|
Обратите внимание, что наш сайт не размещает какие-либо файлы из списка. Вы не можете скачать
эти файлы или скачать torrent-файл.
|
| [CourseClub.ME].url |
122б |
| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| 1.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 1.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 1.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 1.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 1.1 Machine Learning A-Z (Model Selection).zip |
160.01Кб |
| 1.1 Machine Learning A-Z (Model Selection).zip |
160.01Кб |
| 1. Applications of Machine Learning.mp4 |
9.81Мб |
| 1. Applications of Machine Learning.srt |
5.30Кб |
| 1. Apriori Intuition.mp4 |
35.02Мб |
| 1. Apriori Intuition.srt |
25.91Кб |
| 1. Bayes Theorem.mp4 |
50.44Мб |
| 1. Bayes Theorem.srt |
34.45Кб |
| 1. Dataset + Business Problem Description.mp4 |
12.56Мб |
| 1. Dataset + Business Problem Description.srt |
5.68Кб |
| 1. Decision Tree Classification Intuition.mp4 |
21.63Мб |
| 1. Decision Tree Classification Intuition.srt |
12.87Кб |
| 1. Decision Tree Regression Intuition.mp4 |
25.33Мб |
| 1. Decision Tree Regression Intuition.srt |
17.06Кб |
| 1. Eclat Intuition.mp4 |
10.66Мб |
| 1. Eclat Intuition.srt |
8.10Кб |
| 1. Evaluating Regression Models Performance - Homework's Final Part.mp4 |
28.36Мб |
| 1. Evaluating Regression Models Performance - Homework's Final Part.srt |
12.93Кб |
| 1. False Positives & False Negatives.mp4 |
15.13Мб |
| 1. False Positives & False Negatives.srt |
11.29Кб |
| 1. Kernel SVM Intuition.mp4 |
6.42Мб |
| 1. Kernel SVM Intuition.srt |
4.41Кб |
| 1. K-Means Clustering.html |
125б |
| 1. K-Means Clustering Intuition.mp4 |
29.97Мб |
| 1. K-Means Clustering Intuition.srt |
23.34Кб |
| 1. K-Nearest Neighbor.html |
125б |
| 1. K-Nearest Neighbor Intuition.mp4 |
10.48Мб |
| 1. K-Nearest Neighbor Intuition.srt |
8.04Кб |
| 1. Linear Discriminant Analysis (LDA) Intuition.mp4 |
26.99Мб |
| 1. Linear Discriminant Analysis (LDA) Intuition.srt |
5.11Кб |
| 1. Logistic Regression Intuition.mp4 |
29.18Мб |
| 1. Logistic Regression Intuition.srt |
23.94Кб |
| 1. Make sure you have this Model Selection folder ready.html |
973б |
| 1. Make sure you have this Model Selection folder ready.html |
985б |
| 1. Make sure you have your Machine Learning A-Z folder ready.html |
664б |
| 1. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 1. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 1. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 1. Plan of attack.mp4 |
4.74Мб |
| 1. Plan of attack.mp4 |
5.90Мб |
| 1. Plan of attack.srt |
4.00Кб |
| 1. Plan of attack.srt |
5.24Кб |
| 1. Polynomial Regression Intuition.mp4 |
9.44Мб |
| 1. Polynomial Regression Intuition.srt |
7.82Кб |
| 1. Principal Component Analysis (PCA) Intuition.mp4 |
32.12Мб |
| 1. Principal Component Analysis (PCA) Intuition.srt |
5.05Кб |
| 1. Random Forest Classification Intuition.mp4 |
25.66Мб |
| 1. Random Forest Classification Intuition.srt |
7.05Кб |
| 1. Random Forest Regression Intuition.mp4 |
15.65Мб |
