| 1. Introduction | |||
| 1. What Does the Course Cover.mp4 | 54.4 MB | ||
| 1. What Does the Course Cover.srt | 3.16 KB | ||
| 2. How to Succeed in This Course.html | 2.22 KB | ||
| 3. Project Files and Resources.html | 2.06 KB | ||
| 10. Unsupervised Learning Clustering | |||
| 1. Clustering.mp4 | 125.68 MB | ||
| 1. Clustering.srt | 20.68 KB | ||
| 2. k_Means Clustering.mp4 | 57.71 MB | ||
| 2. k_Means Clustering.srt | 10.81 KB | ||
| 11. Deep Learning | |||
| 1. Estimating Simple Function with Neural Networks.mp4 | 143.85 MB | ||
| 1. Estimating Simple Function with Neural Networks.srt | 26.4 KB | ||
| 2. Neural Network Architecture.mp4 | 22.37 MB | ||
| 2. Neural Network Architecture.srt | 7.93 KB | ||
| 3. Motivational Example - Project MNIST.mp4 | 144.96 MB | ||
| 3. Motivational Example - Project MNIST.srt | 25.83 KB | ||
| 4. Binary Classification Problem.mp4 | 72.11 MB | ||
| 4. Binary Classification Problem.srt | 12.2 KB | ||
| 5. Natural Language Processing - Binary Classification.mp4 | 76.05 MB | ||
| 5. Natural Language Processing - Binary Classification.srt | 12.78 KB | ||
| 12. Appendix A1 Foundations of Deep Learning | |||
| 1. Introduction to Neural Networks.mp4 | 13.74 MB | ||
| 1. Introduction to Neural Networks.srt | 2.72 KB | ||
| 10. Gradient Based Optimization.mp4 | 54.96 MB | ||
| 10. Gradient Based Optimization.srt | 13.76 KB | ||
| 11. Getting Started with Neural Network and Deep Learning Libraries.mp4 | 18.67 MB | ||
| 11. Getting Started with Neural Network and Deep Learning Libraries.srt | 5.76 KB | ||
| 12. Categories of Machine Learning.mp4 | 37.47 MB | ||
| 12. Categories of Machine Learning.srt | 12.11 KB | ||
| 13. Over and Under Fitting.mp4 | 70.06 MB | ||
| 13. Over and Under Fitting.srt | 18.2 KB | ||
| 14. Machine Learning Workflow.mp4 | 27.44 MB | ||
| 14. Machine Learning Workflow.srt | 5.7 KB | ||
| 2. Differences between Classical Programming and Machine Learning.mp4 | 20.85 MB | ||
| 2. Differences between Classical Programming and Machine Learning.srt | 5.05 KB | ||
| 3. Learning Representations.mp4 | 77.24 MB | ||
| 3. Learning Representations.srt | 12.58 KB | ||
| 4. What is Deep Learning.mp4 | 155.61 MB | ||
| 4. What is Deep Learning.srt | 26.23 KB | ||
| 5. Learning Neural Networks.mp4 | 40.61 MB | ||
| 5. Learning Neural Networks.srt | 12.7 KB | ||
| 6. Why Now.mp4 | 9.06 MB | ||
| 6. Why Now.srt | 3.37 KB | ||
| 7. Building Block Introduction.mp4 | 14.16 MB | ||
| 7. Building Block Introduction.srt | 5.63 KB | ||
| 8. Tensors.mp4 | 16.88 MB | ||
| 8. Tensors.srt | 4.68 KB | ||
| 9. Tensor Operations.mp4 | 88.79 MB | ||
| 9. Tensor Operations.srt | 21 KB | ||
| 13. Computer Vision and Convolutional Neural Network (CNN) | |||
| 1. Outline.mp4 | 63.65 MB | ||
| 1. Outline.srt | 4.59 KB | ||
| 10. Training Your CNN 1.mp4 | 124.88 MB | ||
| 10. Training Your CNN 1.srt | 16.7 KB | ||
| 11. Training Your CNN 2.mp4 | 128.54 MB | ||
| 11. Training Your CNN 2.srt | 23.82 KB | ||
