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Machine Learning with Python from Scratch
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Machine Learning with Python from Scratch (Size: 5.52 GB) (Files: 360)
Machine Learning with Python from Scratch
3. Machine Learning
20. L20-Linear Regression.mp4
1. L1-Introduction to Machine Learning.mp4
1. L1-Introduction to Machine Learning.vtt
1.1 Machine learning wiki page.html
2. L2-Overfitting and Underfitting.mp4
2. L2-Overfitting and Underfitting.vtt
2.1 Underfitting vs. Overfitting.html
3. L3-KFold Cross Validation.mp4
3. L3-KFold Cross Validation.vtt
3.1 Cross-validation (statistics).html
3.1 Source code - Machine Learning - L3-KFold Cross Validation.html
3.2 Cross-validation (statistics).html
3.2 Source code - Machine Learning - L3-KFold Cross Validation.html
4. L4-Classification Metrics.mp4
4. L4-Classification Metrics.vtt
4.1 Confusion matrix wiki.html
5. L5-Logistic Regression.mp4
5. L5-Logistic Regression.vtt
5.1 Sklearn Linear Model.html
5.1 Source code - Machine Learning - L5-Logistic Regression.html
5.2 Sklearn Linear Model.html
5.2 Softmax function wiki.html
5.3 Softmax function wiki.html
5.3 Source code - Machine Learning - L5-Logistic Regression.html
6. L6-Plotting Decision Boundaries.mp4
6. L6-Plotting Decision Boundaries.vtt
6.1 matplotlib.axes.Axes.pcolormesh.html
6.1 numpy.meshgrid.html
6.2 matplotlib.axes.Axes.pcolormesh.html
6.2 numpy.meshgrid.html
6.3 Source code - Machine Learning - L6-Plotting Decision Boundaries.html
7. L7-Naive Bayes Classifier.mp4
7. L7-Naive Bayes Classifier.vtt
7.1 Source code - Machine Learning - L7-Naive Bayes Classifier.html
7.2 Naive Bayes methods.html
8. L8-Suppor Vector Machines for Classification.mp4
8. L8-Suppor Vector Machines for Classification.vtt
8.1 Source code - Machine Learning - L8-Suppor Vector Machines for Classification.html
8.2 Support vector machines (SVMs).html
9. L9-Decision Trees.mp4
9. L9-Decision Trees.vtt
9.1 Decision Trees (DTs).html
9.1 Source code - Machine Learning - L9-Decision Trees.html
9.2 Decision Trees (DTs).html
9.2 Source code - Machine Learning - L9-Decision Trees.html
10. L10-Random Forest.mp4
10. L10-Random Forest.vtt
10.1 Source code - Machine Learning - L10-Random Forest.html
10.2 sklearn.ensemble.RandomForestClassifier.html
11. L11-KNN.mp4
11. L11-KNN.vtt
11.1 KNeighborsClassifier.html
11.1 Source code - Machine Learning - L11-KNN.html
11.2 KNeighborsClassifier.html
11.2 Source code - Machine Learning - L11-KNN.html
12. L12-GridSearchCV.mp4
12. L12-GridSearchCV.vtt
12.1 Source code - Machine Learning - L12-GridSearchCV.html
12.2 Tuning the hyper-parameters of an estimator.html
13. L13-K-Means.mp4
13. L13-K-Means.vtt
13.1 Simple Plot.html
13.2 KMeans.html
13.2 Source code - Machine Learning - L13-K-Means.html
13.3 KMeans.html
13.3 Source code - Machine Learning - L13-K-Means.html
14. L14-Principal Component Analysis(PCA).mp4
14. L14-Principal Component Analysis(PCA).vtt
14.1 Source code - Machine Learning - L14-Principal Component Analysis(PCA).html
14.2 PCA.html
15. L15-Linear Discriminant Analysis(LDA).mp4
15. L15-Linear Discriminant Analysis(LDA).vtt