| 1. Random Forest Regression Intuition.srt |
10.22Кб |
| 1. R-Squared Intuition.mp4 |
9.81Мб |
| 1. R-Squared Intuition.srt |
7.17Кб |
| 1. Simple Linear Regression Intuition - Step 1.mp4 |
10.53Мб |
| 1. Simple Linear Regression Intuition - Step 1.srt |
8.30Кб |
| 1. SVR Intuition (Updated!).mp4 |
36.85Мб |
| 1. SVR Intuition (Updated!).srt |
11.58Кб |
| 1. The Multi-Armed Bandit Problem.mp4 |
30.20Мб |
| 1. The Multi-Armed Bandit Problem.srt |
22.27Кб |
| 1. Thompson Sampling Intuition.mp4 |
37.28Мб |
| 1. Thompson Sampling Intuition.srt |
27.53Кб |
| 1. Welcome.html |
608б |
| 1. Welcome to Part 10 - Model Selection & Boosting.html |
899б |
| 1. Welcome to Part 1 - Data Preprocessing.html |
531б |
| 1. Welcome to Part 2 - Regression.html |
875б |
| 1. Welcome to Part 3 - Classification.html |
831б |
| 1. Welcome to Part 4 - Clustering.html |
734б |
| 1. Welcome to Part 5 - Association Rule Learning.html |
425б |
| 1. Welcome to Part 6 - Reinforcement Learning.html |
1.52Кб |
| 1. Welcome to Part 7 - Natural Language Processing.html |
1.69Кб |
| 1. Welcome to Part 8 - Deep Learning.html |
870б |
| 1. Welcome to Part 9 - Dimensionality Reduction.html |
1.26Кб |
| 1. YOUR SPECIAL BONUS.html |
4.73Кб |
| 10.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 10.1 Section 40 - Convolutional Neural Networks (CNN).zip |
224.04Мб |
| 10. Data Preprocessing Template.mp4 |
50.74Мб |
| 10. Data Preprocessing Template.srt |
8.32Кб |
| 10. Hierarchical Clustering in R - Step 2.mp4 |
13.87Мб |
| 10. Hierarchical Clustering in R - Step 2.srt |
8.14Кб |
| 10. Installing R and R Studio (Mac, Linux & Windows).mp4 |
23.22Мб |
| 10. Installing R and R Studio (Mac, Linux & Windows).srt |
9.15Кб |
| 10. K-Means Clustering in R.mp4 |
36.91Мб |
| 10. K-Means Clustering in R.srt |
19.44Кб |
| 10. Logistic Regression in R - Step 1.mp4 |
15.73Мб |
| 10. Logistic Regression in R - Step 1.srt |
8.92Кб |
| 10. Make sure you have your dataset ready.html |
797б |
| 10. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 10. Multiple Linear Regression in Python - Step 2.mp4 |
62.33Мб |
| 10. Multiple Linear Regression in Python - Step 2.srt |
14.86Кб |
| 10. Natural Language Processing in Python - Step 4.mp4 |
60.11Мб |
| 10. Natural Language Processing in Python - Step 4.srt |
16.78Кб |
| 10. Polynomial Regression in R - Step 4.mp4 |
28.52Мб |
| 10. Polynomial Regression in R - Step 4.srt |
15.44Кб |
| 10. Simple Linear Regression in R - Step 2.mp4 |
24.87Мб |
| 10. Simple Linear Regression in R - Step 2.srt |
8.89Кб |
| 10. Thompson Sampling in R - Step 2.mp4 |
9.56Мб |
| 10. Thompson Sampling in R - Step 2.srt |
5.29Кб |
| 10. Upper Confidence Bound in Python - Step 7.mp4 |
43.34Мб |
| 10. Upper Confidence Bound in Python - Step 7.srt |
11.67Кб |
| 11. ANN in Python - Step 1.mp4 |
66.47Мб |
| 11. ANN in Python - Step 1.srt |
17.37Кб |
| 11. BONUS Meet your instructors.html |
1.10Кб |
| 11. CNN in Python - Step 1.mp4 |
70.80Мб |
| 11. CNN in Python - Step 1.srt |
18.29Кб |
| 11. Hierarchical Clustering in R - Step 3.mp4 |
9.96Мб |