| 12. Loading Previously Trained Model.mp4 | 11.2 MB | ||
| 12. Loading Previously Trained Model.srt | 1.86 KB | ||
| 13. Model Performance Comparison.mp4 | 79.75 MB | ||
| 13. Model Performance Comparison.srt | 11.57 KB | ||
| 14. Data Augmentation.mp4 | 28.48 MB | ||
| 14. Data Augmentation.srt | 3.62 KB | ||
| 15. Transfer Learning.mp4 | 97 MB | ||
| 15. Transfer Learning.srt | 12.98 KB | ||
| 16. Feature Extraction.mp4 | 111.14 MB | ||
| 16. Feature Extraction.srt | 13.78 KB | ||
| 17. State of the Art Tools.mp4 | 35.41 MB | ||
| 17. State of the Art Tools.srt | 6.72 KB | ||
| 2. Neural Network Revision.mp4 | 43.81 MB | ||
| 2. Neural Network Revision.srt | 10 KB | ||
| 3. Motivational Example.mp4 | 66.21 MB | ||
| 3. Motivational Example.srt | 9.45 KB | ||
| 4. Visualizing CNN.mp4 | 141.94 MB | ||
| 4. Visualizing CNN.srt | 17.43 KB | ||
| 5. Understanding CNN.mp4 | 30.03 MB | ||
| 5. Understanding CNN.srt | 7.36 KB | ||
| 6. Layer - Input.mp4 | 29.13 MB | ||
| 6. Layer - Input.srt | 6.86 KB | ||
| 7. Layer - Filter.mp4 | 84.39 MB | ||
| 7. Layer - Filter.srt | 20.85 KB | ||
| 8. Activation Function.mp4 | 32.32 MB | ||
| 8. Activation Function.srt | 7.84 KB | ||
| 9. Pooling, Flatten, Dense.mp4 | 88.13 MB | ||
| 9. Pooling, Flatten, Dense.srt | 13.9 KB | ||
| Download Paid Udemy Courses For Free.url | 116 B | ||
| GetFreeCourses.Co.url | 116 B | ||
| How you can help GetFreeCourses.Co.txt | 182 B | ||
| 2. Getting Started with Anaconda | |||
| 1. Installing Applications and Creating Environment.mp4 | 38.42 MB | ||
| 1. Installing Applications and Creating Environment.srt | 6.69 KB | ||
| 2. Hello World.mp4 | 51.22 MB | ||
| 2. Hello World.srt | 14 KB | ||
| 3. Iris Project 1 Working with Error Messages.mp4 | 89.84 MB | ||
| 3. Iris Project 1 Working with Error Messages.srt | 16.05 KB | ||
| 4. Iris Project 2 Reading CSV Data into Memory.mp4 | 64.56 MB | ||
| 4. Iris Project 2 Reading CSV Data into Memory.srt | 10.79 KB | ||
| 5. Iris Project 3 Loading data from Seaborn.mp4 | 55.87 MB | ||
| 5. Iris Project 3 Loading data from Seaborn.srt | 10.76 KB | ||
| 6. Iris Project 4 Visualization.mp4 | 93.49 MB | ||
| 6. Iris Project 4 Visualization.srt | 12.38 KB | ||
| 3. Regression | |||
| 1. Scikit-Learn.mp4 | 48.45 MB | ||
| 1. Scikit-Learn.srt | 10.98 KB | ||
| 10. Multiple Regression 2.mp4 | 91.15 MB | ||
| 10. Multiple Regression 2.srt | 15.38 KB | ||
| 11. Regularized Regression.mp4 | 44.35 MB | ||
| 11. Regularized Regression.srt | 8.45 KB | ||
| 12. Polynomial Regression.mp4 | 110.78 MB | ||
| 12. Polynomial Regression.srt | 22.08 KB | ||
| 13. Dealing with Non-linear Relationships.mp4 | 62.69 MB | ||
| 13. Dealing with Non-linear Relationships.srt | 11.03 KB | ||
| 14. Feature Importance.mp4 | 36.25 MB | ||
| 14. Feature Importance.srt | 5.67 KB | ||
| 15. Data Preprocessing.mp4 | 135.55 MB | ||
| 15. Data Preprocessing.srt | 28.16 KB | ||
| 16. Variance-Bias Trade Off.mp4 | 68.7 MB | ||
| 16. Variance-Bias Trade Off.srt | 14.55 KB | ||
