[FreeAllCourse.Com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science

1. 1. Welcome to the course!/6.1 Machine_Learning_A-Z_New.zip.zip 233.92 MB
2. 36. Kernel PCA/3. Kernel PCA in R.mp4 57.92 MB
3. 1. Welcome to the course!/7. Updates on Udemy Reviews.mp4 54.18 MB
4. 39. XGBoost/5. THANK YOU bonus video.mp4 53.50 MB
5. 12. Logistic Regression/13. Logistic Regression in R - Step 5.mp4 52.92 MB
6. 35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 52.52 MB
7. 17. Decision Tree Classification/4. Decision Tree Classification in R.mp4 52.41 MB
8. 18. Random Forest Classification/4. Random Forest Classification in R.mp4 50.57 MB
9. 31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 49.25 MB
10. 39. XGBoost/4. XGBoost in R.mp4 48.41 MB
11. 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4 48.33 MB
12. 18. Random Forest Classification/3. Random Forest Classification in Python.mp4 48.29 MB
13. 32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4 47.98 MB
14. 7. Support Vector Regression (SVR)/2. SVR Intuition.mp4 47.72 MB
15. 7. Support Vector Regression (SVR)/3. SVR in Python.mp4 47.30 MB
16. 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 46.51 MB
17. 8. Decision Tree Regression/4. Decision Tree Regression in R.mp4 45.43 MB
18. 16. Naive Bayes/1. Bayes Theorem.mp4 44.96 MB
19. 24. Apriori/5. Apriori in R - Step 3.mp4 44.89 MB
20. 38. Model Selection/3. k-Fold Cross Validation in R.mp4 44.68 MB
21. 6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp4 44.34 MB
22. 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 44.17 MB
23. 6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 44.02 MB
24. 24. Apriori/3. Apriori in R - Step 1.mp4 43.90 MB
25. 32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 43.78 MB
26. 12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 43.57 MB
27. 15. Kernel SVM/6. Kernel SVM in Python.mp4 42.62 MB
28. 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 42.36 MB
29. 29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4 42.17 MB
30. 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 42.10 MB
31. 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4 41.91 MB
32. 2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp4 41.77 MB
33. 15. Kernel SVM/7. Kernel SVM in R.mp4 41.42 MB
34. 29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4 41.34 MB
35. 9. Random Forest Regression/4. Random Forest Regression in R.mp4 41.31 MB
36. 32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 41.21 MB
37. 21. K-Means Clustering/5. K-Means Clustering in Python.mp4 40.73 MB
38. 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 40.68 MB
39. 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 40.52 MB
40. 29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp4 40.43 MB
41. 9. Random Forest Regression/3. Random Forest Regression in Python.mp4 40.42 MB
42. 2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp4 39.98 MB
43. 31. Artificial Neural Networks/22. ANN in R - Step 1.mp4 39.49 MB
44. 38. Model Selection/4. Grid Search in Python - Step 1.mp4 39.13 MB
45. 24. Apriori/6. Apriori in Python - Step 1.mp4 38.90 MB
46. 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 38.27 MB
47. 16. Naive Bayes/7. Naive Bayes in R.mp4 38.21 MB
48. 28. Thompson Sampling/1. Thompson Sampling Intuition.mp4 38.16 MB
49. 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4 37.62 MB
50. 38. Model Selection/6. Grid Search in R.mp4 36.40 MB