FreeCourseLab me Udemy - Data Science Supervised Machine Learning in Python

1. 1. Introduction and Review/1. Introduction and Outline.mp47.63MB
2. 1. Introduction and Review/2. Review of Important Concepts.mp46.01MB
3. 1. Introduction and Review/3. Where to get the Code and Data.mp43.86MB
4. 1. Introduction and Review/4. How to Succeed in this Course.mp43.3MB
5. 2. K-Nearest Neighbor/1. K-Nearest Neighbor Intuition.mp417.59MB
6. 2. K-Nearest Neighbor/2. K-Nearest Neighbor Concepts.mp48.6MB
7. 2. K-Nearest Neighbor/3. KNN in Code with MNIST.mp417.96MB
8. 2. K-Nearest Neighbor/4. When KNN Can Fail.mp47.71MB
9. 2. K-Nearest Neighbor/5. KNN for the XOR Problem.mp44.26MB
10. 2. K-Nearest Neighbor/6. KNN for the Donut Problem.mp45.42MB
11. 2. K-Nearest Neighbor/7. Effect of K.mp435.8MB
12. 2. K-Nearest Neighbor/8. KNN Exercise.mp416.87MB
13. 3. Naive Bayes and Bayes Classifiers/1. Bayes Classifier Intuition (Continuous).mp480.16MB
14. 3. Naive Bayes and Bayes Classifiers/2. Bayes Classifier Intuition (Discrete).mp450.09MB
15. 3. Naive Bayes and Bayes Classifiers/3. Naive Bayes.mp415.7MB
16. 3. Naive Bayes and Bayes Classifiers/4. Naive Bayes Handwritten Example.mp45.84MB
17. 3. Naive Bayes and Bayes Classifiers/5. Naive Bayes in Code with MNIST.mp414.43MB
18. 3. Naive Bayes and Bayes Classifiers/6. Non-Naive Bayes.mp47.3MB
19. 3. Naive Bayes and Bayes Classifiers/7. Bayes Classifier in Code with MNIST.mp44.44MB
20. 3. Naive Bayes and Bayes Classifiers/8. Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA).mp410.35MB
21. 3. Naive Bayes and Bayes Classifiers/9. Generative vs Discriminative Models.mp45.12MB
22. 4. Decision Trees/1. Decision Tree Intuition.mp420.37MB
23. 4. Decision Trees/2. Decision Tree Basics.mp48.28MB
24. 4. Decision Trees/3. Information Entropy.mp46.99MB
25. 4. Decision Trees/4. Maximizing Information Gain.mp413.96MB
26. 4. Decision Trees/5. Choosing the Best Split.mp46.72MB
27. 4. Decision Trees/6. Decision Tree in Code.mp430.34MB
28. 5. Perceptrons/1. Perceptron Concepts.mp412.22MB
29. 5. Perceptrons/2. Perceptron in Code.mp413.77MB
30. 5. Perceptrons/3. Perceptron for MNIST and XOR.mp48.75MB
31. 5. Perceptrons/4. Perceptron Loss Function.mp46.29MB
32. 6. Practical Machine Learning/1. Hyperparameters and Cross-Validation.mp47.43MB
33. 6. Practical Machine Learning/2. Feature Extraction and Feature Selection.mp47.1MB
34. 6. Practical Machine Learning/3. Comparison to Deep Learning.mp48.7MB
35. 6. Practical Machine Learning/4. Multiclass Classification.mp45.66MB
36. 6. Practical Machine Learning/5. Sci-Kit Learn.mp415.82MB
37. 6. Practical Machine Learning/6. Regression with Sci-Kit Learn is Easy.mp410.75MB
38. 7. Building a Machine Learning Web Service/1. Building a Machine Learning Web Service Concepts.mp47.24MB
39. 7. Building a Machine Learning Web Service/2. Building a Machine Learning Web Service Code.mp411.87MB
40. 8. Conclusion/1. What’s Next Support Vector Machines and Ensemble Methods (e.g. Random Forest).mp46.27MB
41. 9. Appendix FAQ/1. What is the Appendix.mp45.45MB
42. 9. Appendix FAQ/10. Python 2 vs Python 3.mp47.84MB
43. 9. Appendix FAQ/11. What order should I take your courses in (part 1).mp429.32MB
44. 9. Appendix FAQ/12. What order should I take your courses in (part 2).mp437.63MB
45. 9. Appendix FAQ/2. BONUS Where to get Udemy coupons and FREE deep learning material.mp437.81MB
46. 9. Appendix FAQ/3. Windows-Focused Environment Setup 2018.mp4186.38MB
47. 9. Appendix FAQ/4. How to install Numpy, Scipy, Matplotlib, and Sci-Kit Learn.mp443.92MB
48. 9. Appendix FAQ/5. How to Code by Yourself (part 1).mp424.53MB
49. 9. Appendix FAQ/6. How to Code by Yourself (part 2).mp414.8MB
50. 9. Appendix FAQ/7. How to Succeed in this Course (Long Version).mp413MB
51. 9. Appendix FAQ/8. Is this for Beginners or Experts Academic or Practical Fast or slow-paced.mp438.96MB
52. 9. Appendix FAQ/9. Proof that using Jupyter Notebook is the same as not using it.mp478.25MB