| 17. Learning Curve.mp4 | 56.37 MB | ||
| 17. Learning Curve.srt | 10.83 KB | ||
| 18. Cross Validation.mp4 | 48.04 MB | ||
| 18. Cross Validation.srt | 10.22 KB | ||
| 19. CV Illustration.mp4 | 127.23 MB | ||
| 19. CV Illustration.srt | 21.27 KB | ||
| 2. EDA.mp4 | 151.67 MB | ||
| 2. EDA.srt | 24.41 KB | ||
| 3. Correlation Analysis and Feature Selection.mp4 | 22.58 MB | ||
| 3. Correlation Analysis and Feature Selection.srt | 10.67 KB | ||
| 3.1 0305.zip | 2.13 MB | ||
| 4. Correlation Analysis and Feature Selection.mp4 | 105.19 MB | ||
| 4. Correlation Analysis and Feature Selection.srt | 15.22 KB | ||
| 5. Linear Regression with Scikit-Learn.mp4 | 76.98 MB | ||
| 5. Linear Regression with Scikit-Learn.srt | 16.04 KB | ||
| 6. Five Steps Machine Learning Process.mp4 | 77.27 MB | ||
| 6. Five Steps Machine Learning Process.srt | 10.01 KB | ||
| 7. Robust Regression.mp4 | 119.06 MB | ||
| 7. Robust Regression.srt | 21.8 KB | ||
| 8. Evaluate Regression Model Performance.mp4 | 99.66 MB | ||
| 8. Evaluate Regression Model Performance.srt | 19.18 KB | ||
| 9. Multiple Regression 1.mp4 | 125.51 MB | ||
| 9. Multiple Regression 1.srt | 24.28 KB | ||
| 4. Classification | |||
| 1. Logistic Regression.mp4 | 119.59 MB | ||
| 1. Logistic Regression.srt | 25.37 KB | ||
| 10. Precision Recall Tradeoff.mp4 | 102.01 MB | ||
| 10. Precision Recall Tradeoff.srt | 22.26 KB | ||
| 11. Altering the Precision Recall Tradeoff.mp4 | 20.93 MB | ||
| 11. Altering the Precision Recall Tradeoff.srt | 3.69 KB | ||
| 12. ROC.mp4 | 52.22 MB | ||
| 12. ROC.srt | 8.24 KB | ||
| 2. Introduction to Classification.mp4 | 42.12 MB | ||
| 2. Introduction to Classification.srt | 6.02 KB | ||
| 3. Understanding MNIST.mp4 | 108.98 MB | ||
| 3. Understanding MNIST.srt | 18.3 KB | ||
| 4. SGD.mp4 | 57.3 MB | ||
| 4. SGD.srt | 11.5 KB | ||
| 5. Performance Measure and Stratified k-Fold.mp4 | 51.54 MB | ||
| 5. Performance Measure and Stratified k-Fold.srt | 8.69 KB | ||
| 6. Confusion Matrix.mp4 | 54.71 MB | ||
| 6. Confusion Matrix.srt | 11.7 KB | ||
| 7. Precision.mp4 | 23.58 MB | ||
| 7. Precision.srt | 4.35 KB | ||
| 8. Recall.mp4 | 19.64 MB | ||
| 8. Recall.srt | 3.93 KB | ||
| 9. f1.mp4 | 12.11 MB | ||
| 9. f1.srt | 2.37 KB | ||
| 5. Support Vector Machine (SVM) | |||
| 1. Support Vector Machine (SVM) Concepts.mp4 | 37.87 MB | ||
| 1. Support Vector Machine (SVM) Concepts.srt | 8.63 KB | ||
| 2. Linear SVM Classification.mp4 | 80.94 MB | ||
| 2. Linear SVM Classification.srt | 13.07 KB | ||
| 3. Polynomial Kernel.mp4 | 34.96 MB | ||
| 3. Polynomial Kernel.srt | 5.98 KB | ||
| 4. Radial Basis Function.mp4 | 70.13 MB | ||
| 4. Radial Basis Function.srt | 9.41 KB | ||
| 5. Support Vector Regression.mp4 | 59.68 MB | ||
| 5. Support Vector Regression.srt | 9.8 KB | ||
| 6. Tree | |||
| 1. Introduction to Decision Tree.mp4 | 43.86 MB | ||
| 1. Introduction to Decision Tree.srt | 8.65 KB | ||
| 2. Training and Visualizing a Decision Tree.mp4 | 51.4 MB | ||
