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文档列表

6. Tree/6. Project HR.mp4 186.5 MB
7. Ensemble Machine Learning/2. Bagging.mp4 173.5 MB
12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.mp4 163.2 MB
3. Regression/2. EDA.mp4 159.0 MB
11. Deep Learning/3. Motivational Example - Project MNIST.mp4 152.0 MB
11. Deep Learning/1. Estimating Simple Function with Neural Networks.mp4 150.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.mp4 148.8 MB
3. Regression/15. Data Preprocessing.mp4 142.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.mp4 134.8 MB
3. Regression/19. CV Illustration.mp4 133.4 MB
10. Unsupervised Learning Clustering/1. Clustering.mp4 131.8 MB
3. Regression/9. Multiple Regression 1.mp4 131.6 MB
13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.mp4 130.9 MB
4. Classification/1. Logistic Regression.mp4 125.4 MB
3. Regression/7. Robust Regression.mp4 124.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.mp4 116.5 MB
3. Regression/12. Polynomial Regression.mp4 116.2 MB
4. Classification/3. Understanding MNIST.mp4 114.3 MB
3. Regression/4. Correlation Analysis and Feature Selection.mp4 110.3 MB
4. Classification/10. Precision Recall Tradeoff.mp4 107.0 MB
3. Regression/8. Evaluate Regression Model Performance.mp4 104.5 MB
13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.mp4 101.7 MB
2. Getting Started with Anaconda/6. Iris Project 4 Visualization.mp4 98.0 MB
3. Regression/10. Multiple Regression 2.mp4 95.6 MB
2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.mp4 94.2 MB
12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.mp4 93.1 MB
13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.mp4 92.4 MB
13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.mp4 88.5 MB
5. Support Vector Machine (SVM)/2. Linear SVM Classification.mp4 84.9 MB
7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.mp4 84.2 MB
13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.mp4 83.6 MB
3. Regression/6. Five Steps Machine Learning Process.mp4 81.0 MB
12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.mp4 81.0 MB
3. Regression/5. Linear Regression with Scikit-Learn.mp4 80.7 MB
11. Deep Learning/5. Natural Language Processing - Binary Classification.mp4 79.7 MB
8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.mp4 79.4 MB
11. Deep Learning/4. Binary Classification Problem.mp4 75.6 MB
5. Support Vector Machine (SVM)/4. Radial Basis Function.mp4 73.5 MB
12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.mp4 73.5 MB
3. Regression/16. Variance-Bias Trade Off.mp4 72.0 MB
6. Tree/7. Project HR with Google Colab.mp4 69.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.mp4 69.4 MB
2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.mp4 67.7 MB
13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.mp4 66.7 MB
8. k-Nearest Neighbours (kNN)/1. kNN Introduction.mp4 66.0 MB
3. Regression/13. Dealing with Non-linear Relationships.mp4 65.7 MB
5. Support Vector Machine (SVM)/5. Support Vector Regression.mp4 62.6 MB
7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.mp4 62.1 MB
10. Unsupervised Learning Clustering/2. k_Means Clustering.mp4 60.5 MB
4. Classification/4. SGD.mp4 60.1 MB
3. Regression/17. Learning Curve.mp4 59.1 MB
2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.mp4 58.6 MB
12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.mp4 57.6 MB
6. Tree/3. Visualizing Boundary.mp4 57.4 MB
4. Classification/6. Confusion Matrix.mp4 57.4 MB
1. Introduction/1. What Does the Course Cover.mp4 57.0 MB
4. Classification/12. ROC.mp4 54.8 MB
4. Classification/5. Performance Measure and Stratified k-Fold.mp4 54.0 MB
6. Tree/2. Training and Visualizing a Decision Tree.mp4 53.9 MB