| 11. Hierarchical Clustering in R - Step 3.srt |
4.73Кб |
| 11. Logistic Regression in R - Step 2.mp4 |
14.85Мб |
| 11. Logistic Regression in R - Step 2.srt |
4.35Кб |
| 11. Multiple Linear Regression in Python - Step 3.mp4 |
58.21Мб |
| 11. Multiple Linear Regression in Python - Step 3.srt |
16.60Кб |
| 11. Natural Language Processing in Python - Step 5.mp4 |
89.63Мб |
| 11. Natural Language Processing in Python - Step 5.srt |
26.38Кб |
| 11. R Regression Template.mp4 |
31.34Мб |
| 11. R Regression Template.srt |
18.63Кб |
| 11. Simple Linear Regression in R - Step 3.mp4 |
11.43Мб |
| 11. Simple Linear Regression in R - Step 3.srt |
5.52Кб |
| 11. Upper Confidence Bound in R - Step 1.mp4 |
34.01Мб |
| 11. Upper Confidence Bound in R - Step 1.srt |
20.54Кб |
| 12. Check out our free course on ANN for Regression.html |
533б |
| 12. CNN in Python - Step 2.mp4 |
106.88Мб |
| 12. CNN in Python - Step 2.srt |
29.00Кб |
| 12. Hierarchical Clustering in R - Step 4.mp4 |
10.18Мб |
| 12. Hierarchical Clustering in R - Step 4.srt |
3.83Кб |
| 12. Logistic Regression in R - Step 3.mp4 |
27.45Мб |
| 12. Logistic Regression in R - Step 3.srt |
7.42Кб |
| 12. Multiple Linear Regression in Python - Step 4.mp4 |
72.52Мб |
| 12. Multiple Linear Regression in Python - Step 4.srt |
20.01Кб |
| 12. Natural Language Processing in Python - Step 6.mp4 |
52.90Мб |
| 12. Natural Language Processing in Python - Step 6.srt |
15.02Кб |
| 12. Simple Linear Regression in R - Step 4.mp4 |
49.16Мб |
| 12. Simple Linear Regression in R - Step 4.srt |
23.94Кб |
| 12. Some Additional Resources.html |
553б |
| 12. Upper Confidence Bound in R - Step 2.mp4 |
34.10Мб |
| 12. Upper Confidence Bound in R - Step 2.srt |
22.17Кб |
| 13. ANN in Python - Step 2.mp4 |
111.03Мб |
| 13. ANN in Python - Step 2.srt |
31.00Кб |
| 13. CNN in Python - Step 3.mp4 |
118.59Мб |
| 13. CNN in Python - Step 3.srt |
28.91Кб |
| 13. FAQBot!.html |
2.98Кб |
| 13. Hierarchical Clustering in R - Step 5.mp4 |
13.68Мб |
| 13. Hierarchical Clustering in R - Step 5.srt |
4.03Кб |
| 13. Logistic Regression in R - Step 4.mp4 |
11.73Мб |
| 13. Logistic Regression in R - Step 4.srt |
3.98Кб |
| 13. Multiple Linear Regression in Python - Backward Elimination.html |
3.48Кб |
| 13. Natural Language Processing in Python - BONUS.html |
1.10Кб |
| 13. Simple Linear Regression.html |
125б |
| 13. Upper Confidence Bound in R - Step 3.mp4 |
57.84Мб |
| 13. Upper Confidence Bound in R - Step 3.srt |
25.32Кб |
| 14. ANN in Python - Step 3.mp4 |
75.07Мб |
| 14. ANN in Python - Step 3.srt |
23.46Кб |
| 14. CNN in Python - Step 4.mp4 |
40.02Мб |
| 14. CNN in Python - Step 4.srt |
11.44Кб |
| 14. Hierarchical Clustering.html |
125б |
| 14. Homework Challenge.html |
1.36Кб |
| 14. Multiple Linear Regression in Python - BONUS.html |
1.20Кб |
| 14. Upper Confidence Bound in R - Step 4.mp4 |
9.55Мб |
| 14. Upper Confidence Bound in R - Step 4.srt |
4.41Кб |
| 14. Warning - Update.html |
1.33Кб |
| 14. Your Shortcut To Becoming A Better Data Scientist!.html |
3.74Кб |
| 15.1 Clustering-Pros-Cons.pdf |