| 2. Training and Visualizing a Decision Tree.srt | 7.46 KB | ||
| 3. Visualizing Boundary.mp4 | 54.72 MB | ||
| 3. Visualizing Boundary.srt | 9.61 KB | ||
| 4. Tree Regression, Regularization and Over Fitting.mp4 | 40.05 MB | ||
| 4. Tree Regression, Regularization and Over Fitting.srt | 5.59 KB | ||
| 5. End to End Modeling.mp4 | 35.62 MB | ||
| 5. End to End Modeling.srt | 5.55 KB | ||
| 6. Project HR.mp4 | 177.83 MB | ||
| 6. Project HR.srt | 30.75 KB | ||
| 7. Project HR with Google Colab.mp4 | 66.57 MB | ||
| 7. Project HR with Google Colab.srt | 12.68 KB | ||
| 7. Ensemble Machine Learning | |||
| 1. Ensemble Learning Methods Introduction.mp4 | 37.17 MB | ||
| 1. Ensemble Learning Methods Introduction.srt | 5.85 KB | ||
| 10. Ensemble of ensembles Part 2.mp4 | 37.85 MB | ||
| 10. Ensemble of ensembles Part 2.srt | 6.14 KB | ||
| 2. Bagging.mp4 | 165.44 MB | ||
| 2. Bagging.srt | 22.8 KB | ||
| 3. Random Forests and Extra-Trees.mp4 | 80.28 MB | ||
| 3. Random Forests and Extra-Trees.srt | 11.54 KB | ||
| 4. AdaBoost.mp4 | 49.85 MB | ||
| 4. AdaBoost.srt | 8.22 KB | ||
| 5. Gradient Boosting Machine.mp4 | 21.96 MB | ||
| 5. Gradient Boosting Machine.srt | 3.67 KB | ||
| 6. XGBoost Installation.mp4 | 22.26 MB | ||
| 6. XGBoost Installation.srt | 3.02 KB | ||
| 7. XGBoost.mp4 | 35.05 MB | ||
| 7. XGBoost.srt | 5.4 KB | ||
| 8. Project HR - Human Resources Analytics.mp4 | 59.21 MB | ||
| 8. Project HR - Human Resources Analytics.srt | 10.44 KB | ||
| 9. Ensemble of Ensembles Part 1.mp4 | 46.4 MB | ||
| 9. Ensemble of Ensembles Part 1.srt | 7.79 KB | ||
| Download Paid Udemy Courses For Free.url | 116 B | ||
| GetFreeCourses.Co.url | 116 B | ||
| How you can help GetFreeCourses.Co.txt | 182 B | ||
| 8. k-Nearest Neighbours (kNN) | |||
| 1. kNN Introduction.mp4 | 62.95 MB | ||
| 1. kNN Introduction.srt | 12 KB | ||
| 2. Project Cancer Detection.mp4 | 75.73 MB | ||
| 2. Project Cancer Detection.srt | 10.52 KB | ||
| 3. Addition Materials.html | 335 B | ||
| 4. Project Cancer Detection Part 1.mp4 | 49.4 MB | ||
| 4. Project Cancer Detection Part 1.srt | 24.53 KB | ||
| 4.1 0805.zip | 40.76 KB | ||
| 9. Unsupervised Learning Dimensionality Reduction | |||
| 1. Dimensionality Reduction Concept.mp4 | 31.37 MB | ||
| 1. Dimensionality Reduction Concept.srt | 5.66 KB | ||
| 2. PCA Introduction.mp4 | 49.03 MB | ||
| 2. PCA Introduction.srt | 8.82 KB | ||
| 3. Project Wine.mp4 | 47.87 MB | ||
| 3. Project Wine.srt | 7.52 KB | ||
| 4. Kernel PCA.mp4 | 36.6 MB | ||
| 4. Kernel PCA.srt | 6.56 KB | ||
| 5. Kernel PCA Demo.mp4 | 21.44 MB | ||
| 5. Kernel PCA Demo.srt | 3.91 KB | ||
| 6. LDA vs PCA.mp4 | 34.15 MB | ||
| 6. LDA vs PCA.srt | 6.43 KB | ||
| 7. Project Abalone.mp4 | 30.74 MB | ||
| 7. Project Abalone.srt | 4.75 KB | ||
| Download Paid Udemy Courses For Free.url | 116 B | ||
| GetFreeCourses.Co.url | 116 B | ||
| How you can help GetFreeCourses.Co.txt | 182 B | ||
| ▲ 230 total files | |||
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