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文档列表

9. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4 195.4 MB
3. Naive Bayes and Bayes Classifiers/1. Bayes Classifier Intuition (Continuous).mp4 84.1 MB
9. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp4 82.1 MB
2. K-Nearest Neighbor/6. KNN for the Donut Problem.srt 74.6 MB
3. Naive Bayes and Bayes Classifiers/2. Bayes Classifier Intuition (Discrete).mp4 52.5 MB
9. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn.mp4 46.1 MB
2. K-Nearest Neighbor/7. Effect of K.srt 44.6 MB
9. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp4 40.9 MB
9. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp4 39.7 MB
9. Appendix FAQ/12. What order should I take your courses in (part 2).srt 39.5 MB
9. Appendix FAQ/12. What order should I take your courses in (part 2).mp4 39.5 MB
2. K-Nearest Neighbor/7. Effect of K.mp4 37.5 MB
2. K-Nearest Neighbor/3. KNN in Code with MNIST.srt 34.0 MB
4. Decision Trees/6. Decision Tree in Code.mp4 31.8 MB
9. Appendix FAQ/11. What order should I take your courses in (part 1).mp4 30.7 MB
9. Appendix FAQ/5. How to Code by Yourself (part 1).mp4 25.7 MB
4. Decision Trees/1. Decision Tree Intuition.mp4 21.4 MB
2. K-Nearest Neighbor/3. KNN in Code with MNIST.mp4 18.8 MB
2. K-Nearest Neighbor/1. K-Nearest Neighbor Intuition.mp4 18.4 MB
2. K-Nearest Neighbor/8. KNN Exercise.mp4 17.7 MB
6. Practical Machine Learning/5. Sci-Kit Learn.mp4 16.6 MB
3. Naive Bayes and Bayes Classifiers/3. Naive Bayes.mp4 16.5 MB
9. Appendix FAQ/6. How to Code by Yourself (part 2).mp4 15.5 MB
3. Naive Bayes and Bayes Classifiers/5. Naive Bayes in Code with MNIST.mp4 15.1 MB
4. Decision Trees/4. Maximizing Information Gain.mp4 14.6 MB
5. Perceptrons/2. Perceptron in Code.mp4 14.4 MB
9. Appendix FAQ/7. How to Succeed in this Course (Long Version).mp4 13.6 MB
5. Perceptrons/1. Perceptron Concepts.mp4 12.8 MB
7. Building a Machine Learning Web Service/2. Building a Machine Learning Web Service Code.mp4 12.4 MB
6. Practical Machine Learning/6. Regression with Sci-Kit Learn is Easy.mp4 11.3 MB
3. Naive Bayes and Bayes Classifiers/8. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).mp4 10.9 MB
5. Perceptrons/3. Perceptron for MNIST and XOR.mp4 9.2 MB
6. Practical Machine Learning/3. Comparison to Deep Learning.mp4 9.1 MB
2. K-Nearest Neighbor/2. K-Nearest Neighbor Concepts.mp4 9.0 MB
4. Decision Trees/2. Decision Tree Basics.mp4 8.7 MB
9. Appendix FAQ/10. Python 2 vs Python 3.mp4 8.2 MB
2. K-Nearest Neighbor/4. When KNN Can Fail.mp4 8.1 MB
1. Introduction and Review/1. Introduction and Outline.mp4 8.0 MB
6. Practical Machine Learning/1. Hyperparameters and Cross-Validation.mp4 7.8 MB
3. Naive Bayes and Bayes Classifiers/6. Non-Naive Bayes.mp4 7.7 MB
7. Building a Machine Learning Web Service/1. Building a Machine Learning Web Service Concepts.mp4 7.6 MB
6. Practical Machine Learning/2. Feature Extraction and Feature Selection.mp4 7.4 MB
4. Decision Trees/3. Information Entropy.mp4 7.3 MB
4. Decision Trees/5. Choosing the Best Split.mp4 7.0 MB
5. Perceptrons/4. Perceptron Loss Function.mp4 6.6 MB
8. Conclusion/1. What’s Next Support Vector Machines and Ensemble Methods (e.g. Random Forest).mp4 6.6 MB
3. Naive Bayes and Bayes Classifiers/7. Bayes Classifier in Code with MNIST.srt 6.5 MB
1. Introduction and Review/2. Review of Important Concepts.mp4 6.3 MB
3. Naive Bayes and Bayes Classifiers/4. Naive Bayes Handwritten Example.mp4 6.1 MB
6. Practical Machine Learning/4. Multiclass Classification.mp4 5.9 MB
9. Appendix FAQ/1. What is the Appendix.mp4 5.7 MB
2. K-Nearest Neighbor/6. KNN for the Donut Problem.mp4 5.7 MB
3. Naive Bayes and Bayes Classifiers/9. Generative vs Discriminative Models.mp4 5.4 MB
