", "Discussed optimizing the model by tweaking parameters if the initial configuration does not yield desired results It was mentioned that the model's performance can be optimized by tweaking the parameters if the initial configuration does not yield satisfactory results."]}, {'end': 10460.307, 'start': 10044.609, 'title': 'Handwritten digit recognition', 'summary': 'Demonstrates building a handwritten digit recognition model using python, achieving 0.97 accuracy, and successfully recognizing a hand-drawn digit using an unrelated image, showcasing the practical application of the model.', 'duration': 415.698, 'highlights': ['The model achieved an accuracy of 0.97, indicating its strong performance in recognizing handwritten digits.', 'The demonstration of using the model to successfully recognize a hand-drawn digit from an unrelated image showcases its practical application.', 'The process of modifying the image data to match the format of the MNIST dataset, including inverting the pixel values and converting them to bytes, demonstrates the necessary preprocessing steps for inputting images into the model.', "The utilization of the neural network to recognize the hand-drawn digit, resulting in a successful recognition of the digit 'five', serves as a practical validation of the model's effectiveness.", 'The tutorial concludes by emphasizing the capability of building a handwritten digits recognition model using sklearn, demonstrating the practical applicability of the model beyond the standard dataset.']}], 'duration': 1366.092, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/pqNCD_5r0IU/pics/pqNCD_5r0IU9094215.jpg', 'highlights': ['The model achieved an accuracy of 0.97, indicating its strong performance in recognizing handwritten digits.', 'Google Colab allows you to write Python code on the web without needing Python installed on your computer, making it accessible to a wider audience.', 'The demonstration of using the model to successfully recognize a hand-drawn digit from an unrelated image showcases its practical application.', 'The project involves creating a new matrix capable of detecting patterns for a handwriting digit recognition AI using sklearn and Python, showcasing the practical application of the discussed concepts.', "Successfully trained the model using MLP classifier with solver 'adam' and ReLU activation function, with two hidden layers of 64 by 64 The model was trained using MLP classifier with specific parameters such as solver 'adam', and ReLU activation function, along with two hidden layers of 64 by 64."]}], 'highlights': ['scikit-learn is a free software for machine learning, providing a bunch of libraries programmed for Python, making it one of the best machine learning libraries out there.', 'The scikit-learn website describes it as a simple and efficient tool for predictive data analysis, emphasizing its significance in the field of machine learning.', 'The installation process of scikit-learn is demonstrated using pip install, showcasing its ease of use and accessibility for predictive data analysis.', 'The model achieved a pretty good accuracy of 0.93, indicating a strong performance.', 'The model achieved an accuracy of 0.97, indicating its strong performance in recognizing handwritten digits.', 'The process of classification involves finding similar features associated with a label, and any other feature resembling this feature will be classified with the same label, with an example of two features and three labels provided.', 'The chapter explains the concept of k-means clustering and its use in grouping data points into clusters based on centroids.', 'Neural networks aim to find patterns and adjust weights during training, illustrated by the concept of inputs, outputs, weights, and activation functions.', 'The training process of a neural network involves adjusting weights and biases.', 'The cost function of a neural network is commonly the mean squared error function, which measures the difference between predicted output and actual output, allowing the model to learn and improve by minimizing this cost.']}