25.76Кб |
| 15. ANN in Python - Step 4.mp4 |
65.38Мб |
| 15. ANN in Python - Step 4.srt |
20.20Кб |
| 15. CNN in Python - Step 5.mp4 |
97.68Мб |
| 15. CNN in Python - Step 5.srt |
22.49Кб |
| 15. Conclusion of Part 4 - Clustering.html |
516б |
| 15. Logistic Regression in R - Step 5.mp4 |
93.77Мб |
| 15. Logistic Regression in R - Step 5.srt |
29.10Кб |
| 15. Multiple Linear Regression in R - Step 1.mp4 |
23.44Мб |
| 15. Multiple Linear Regression in R - Step 1.srt |
11.85Кб |
| 15. Natural Language Processing in R - Step 1.mp4 |
51.20Мб |
| 15. Natural Language Processing in R - Step 1.srt |
24.00Кб |
| 16. ANN in Python - Step 5.mp4 |
101.34Мб |
| 16. ANN in Python - Step 5.srt |
25.76Кб |
| 16. CNN in Python - FINAL DEMO!.mp4 |
152.77Мб |
| 16. CNN in Python - FINAL DEMO!.srt |
38.78Кб |
| 16. Multiple Linear Regression in R - Step 2.mp4 |
45.22Мб |
| 16. Multiple Linear Regression in R - Step 2.srt |
15.43Кб |
| 16. Natural Language Processing in R - Step 2.mp4 |
21.66Мб |
| 16. Natural Language Processing in R - Step 2.srt |
12.88Кб |
| 16. R Classification Template.mp4 |
17.51Мб |
| 16. R Classification Template.srt |
6.70Кб |
| 17. ANN in R - Step 1.mp4 |
49.90Мб |
| 17. ANN in R - Step 1.srt |
26.79Кб |
| 17. Deep Learning BONUS #2.html |
923б |
| 17. Machine Learning Regression and Classification BONUS.html |
819б |
| 17. Multiple Linear Regression in R - Step 3.mp4 |
13.85Мб |
| 17. Multiple Linear Regression in R - Step 3.srt |
7.06Кб |
| 17. Natural Language Processing in R - Step 3.mp4 |
16.89Мб |
| 17. Natural Language Processing in R - Step 3.srt |
10.12Кб |
| 18. ANN in R - Step 2.mp4 |
18.24Мб |
| 18. ANN in R - Step 2.srt |
10.12Кб |
| 18. Logistic Regression.html |
125б |
| 18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 |
50.79Мб |
| 18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.srt |
27.48Кб |
| 18. Natural Language Processing in R - Step 4.mp4 |
8.25Мб |
| 18. Natural Language Processing in R - Step 4.srt |
4.66Кб |
| 19. ANN in R - Step 3.mp4 |
37.86Мб |
| 19. ANN in R - Step 3.srt |
18.88Кб |
| 19. BONUS Logistic Regression Practical Case Study.html |
619б |
| 19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 |
21.95Мб |
| 19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.srt |
11.86Кб |
| 19. Natural Language Processing in R - Step 5.mp4 |
5.78Мб |
| 19. Natural Language Processing in R - Step 5.srt |
3.25Кб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 2.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 2. Adjusted R-Squared Intuition.mp4 |
21.42Мб |
| 2. Adjusted R-Squared Intuition.srt |
14.45Кб |
| 2. Algorithm Comparison UCB vs Thompson Sampling.mp4 |
14.08Мб |
| 2. Algorithm Comparison UCB vs Thompson Sampling.srt |
11.14Кб |
| 2. BONUS #1 Learning Paths.html |
1.39Кб |
| 2. Confusion Matrix.mp4 |
8.91Мб |
| 2. Confusion Matrix.srt |
7.51Кб |
| 2. Getting Started.mp4 |
54.34Мб |
| 2. Getting Started.mp4 |
9.81Мб |
| 2. Getting Started.srt |
16.73Кб |
| 2. Getting Started.srt |
2.46Кб |
| 2. Heads-up on non-linear SVR.mp4 |
19.78Мб |