15.1 sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html
15.1 Source code - Machine Learning - L15-Linear Discriminant Analysis(LDA).html
15.2 sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html
15.2 Source code - Machine Learning - L15-Linear Discriminant Analysis(LDA).html
16. L16-Kernel Principal Component Analysis(KPCA).mp4
16. L16-Kernel Principal Component Analysis(KPCA).vtt
16.1 sklearn.decomposition.KernelPCA.html
16.2 Source code - Machine Learning - L16-Kernel Principal Component Analysis(KPCA).html
17. L17-Ensemble Methods(Bagging).mp4
17. L17-Ensemble Methods(Bagging).vtt
17.1 sklearn.ensemble.VotingClassifier.html
17.2 Source code - Machine Learning - L17-Ensemble Methods(Bagging).html
18. L18-AdaBoost.mp4
18. L18-AdaBoost.vtt
18.1 sklearn.ensemble.AdaBoostClassifier.html
18.1 Source code - Machine Learning - L18-AdaBoost.html
18.2 sklearn.ensemble.AdaBoostClassifier.html
18.2 Source code - Machine Learning - L18-AdaBoost.html
19. L19-Regression Model and Metrics.mp4
19. L19-Regression Model and Metrics.vtt
19.1 Linear regression wiki.html
20. L20-Linear Regression.vtt
20.1 sklearn.linear_model.LinearRegression.html
20.1 Source code - Machine Learning - L20-Linear Regression.html
20.2 sklearn.linear_model.LinearRegression.html
20.2 Source code - Machine Learning - L20-Linear Regression.html
21. L21-Regularization with Lasso, Ridge and ElasticNet.mp4
21. L21-Regularization with Lasso, Ridge and ElasticNet.vtt
21.1 Source code - Machine Learning - L21-Regularization with Lasso, Ridge and ElasticNet.html
21.2 sklearn.linear_model.Lasso.html
21.2 sklearn.linear_model.Ridge.html
21.3 sklearn.linear_model.ElasticNet.html
21.3 sklearn.linear_model.Ridge.html
21.4 sklearn.linear_model.ElasticNet.html
21.4 sklearn.linear_model.Lasso.html
22. L22-Polynomial Regression.mp4
22. L22-Polynomial Regression.vtt
22.1 sklearn.preprocessing.PolynomialFeatures.html
22.1 Source code - Machine Learning - L22-Polynomial Regression.html
22.2 sklearn.preprocessing.PolynomialFeatures.html
22.2 Source code - Machine Learning - L22-Polynomial Regression.html
23. L23-SVM, KNN and Random Forest for Regression.mp4
23. L23-SVM, KNN and Random Forest for Regression.vtt
23.1 sklearn.ensemble.RandomForestRegressor.html
23.1 Source code - Machine Learning - L23-SVM, KNN and Random Forest for Regression.html
23.2 sklearn.svm.SVR.html
23.3 sklearn.ensemble.RandomForestRegressor.html
23.3 Source code - Machine Learning - L23-SVM, KNN and Random Forest for Regression.html
23.4 sklearn.neighbors.KNeighborsRegressor.html
24. L24-RANSAC Regression.mp4
24. L24-RANSAC Regression.vtt
24.1 sklearn.linear_model.RANSACRegressor.html
ReadMe.txt
Visit Coursedrive.org.url
ReadMe.txt
Visit Coursedrive.org.url
1. Environment Setup
1. Source Code.html
2. L1-Anaconda.mp4
2. L1-Anaconda.vtt
2.1 Anaconda main web page.html
2.1 Python main web page.html
2.2 Anaconda main web page.html
2.2 Python main web page.html
3. L2-Jupyter Notebook.mp4
3. L2-Jupyter Notebook.vtt
3.1 Source code - Environment Setup - L2-Jupyter Notebook.html
3.2 Jupyter Notebook main web page.html
Must Read.txt
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2. Data Analysis