3. Naive Bayes and Bayes Classifiers/7. Bayes Classifier in Code with MNIST.mp4 4.7 MB
2. K-Nearest Neighbor/5. KNN for the XOR Problem.mp4 4.5 MB
1. Introduction and Review/3. Where to get the Code and Data.mp4 4.0 MB
1. Introduction and Review/4. How to Succeed in this Course.mp4 3.5 MB
9. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.srt 32.5 kB
3. Naive Bayes and Bayes Classifiers/1. Bayes Classifier Intuition (Continuous).srt 23.5 kB
9. Appendix FAQ/5. How to Code by Yourself (part 1).srt 23.3 kB
9. Appendix FAQ/3. Windows-Focused Environment Setup 2018.srt 20.6 kB
9. Appendix FAQ/11. What order should I take your courses in (part 1).srt 16.4 kB
9. Appendix FAQ/7. How to Succeed in this Course (Long Version).srt 15.0 kB
9. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn.srt 14.8 kB
9. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.srt 14.5 kB
9. Appendix FAQ/6. How to Code by Yourself (part 2).srt 13.6 kB
3. Naive Bayes and Bayes Classifiers/2. Bayes Classifier Intuition (Discrete).srt 13.2 kB
3. Naive Bayes and Bayes Classifiers/3. Naive Bayes.srt 11.8 kB
4. Decision Trees/6. Decision Tree in Code.srt 11.4 kB
6. Practical Machine Learning/5. Sci-Kit Learn.srt 11.4 kB
4. Decision Trees/4. Maximizing Information Gain.srt 9.9 kB
5. Perceptrons/1. Perceptron Concepts.srt 8.9 kB
9. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.srt 8.1 kB
7. Building a Machine Learning Web Service/2. Building a Machine Learning Web Service Code.srt 7.6 kB
3. Naive Bayes and Bayes Classifiers/8. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).srt 7.6 kB
2. K-Nearest Neighbor/2. K-Nearest Neighbor Concepts.srt 6.4 kB
4. Decision Trees/2. Decision Tree Basics.srt 6.3 kB
9. Appendix FAQ/10. Python 2 vs Python 3.srt 6.2 kB
6. Practical Machine Learning/6. Regression with Sci-Kit Learn is Easy.srt 6.2 kB
6. Practical Machine Learning/3. Comparison to Deep Learning.srt 6.2 kB
1. Introduction and Review/1. Introduction and Outline.srt 5.8 kB
4. Decision Trees/1. Decision Tree Intuition.srt 5.7 kB
2. K-Nearest Neighbor/8. KNN Exercise.srt 5.6 kB
7. Building a Machine Learning Web Service/1. Building a Machine Learning Web Service Concepts.srt 5.5 kB
6. Practical Machine Learning/1. Hyperparameters and Cross-Validation.srt 5.2 kB
5. Perceptrons/4. Perceptron Loss Function.srt 5.0 kB
2. K-Nearest Neighbor/1. K-Nearest Neighbor Intuition.srt 5.0 kB
3. Naive Bayes and Bayes Classifiers/6. Non-Naive Bayes.srt 5.0 kB
6. Practical Machine Learning/2. Feature Extraction and Feature Selection.srt 5.0 kB
4. Decision Trees/5. Choosing the Best Split.srt 4.9 kB
3. Naive Bayes and Bayes Classifiers/5. Naive Bayes in Code with MNIST.srt 4.9 kB
5. Perceptrons/2. Perceptron in Code.srt 4.8 kB
2. K-Nearest Neighbor/4. When KNN Can Fail.srt 4.7 kB
1. Introduction and Review/2. Review of Important Concepts.srt 4.5 kB
4. Decision Trees/3. Information Entropy.srt 4.4 kB
6. Practical Machine Learning/4. Multiclass Classification.srt 4.2 kB
1. Introduction and Review/4. How to Succeed in this Course.srt 4.1 kB
9. Appendix FAQ/1. What is the Appendix.srt 3.8 kB
3. Naive Bayes and Bayes Classifiers/4. Naive Bayes Handwritten Example.srt 3.7 kB
8. Conclusion/1. What’s Next Support Vector Machines and Ensemble Methods (e.g. Random Forest).srt 3.6 kB
3. Naive Bayes and Bayes Classifiers/9. Generative vs Discriminative Models.srt 3.2 kB
1. Introduction and Review/3. Where to get the Code and Data.srt 2.7 kB
5. Perceptrons/3. Perceptron for MNIST and XOR.srt 2.4 kB
2. K-Nearest Neighbor/5. KNN for the XOR Problem.srt 2.2 kB
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[Tutorialsplanet.NET] Udemy - Data Science Supervised Machine Learning in Python

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