| 2. Heads-up on non-linear SVR.srt |
5.93Кб |
| 2. Hierarchical Clustering Intuition.mp4 |
16.52Мб |
| 2. Hierarchical Clustering Intuition.srt |
14.59Кб |
| 2. Interpreting Linear Regression Coefficients.mp4 |
27.38Мб |
| 2. Interpreting Linear Regression Coefficients.srt |
13.32Кб |
| 2. Kernel PCA in Python.mp4 |
77.50Мб |
| 2. Kernel PCA in Python.srt |
17.46Кб |
| 2. k-Fold Cross Validation in Python.mp4 |
112.37Мб |
| 2. k-Fold Cross Validation in Python.srt |
28.65Кб |
| 2. K-Means Random Initialization Trap.mp4 |
15.37Мб |
| 2. K-Means Random Initialization Trap.srt |
12.95Кб |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 2. Mapping to a higher dimension.mp4 |
15.40Мб |
| 2. Mapping to a higher dimension.srt |
10.54Кб |
| 2. Multiple Linear Regression Intuition - Step 1.mp4 |
2.00Мб |
| 2. Multiple Linear Regression Intuition - Step 1.srt |
1.58Кб |
| 2. Naive Bayes Intuition.mp4 |
31.11Мб |
| 2. Naive Bayes Intuition.srt |
23.34Кб |
| 2. NLP Intuition.mp4 |
12.71Мб |
| 2. NLP Intuition.srt |
4.57Кб |
| 2. Preparation of the Regression Code Templates.mp4 |
123.59Мб |
| 2. Preparation of the Regression Code Templates.srt |
30.20Кб |
| 2. Simple Linear Regression Intuition - Step 2.mp4 |
5.99Мб |
| 2. Simple Linear Regression Intuition - Step 2.srt |
4.34Кб |
| 2. SVM Intuition.mp4 |
19.92Мб |
| 2. SVM Intuition.srt |
15.72Кб |
| 2. The Neuron.mp4 |
29.87Мб |
| 2. The Neuron.srt |
25.04Кб |
| 2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.mp4 |
135.99Мб |
| 2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.srt |
34.43Кб |
| 2. Upper Confidence Bound (UCB) Intuition.mp4 |
29.33Мб |
| 2. Upper Confidence Bound (UCB) Intuition.srt |
21.92Кб |
| 2. What are convolutional neural networks.mp4 |
29.51Мб |
| 2. What are convolutional neural networks.srt |
22.06Кб |
| 2. What is Deep Learning.mp4 |
31.32Мб |
| 2. What is Deep Learning.srt |
158.11Мб |
| 2. XGBoost in Python.mp4 |
89.99Мб |
| 2. XGBoost in Python.srt |
23.05Кб |
| 20. ANN in R - Step 4 (Last step).mp4 |
43.76Мб |
| 20. ANN in R - Step 4 (Last step).srt |
20.68Кб |
| 20. Multiple Linear Regression in R - Automatic Backward Elimination.html |
726б |
| 20. Natural Language Processing in R - Step 6.mp4 |
16.10Мб |
| 20. Natural Language Processing in R - Step 6.srt |
8.35Кб |
| 21. Deep Learning BONUS #1.html |
1011б |
| 21. Multiple Linear Regression.html |
125б |
| 21. Natural Language Processing in R - Step 7.mp4 |
9.59Мб |
| 21. Natural Language Processing in R - Step 7.srt |
5.59Кб |
| 22. BONUS ANN Case Study.html |
544б |
| 22. Natural Language Processing in R - Step 8.mp4 |
17.23Мб |
| 22. Natural Language Processing in R - Step 8.srt |
8.01Кб |
| 23. Natural Language Processing in R - Step 9.mp4 |
37.70Мб |
| 23. Natural Language Processing in R - Step 9.srt |
19.60Кб |
| 24. Natural Language Processing in R - Step 10.mp4 |
54.15Мб |
| 24. Natural Language Processing in R - Step 10.srt |
26.29Кб |
| 25. Homework Challenge.html |
1.40Кб |
| 26. BONUS NLP BERT.html |
906б |