1. L1-Introduction.mp4
1. L1-Introduction.vtt
1.1 Matplotlib.html
1.1 Seaborn.html
1.2 Numpy.html
1.2 Pandas.html
1.3 Matplotlib.html
1.3 Numpy.html
1.4 Pandas.html
1.4 Seaborn.html
2. L2-Numpy Array Concept and Math Operations.mp4
2. L2-Numpy Array Concept and Math Operations.vtt
2.1 Source code - Data Analysis - L2-Numpy Array Concept and Math Operations.html
3. L3-Numpy Indexing, Slicing and Iterating.mp4
3. L3-Numpy Indexing, Slicing and Iterating.vtt
3.1 Source code - Data Analysis - L3-Numpy Indexing, Slicing and Iterating.html
4. L4-Numpy Shape Manipulation.mp4
4. L4-Numpy Shape Manipulation.vtt
4.1 Source code - Data Analysis - L4-Numpy Shape Manipulation.html
5. L5-Numpy Linear Algebra.mp4
5. L5-Numpy Linear Algebra.vtt
5.1 Quickstart tutorial.html
5.1 Source code - Data Analysis - L5-Numpy Linear Algebra.html
5.2 Quickstart tutorial.html
5.2 Source code - Data Analysis - L5-Numpy Linear Algebra.html
5.3 NumPy.html
6. L6-Pandas Data structures and properties.mp4
6. L6-Pandas Data structures and properties.vtt
6.1 Essential Basic Functionality.html
6.1 Intro to Data Structures.html
6.2 Essential Basic Functionality.html
6.2 Source code - Data Analysis - L6-Pandas Data structures and properties.html
6.3 Intro to Data Structures.html
6.3 Source code - Data Analysis - L6-Pandas Data structures and properties.html
7. L7-Pandas Operations.mp4
7. L7-Pandas Operations.vtt
7.1 Working with Text Data.html
7.2 pandas.get_dummies.html
7.3 Source code - Data Analysis - L7-Pandas Operations.html
8. L8-Pandas Applying Functions.mp4
8. L8-Pandas Applying Functions.vtt
8.1 pandas.DataFrame.apply.html
8.2 Source code - Data Analysis - L8-Pandas Applying Functions.html
9. L9-Pandas Importing and Exporting data.mp4
9. L9-Pandas Importing and Exporting data.vtt
9.1 pandas.read_table.html
9.2 IO Tools (Text, CSV, HDF5, ...).html
9.2 pandas.DataFrame.to_csv.html
9.3 IO Tools (Text, CSV, HDF5, ...).html
9.3 pandas.DataFrame.to_csv.html
9.4 pandas.read_csv.html
9.4 Source code - Data Analysis - L9-Pandas Importing and Exporting data.html
9.5 pandas.read_excel.html
9.6 pandas.read_csv.html
9.6 Source code - Data Analysis - L9-Pandas Importing and Exporting data.html
10. L10-Pandas Merge-Join-Concat-Group by.mp4
10. L10-Pandas Merge-Join-Concat-Group by.vtt
10.1 Brief primer on merge methods (relational algebra).html
10.1 Pandas Data Frame (pandas.DataFrame.join).html
10.2 Brief primer on merge methods (relational algebra).html
10.2 Source code - Data Analysis - L10-Pandas Merge-Join-Concat-Group by.html
10.3 Pandas.concat.html
10.4 Pandas Data Frame (pandas.DataFrame.join).html
10.4 Source code - Data Analysis - L10-Pandas Merge-Join-Concat-Group by.html
11. L11-Pandas Statistics with Pandas.mp4
11. L11-Pandas Statistics with Pandas.vtt
11.1 Source code - Data Analysis - L11-Pandas Statistics with Pandas.html
11.2 Pandas Data Frame (pandas.DataFrame.describe).html
12. L12-Time Series with Pandas.mp4
12. L12-Time Series with Pandas.vtt
12.1 Source code - Data Analysis - L12-Time Series with Pandas.html
12.1 Time Series Date functionality.html
12.2 pandas.to_datetime.html
12.2 Time Series Date functionality.html