2. Getting Started with Anaconda/2. Hello World.mp4 53.7 MB
7. Ensemble Machine Learning/4. AdaBoost.mp4 52.3 MB
8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.mp4 51.8 MB
9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.mp4 51.4 MB
3. Regression/1. Scikit-Learn.mp4 50.8 MB
3. Regression/18. Cross Validation.mp4 50.4 MB
9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.mp4 50.2 MB
7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.mp4 48.7 MB
3. Regression/11. Regularized Regression.mp4 46.5 MB
6. Tree/1. Introduction to Decision Tree.mp4 46.0 MB
13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.mp4 45.9 MB
4. Classification/2. Introduction to Classification.mp4 44.2 MB
12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.mp4 42.6 MB
6. Tree/4. Tree Regression, Regularization and Over Fitting.mp4 42.0 MB
2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.mp4 40.3 MB
5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.mp4 39.7 MB
7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.mp4 39.7 MB
12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.mp4 39.3 MB
7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.mp4 39.0 MB
9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.mp4 38.4 MB
3. Regression/14. Feature Importance.mp4 38.0 MB
6. Tree/5. End to End Modeling.mp4 37.3 MB
13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.mp4 37.1 MB
7. Ensemble Machine Learning/7. XGBoost.mp4 36.8 MB
5. Support Vector Machine (SVM)/3. Polynomial Kernel.mp4 36.7 MB
9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.mp4 35.8 MB
13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.mp4 33.9 MB
9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.mp4 32.9 MB
9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.mp4 32.2 MB
13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.mp4 31.5 MB
13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.mp4 30.5 MB
13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.mp4 29.9 MB
12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.mp4 28.8 MB
4. Classification/7. Precision.mp4 24.7 MB
3. Regression/3. Correlation Analysis and Feature Selection.mp4 23.7 MB
11. Deep Learning/2. Neural Network Architecture.mp4 23.5 MB
7. Ensemble Machine Learning/6. XGBoost Installation.mp4 23.3 MB
7. Ensemble Machine Learning/5. Gradient Boosting Machine.mp4 23.0 MB
9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.mp4 22.5 MB
4. Classification/11. Altering the Precision Recall Tradeoff.mp4 21.9 MB
12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.mp4 21.9 MB
4. Classification/8. Recall.mp4 20.6 MB
12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.mp4 19.6 MB
12. Appendix A1 Foundations of Deep Learning/8. Tensors.mp4 17.7 MB
12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.mp4 14.8 MB
12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.mp4 14.4 MB
4. Classification/9. f1.mp4 12.7 MB
13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.mp4 11.7 MB
12. Appendix A1 Foundations of Deep Learning/6. Why Now.mp4 9.5 MB
3. Regression/3.1 0305.zip 2.2 MB
8. k-Nearest Neighbours (kNN)/4.1 0805.zip 41.7 kB
6. Tree/6. Project HR.srt 31.5 kB
3. Regression/15. Data Preprocessing.srt 28.8 kB
11. Deep Learning/1. Estimating Simple Function with Neural Networks.srt 27.0 kB
12. Appendix A1 Foundations of Deep Learning/4. What is Deep Learning.srt 26.9 kB
11. Deep Learning/3. Motivational Example - Project MNIST.srt 26.4 kB
4. Classification/1. Logistic Regression.srt 26.0 kB
8. k-Nearest Neighbours (kNN)/4. Project Cancer Detection Part 1.srt 25.1 kB
3. Regression/2. EDA.srt 25.0 kB
3. Regression/9. Multiple Regression 1.srt 24.9 kB