| 3.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 3.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 3.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 3.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 3.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 3.1 Regression_Bonus.zip |
364.49Кб |
| 3. Accuracy Paradox.mp4 |
4.22Мб |
| 3. Accuracy Paradox.srt |
3.24Кб |
| 3. Apriori in Python - Step 1.mp4 |
69.84Мб |
| 3. Apriori in Python - Step 1.srt |
14.29Кб |
| 3. BONUS #2 ML vs. DL vs. AI - What’s the Difference.html |
499б |
| 3. Conclusion of Part 2 - Regression.html |
1.71Кб |
| 3. Decision Tree Classification in Python.mp4 |
108.06Мб |
| 3. Decision Tree Classification in Python.srt |
22.26Кб |
| 3. Decision Tree Regression in Python - Step 1.mp4 |
42.39Мб |
| 3. Decision Tree Regression in Python - Step 1.srt |
13.28Кб |
| 3. Eclat in Python.mp4 |
75.55Мб |
| 3. Eclat in Python.srt |
18.85Кб |
| 3. Grid Search in Python.mp4 |
151.79Мб |
| 3. Grid Search in Python.srt |
34.61Кб |
| 3. Hierarchical Clustering How Dendrograms Work.mp4 |
17.47Мб |
| 3. Hierarchical Clustering How Dendrograms Work.srt |
14.35Кб |
| 3. Importing the Libraries.mp4 |
15.98Мб |
| 3. Importing the Libraries.srt |
5.62Кб |
| 3. Kernel PCA in R.mp4 |
56.58Мб |
| 3. Kernel PCA in R.srt |
30.81Кб |
| 3. K-Means Selecting The Number Of Clusters.mp4 |
25.69Мб |
| 3. K-Means Selecting The Number Of Clusters.srt |
18.46Кб |
| 3. K-NN in Python.mp4 |
146.61Мб |
| 3. K-NN in Python.srt |
30.75Кб |
| 3. LDA in Python.mp4 |
102.00Мб |
| 3. LDA in Python.srt |
23.45Кб |
| 3. Logistic Regression in Python - Step 1.mp4 |
44.60Мб |
| 3. Logistic Regression in Python - Step 1.srt |
14.49Кб |
| 3. Make sure you have your dataset ready.html |
465б |
| 3. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 3. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 3. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 3. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 3. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 3. Model Selection and Boosting BONUS.html |
1.14Кб |
| 3. Multiple Linear Regression Intuition - Step 2.mp4 |
2.04Мб |
| 3. Multiple Linear Regression Intuition - Step 2.srt |
1.45Кб |
| 3. Naive Bayes Intuition (Challenge Reveal).mp4 |
13.27Мб |
| 3. Naive Bayes Intuition (Challenge Reveal).srt |
9.50Кб |
| 3. PCA in Python - Step 1.mp4 |
112.91Мб |
| 3. PCA in Python - Step 1.srt |
26.45Кб |
| 3. Polynomial Regression in Python - Step 1.mp4 |
58.25Мб |
| 3. Polynomial Regression in Python - Step 1.srt |
20.81Кб |
| 3. Random Forest Classification in Python.mp4 |
96.69Мб |
| 3. Random Forest Classification in Python.srt |
21.42Кб |
| 3. Random Forest Regression in Python.mp4 |
74.39Мб |
| 3. Random Forest Regression in Python.srt |
21.08Кб |
| 3. Step 1 - Convolution Operation.mp4 |
31.02Мб |
| 3. Step 1 - Convolution Operation.srt |
23.23Кб |
| 3. The Activation Function.mp4 |
14.76Мб |
| 3. The Activation Function.srt |
12.03Кб |
| 3. The Kernel Trick.mp4 |
34.73Мб |
| 3. The Kernel Trick.srt |
16.52Кб |
| 3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.mp4 |