12.3 pandas.to_datetime.html
12.3 Source code - Data Analysis - L12-Time Series with Pandas.html
13. L13-Matplotlib basics.mp4
13. L13-Matplotlib basics.vtt
13.1 Source code - Data Analysis - L13-Matplotlib basics.html
14. L14-Matplotlib Subplots and Axes.mp4
14. L14-Matplotlib Subplots and Axes.vtt
14.1 Source code - Data Analysis - L14-Matplotlib Subplots and Axes.html
14.2 Sharing axis limits and views.html
15. L15-Matplotlib Object Oriented Method.mp4
15. L15-Matplotlib Object Oriented Method.vtt
15.1 Matplotlib Usage FAQ.html
15.1 Source code - Data Analysis - L15-Matplotlib Object Oriented Method.html
15.2 Matplotlib Usage FAQ.html
15.2 Source code - Data Analysis - L15-Matplotlib Object Oriented Method.html
16. L16-Matplotlib Color Maps.mp4
16. L16-Matplotlib Color Maps.vtt
16.1 Matplotlib Colormaps Reference.html
16.1 Source code - Data Analysis - L16-Matplotlib Color Maps.html
16.2 Matplotlib Colormaps Reference.html
16.2 Source code - Data Analysis - L16-Matplotlib Color Maps.html
17. L17-Matplotlib Statistical Graphs part1.mp4
17. L17-Matplotlib Statistical Graphs part1.vtt
17.1 Matplotlib Statistics Examples.html
17.1 Source code - Data Analysis - L17-Matplotlib Statistical Graphs part1.html
17.2 Matplotlib Statistics Examples.html
17.2 Source code - Data Analysis - L17-Matplotlib Statistical Graphs part1.html
18. L18-Matplotlib Statistical Graphs part2.mp4
18. L18-Matplotlib Statistical Graphs part2.vtt
18.1 Matplotlib mplot3d API.html
18.1 Matplotlib Statistics Examples.html
18.2 Matplotlib mplot3d API.html
18.2 Matplotlib Statistics Examples.html
18.3 matplotlib.axes.Axes.violinplot.html
18.4 Source code - Data Analysis - L18-Matplotlib Statistical Graphs part2.html
19. L19-Seaborn Basics.mp4
19. L19-Seaborn Basics.vtt
19.1 Installing and getting started.html
19.1 Source code - Data Analysis - L19-Seaborn Basics.html
19.2 Installing and getting started.html
19.2 seaborn statistical data visualization.html
19.3 seaborn statistical data visualization.html
19.3 Source code - Data Analysis - L19-Seaborn Basics.html
20. L20-Seaborn Color Palette.mp4
20. L20-Seaborn Color Palette.vtt
20.1 Choosing color palettes.html
20.2 Source code - Data Analysis - L20-Seaborn Color Palette.html
21. L21-Seaborn Categorical Data.mp4
21. L21-Seaborn Categorical Data.vtt
21.1 Plotting with Categorical Data.html
21.1 Source code - Data Analysis - L21-Seaborn Categorical Data.html
21.2 Plotting with Categorical Data.html
21.2 Source code - Data Analysis - L21-Seaborn Categorical Data.html
22. L22-Seaborn Numerical Data.mp4
22. L22-Seaborn Numerical Data.vtt
22.1 Source code - Data Analysis - L22-Seaborn Numerical Data.html
22.2 seaborn statistical data visualization.html
22.2 seaborn.jointplot.html
22.3 seaborn statistical data visualization.html
22.3 seaborn.jointplot.html
4. Neural Networks
1. L1-Neural Networks Concepts-Part 1.mp4
1. L1-Neural Networks Concepts-Part 1.vtt
1.1 Delta rule.html
1.2 Backpropagation.html
1.2 Perceptron.html
1.3 Chain rule.html
1.3 Gradient descent.html
1.4 Artificial neural network wiki.html
1.5 Gradient descent.html
1.5 Multilayer perceptron.html
1.6 Chain rule.html
1.6 Multilayer perceptron.html