13. Computer Vision and Convolutional Neural Network (CNN)/11. Training Your CNN 2.srt 24.4 kB
7. Ensemble Machine Learning/2. Bagging.srt 23.4 kB
4. Classification/10. Precision Recall Tradeoff.srt 22.8 kB
3. Regression/12. Polynomial Regression.srt 22.6 kB
3. Regression/7. Robust Regression.srt 22.3 kB
3. Regression/19. CV Illustration.srt 21.8 kB
12. Appendix A1 Foundations of Deep Learning/9. Tensor Operations.srt 21.5 kB
13. Computer Vision and Convolutional Neural Network (CNN)/7. Layer - Filter.srt 21.4 kB
10. Unsupervised Learning Clustering/1. Clustering.srt 21.2 kB
3. Regression/8. Evaluate Regression Model Performance.srt 19.6 kB
4. Classification/3. Understanding MNIST.srt 18.7 kB
12. Appendix A1 Foundations of Deep Learning/13. Over and Under Fitting.srt 18.6 kB
13. Computer Vision and Convolutional Neural Network (CNN)/4. Visualizing CNN.srt 17.9 kB
13. Computer Vision and Convolutional Neural Network (CNN)/10. Training Your CNN 1.srt 17.1 kB
2. Getting Started with Anaconda/3. Iris Project 1 Working with Error Messages.srt 16.4 kB
3. Regression/5. Linear Regression with Scikit-Learn.srt 16.4 kB
3. Regression/10. Multiple Regression 2.srt 15.8 kB
3. Regression/4. Correlation Analysis and Feature Selection.srt 15.6 kB
3. Regression/16. Variance-Bias Trade Off.srt 14.9 kB
2. Getting Started with Anaconda/2. Hello World.srt 14.3 kB
13. Computer Vision and Convolutional Neural Network (CNN)/9. Pooling, Flatten, Dense.srt 14.2 kB
13. Computer Vision and Convolutional Neural Network (CNN)/16. Feature Extraction.srt 14.1 kB
12. Appendix A1 Foundations of Deep Learning/10. Gradient Based Optimization.srt 14.1 kB
5. Support Vector Machine (SVM)/2. Linear SVM Classification.srt 13.4 kB
13. Computer Vision and Convolutional Neural Network (CNN)/15. Transfer Learning.srt 13.3 kB
11. Deep Learning/5. Natural Language Processing - Binary Classification.srt 13.1 kB
12. Appendix A1 Foundations of Deep Learning/5. Learning Neural Networks.srt 13.0 kB
6. Tree/7. Project HR with Google Colab.srt 13.0 kB
12. Appendix A1 Foundations of Deep Learning/3. Learning Representations.srt 12.9 kB
2. Getting Started with Anaconda/6. Iris Project 4 Visualization.srt 12.7 kB
11. Deep Learning/4. Binary Classification Problem.srt 12.5 kB
12. Appendix A1 Foundations of Deep Learning/12. Categories of Machine Learning.srt 12.4 kB
8. k-Nearest Neighbours (kNN)/1. kNN Introduction.srt 12.3 kB
4. Classification/6. Confusion Matrix.srt 12.0 kB
13. Computer Vision and Convolutional Neural Network (CNN)/13. Model Performance Comparison.srt 11.8 kB
7. Ensemble Machine Learning/3. Random Forests and Extra-Trees.srt 11.8 kB
4. Classification/4. SGD.srt 11.8 kB
3. Regression/13. Dealing with Non-linear Relationships.srt 11.3 kB
3. Regression/1. Scikit-Learn.srt 11.2 kB
3. Regression/17. Learning Curve.srt 11.1 kB
10. Unsupervised Learning Clustering/2. k_Means Clustering.srt 11.1 kB
2. Getting Started with Anaconda/4. Iris Project 2 Reading CSV Data into Memory.srt 11.1 kB
2. Getting Started with Anaconda/5. Iris Project 3 Loading data from Seaborn.srt 11.0 kB
3. Regression/3. Correlation Analysis and Feature Selection.srt 10.9 kB
8. k-Nearest Neighbours (kNN)/2. Project Cancer Detection.srt 10.8 kB
7. Ensemble Machine Learning/8. Project HR - Human Resources Analytics.srt 10.7 kB
3. Regression/18. Cross Validation.srt 10.5 kB
3. Regression/6. Five Steps Machine Learning Process.srt 10.2 kB
13. Computer Vision and Convolutional Neural Network (CNN)/2. Neural Network Revision.srt 10.2 kB
5. Support Vector Machine (SVM)/5. Support Vector Regression.srt 10.0 kB
6. Tree/3. Visualizing Boundary.srt 9.8 kB
13. Computer Vision and Convolutional Neural Network (CNN)/3. Motivational Example.srt 9.7 kB