56.78Мб |
| 3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.srt |
13.62Кб |
| 3. Types of Natural Language Processing.mp4 |
22.50Мб |
| 3. Types of Natural Language Processing.srt |
5.94Кб |
| 4.1 Eclat.zip |
48.54Кб |
| 4.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 4.1 Regression_Bonus.zip |
364.49Кб |
| 4. Apriori in Python - Step 2.mp4 |
107.70Мб |
| 4. Apriori in Python - Step 2.srt |
26.41Кб |
| 4. BONUS #3 Regression Types.html |
511б |
| 4. CAP Curve.mp4 |
20.32Мб |
| 4. CAP Curve.srt |
16.18Кб |
| 4. Classical vs Deep Learning Models.mp4 |
83.95Мб |
| 4. Classical vs Deep Learning Models.srt |
16.14Кб |
| 4. Conclusion of Part 2 - Regression.html |
1.71Кб |
| 4. Dataset Description.mp4 |
11.84Мб |
| 4. Dataset Description.srt |
3.18Кб |
| 4. Decision Tree Classification in R.mp4 |
68.19Мб |
| 4. Decision Tree Classification in R.srt |
29.14Кб |
| 4. Decision Tree Regression in Python - Step 2.mp4 |
26.26Мб |
| 4. Decision Tree Regression in Python - Step 2.srt |
7.54Кб |
| 4. Eclat in R.mp4 |
25.26Мб |
| 4. Eclat in R.srt |
15.79Кб |
| 4. Hierarchical Clustering Using Dendrograms.mp4 |
22.82Мб |
| 4. Hierarchical Clustering Using Dendrograms.srt |
17.60Кб |
| 4. How do Neural Networks work.mp4 |
23.53Мб |
| 4. How do Neural Networks work.srt |
19.11Кб |
| 4. Importing the Dataset.mp4 |
71.79Мб |
| 4. Importing the Dataset.srt |
24.12Кб |
| 4. k-Fold Cross Validation in R.mp4 |
43.64Мб |
| 4. k-Fold Cross Validation in R.srt |
27.91Кб |
| 4. K-NN in R.mp4 |
55.78Мб |
| 4. K-NN in R.srt |
23.37Кб |
| 4. LDA in R.mp4 |
51.29Мб |
| 4. LDA in R.srt |
29.68Кб |
| 4. Logistic Regression in Python - Step 2.mp4 |
84.66Мб |
| 4. Logistic Regression in Python - Step 2.srt |
21.41Кб |
| 4. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 4. Multiple Linear Regression Intuition - Step 3.mp4 |
16.59Мб |
| 4. Multiple Linear Regression Intuition - Step 3.srt |
10.70Кб |
| 4. Naive Bayes Intuition (Extras).mp4 |
18.94Мб |
| 4. Naive Bayes Intuition (Extras).srt |
15.93Кб |
| 4. PCA in Python - Step 2.mp4 |
40.79Мб |
| 4. PCA in Python - Step 2.srt |
9.19Кб |
| 4. Polynomial Regression in Python - Step 2.mp4 |
69.31Мб |
| 4. Polynomial Regression in Python - Step 2.srt |
17.62Кб |
| 4. Random Forest Classification in R.mp4 |
64.11Мб |
| 4. Random Forest Classification in R.srt |
32.40Кб |
| 4. Random Forest Regression in R.mp4 |
51.87Мб |
| 4. Random Forest Regression in R.srt |
28.11Кб |
| 4. Simple Linear Regression in Python - Step 1.mp4 |
48.61Мб |
| 4. Simple Linear Regression in Python - Step 1.srt |
19.77Кб |
| 4. Step 1(b) - ReLU Layer.mp4 |
14.09Мб |
| 4. Step 1(b) - ReLU Layer.srt |
9.20Кб |
| 4. SVM in Python.mp4 |
104.75Мб |
| 4. SVM in Python.srt |
23.96Кб |
| 4. SVR in Python - Step 1.mp4 |
42.56Мб |
| 4. SVR in Python - Step 1.srt |
13.98Кб |
| 4. Thompson Sampling in Python - Step 1.mp4 |
30.59Мб |
| 4. Thompson Sampling in Python - Step 1.srt |
9.74Кб |
| 4. Types of Kernel Functions.mp4 |
15.71Мб |
| 4. Types of Kernel Functions.srt |
4.94Кб |