1.7 Backpropagation.html
1.7 Perceptron.html
2. L2-Neural Networks Concepts-Part 2.mp4
2. L2-Neural Networks Concepts-Part 2.vtt
2.1 Backpropagation.html
2.1 Multilayer perceptron.html
2.2 Chain rule.html
2.3 Gradient descent.html
2.4 Backpropagation.html
2.4 Multilayer perceptron.html
3. L3-Loss Functions.mp4
3. L3-Loss Functions.vtt
3.1 Cross entropy.html
3.1 sklearn.metrics.log_loss.html
3.2 Mean squared error.html
3.3 Source code - Neural Networks - L3-Loss Functions.html
3.4 Cross entropy.html
3.4 sklearn.metrics.log_loss.html
4. L4-Activation Functions.mp4
4. L4-Activation Functions.vtt
4.1 Activation function.html
4.2 Source code - Neural Networks - L4-Activation Functions.html
4.3 numpy.tanh.html
5. L5-Optimization of ANNs.mp4
5. L5-Optimization of ANNs.vtt
5.1 Neural network models (supervised).html
6. L6-Constructing an ANN with Python-part1.mp4
6. L6-Constructing an ANN with Python-part1.vtt
6.1 Source code - Neural Networks - L6-Constructing an ANN with Python-part1.html
7. L7-Constructing an ANN with Python-part2.mp4
7. L7-Constructing an ANN with Python-part2.vtt
7.1 Source code - Neural Networks - L7-Constructing an ANN with Python-part2.html
8. L8-Constructing an ANN with Python-part3.mp4
8. L8-Constructing an ANN with Python-part3.vtt
8.1 Source code - Neural Networks - L8-Constructing an ANN with Python-part3.html
9. L9-Perceptron with Scikit Learn.mp4
9. L9-Perceptron with Scikit Learn.vtt
9.1 sklearn.linear_model.Perceptron.html
9.1 Source code - Neural Networks - L9-Perceptron with Scikit Learn.html
9.2 sklearn.linear_model.Perceptron.html
9.2 Source code - Neural Networks - L9-Perceptron with Scikit Learn.html
10. L10-Multilayer Perceptron with Scikit Learn.mp4
10. L10-Multilayer Perceptron with Scikit Learn.vtt
10.1 Source code - Neural Networks - L10-Multilayer Perceptron with Scikit Learn.html
10.2 Neural network models (supervised).html
10.2 sklearn.neural_network.MLPClassifier.html
10.3 Neural network models (supervised).html
10.3 sklearn.neural_network.MLPClassifier.html
5. Applications
1. L1-Datasets.mp4
1. L1-Datasets.vtt
1.1 Data Science and Machine Learning website.html
1.1 UC Irvine Machine Learning Repository.html
1.2 Data Science and Machine Learning website.html
1.2 UC Irvine Machine Learning Repository.html
2. L2-ANN for Regression Part 1.mp4
2. L2-ANN for Regression Part 1.vtt
2.1 Source code - Applications - L2-ANN for Regression Part 1.html
3. L3-ANN for Regression Part 2.mp4
3. L3-ANN for Regression Part 2.vtt
3.1 Source code - Applications - L3-ANN for Regression Part 2.html
4. L4-Recognizing Handwritten Digits.mp4
4. L4-Recognizing Handwritten Digits.vtt
4.1 Recognizing hand-written digits.html
4.1 Source code - Applications - L4-Recognizing Handwritten Digits.html
4.2 Recognizing hand-written digits.html
4.2 Source code - Applications - L4-Recognizing Handwritten Digits.html
6. Bonus Material
1. Bonus - Please Watch!.mp4
1. Bonus - Please Watch!.vtt
2. Check out Our Other 36 Udemy Courses!.html
ReadMe.txt
Visit Coursedrive.org.url
Visit Coursedrive.org.url
Course Downloaded from coursedrive.org.txt
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