5. Support Vector Machine (SVM)/4. Radial Basis Function.srt 9.6 kB
9. Unsupervised Learning Dimensionality Reduction/2. PCA Introduction.srt 9.0 kB
4. Classification/5. Performance Measure and Stratified k-Fold.srt 8.9 kB
6. Tree/1. Introduction to Decision Tree.srt 8.9 kB
5. Support Vector Machine (SVM)/1. Support Vector Machine (SVM) Concepts.srt 8.8 kB
3. Regression/11. Regularized Regression.srt 8.7 kB
4. Classification/12. ROC.srt 8.4 kB
7. Ensemble Machine Learning/4. AdaBoost.srt 8.4 kB
11. Deep Learning/2. Neural Network Architecture.srt 8.1 kB
13. Computer Vision and Convolutional Neural Network (CNN)/8. Activation Function.srt 8.0 kB
7. Ensemble Machine Learning/9. Ensemble of Ensembles Part 1.srt 8.0 kB
9. Unsupervised Learning Dimensionality Reduction/3. Project Wine.srt 7.7 kB
6. Tree/2. Training and Visualizing a Decision Tree.srt 7.6 kB
13. Computer Vision and Convolutional Neural Network (CNN)/5. Understanding CNN.srt 7.5 kB
13. Computer Vision and Convolutional Neural Network (CNN)/6. Layer - Input.srt 7.0 kB
13. Computer Vision and Convolutional Neural Network (CNN)/17. State of the Art Tools.srt 6.9 kB
2. Getting Started with Anaconda/1. Installing Applications and Creating Environment.srt 6.9 kB
9. Unsupervised Learning Dimensionality Reduction/4. Kernel PCA.srt 6.7 kB
9. Unsupervised Learning Dimensionality Reduction/6. LDA vs PCA.srt 6.6 kB
7. Ensemble Machine Learning/10. Ensemble of ensembles Part 2.srt 6.3 kB
4. Classification/2. Introduction to Classification.srt 6.2 kB
5. Support Vector Machine (SVM)/3. Polynomial Kernel.srt 6.1 kB
7. Ensemble Machine Learning/1. Ensemble Learning Methods Introduction.srt 6.0 kB
12. Appendix A1 Foundations of Deep Learning/11. Getting Started with Neural Network and Deep Learning Libraries.srt 5.9 kB
12. Appendix A1 Foundations of Deep Learning/14. Machine Learning Workflow.srt 5.8 kB
3. Regression/14. Feature Importance.srt 5.8 kB
9. Unsupervised Learning Dimensionality Reduction/1. Dimensionality Reduction Concept.srt 5.8 kB
12. Appendix A1 Foundations of Deep Learning/7. Building Block Introduction.srt 5.8 kB
6. Tree/4. Tree Regression, Regularization and Over Fitting.srt 5.7 kB
6. Tree/5. End to End Modeling.srt 5.7 kB
7. Ensemble Machine Learning/7. XGBoost.srt 5.5 kB
12. Appendix A1 Foundations of Deep Learning/2. Differences between Classical Programming and Machine Learning.srt 5.2 kB
9. Unsupervised Learning Dimensionality Reduction/7. Project Abalone.srt 4.9 kB
12. Appendix A1 Foundations of Deep Learning/8. Tensors.srt 4.8 kB
13. Computer Vision and Convolutional Neural Network (CNN)/1. Outline.srt 4.7 kB
4. Classification/7. Precision.srt 4.5 kB
4. Classification/8. Recall.srt 4.0 kB
9. Unsupervised Learning Dimensionality Reduction/5. Kernel PCA Demo.srt 4.0 kB
4. Classification/11. Altering the Precision Recall Tradeoff.srt 3.8 kB
7. Ensemble Machine Learning/5. Gradient Boosting Machine.srt 3.8 kB
13. Computer Vision and Convolutional Neural Network (CNN)/14. Data Augmentation.srt 3.7 kB
12. Appendix A1 Foundations of Deep Learning/6. Why Now.srt 3.5 kB
1. Introduction/1. What Does the Course Cover.srt 3.2 kB
7. Ensemble Machine Learning/6. XGBoost Installation.srt 3.1 kB
12. Appendix A1 Foundations of Deep Learning/1. Introduction to Neural Networks.srt 2.8 kB
4. Classification/9. f1.srt 2.4 kB
1. Introduction/2. How to Succeed in This Course.html 2.3 kB
1. Introduction/3. Project Files and Resources.html 2.1 kB
13. Computer Vision and Convolutional Neural Network (CNN)/12. Loading Previously Trained Model.srt 1.9 kB
8. k-Nearest Neighbours (kNN)/3. Addition Materials.html 335 Bytes
[FreeCourseWorld.Com].url 54 Bytes

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