| 4. Upper Confidence Bound in Python - Step 1.mp4 |
58.74Мб |
| 4. Upper Confidence Bound in Python - Step 1.srt |
20.57Кб |
| 4. XGBoost in R.mp4 |
47.27Мб |
| 4. XGBoost in R.srt |
26.00Кб |
| 5.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 5.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 5.1 SVM.zip |
8.27Кб |
| 5. Apriori in Python - Step 3.mp4 |
69.20Мб |
| 5. Apriori in Python - Step 3.srt |
19.25Кб |
| 5. Bag-Of-Words Model.mp4 |
103.50Мб |
| 5. Bag-Of-Words Model.srt |
28.31Кб |
| 5. CAP Curve Analysis.mp4 |
12.95Мб |
| 5. CAP Curve Analysis.srt |
9.24Кб |
| 5. Decision Tree Regression in Python - Step 3.mp4 |
19.47Мб |
| 5. Decision Tree Regression in Python - Step 3.srt |
4.93Кб |
| 5. For Python learners, summary of Object-oriented programming classes & objects.html |
1.47Кб |
| 5. Grid Search in R.mp4 |
35.55Мб |
| 5. Grid Search in R.srt |
20.94Кб |
| 5. How do Neural Networks learn.mp4 |
26.56Мб |
| 5. How do Neural Networks learn.srt |
18.95Кб |
| 5. Importing the Dataset.mp4 |
16.42Мб |
| 5. Importing the Dataset.srt |
4.50Кб |
| 5. K-Means Clustering in Python - Step 1.mp4 |
38.09Мб |
| 5. K-Means Clustering in Python - Step 1.srt |
12.88Кб |
| 5. Logistic Regression in Python - Step 3.mp4 |
43.05Мб |
| 5. Logistic Regression in Python - Step 3.srt |
10.89Кб |
| 5. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 5. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 5. Multiple Linear Regression Intuition - Step 4.mp4 |
5.34Мб |
| 5. Multiple Linear Regression Intuition - Step 4.srt |
3.51Кб |
| 5. Non-Linear Kernel SVR (Advanced).mp4 |
65.64Мб |
| 5. Non-Linear Kernel SVR (Advanced).srt |
16.03Кб |
| 5. PCA in R - Step 1.mp4 |
30.66Мб |
| 5. PCA in R - Step 1.srt |
18.70Кб |
| 5. Polynomial Regression in Python - Step 3.mp4 |
77.86Мб |
| 5. Polynomial Regression in Python - Step 3.srt |
19.92Кб |
| 5. Simple Linear Regression in Python - Step 2.mp4 |
39.85Мб |
| 5. Simple Linear Regression in Python - Step 2.srt |
11.80Кб |
| 5. Step 2 - Pooling.mp4 |
40.25Мб |
| 5. Step 2 - Pooling.srt |
21.04Кб |
| 5. SVM in R.mp4 |
65.32Мб |
| 5. SVM in R.srt |
18.40Кб |
| 5. SVR in Python - Step 2.mp4 |
86.92Мб |
| 5. SVR in Python - Step 2.srt |
22.14Кб |
| 5. THANK YOU Bonus Video.mp4 |
52.25Мб |
| 5. THANK YOU Bonus Video.srt |
2.31Кб |
| 5. Thompson Sampling in Python - Step 2.mp4 |
70.01Мб |
| 5. Thompson Sampling in Python - Step 2.srt |
17.89Кб |
| 5. Upper Confidence Bound in Python - Step 2.mp4 |
17.75Мб |
| 5. Upper Confidence Bound in Python - Step 2.srt |
6.38Кб |
| 5. Why Machine Learning is the Future.mp4 |
14.49Мб |
| 5. Why Machine Learning is the Future.srt |
9.23Кб |
| 6.1 Classification_Pros_Cons.pdf |
29.25Кб |
| 6.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 6.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 6. Apriori in Python - Step 4.mp4 |
164.33Мб |
| 6. Apriori in Python - Step 4.srt |
31.25Кб |
| 6. Conclusion of Part 3 - Classification.html |
3.35Кб |
| 6. Decision Tree Regression in Python - Step 4.mp4 |
54.79Мб |
| 6. Decision Tree Regression in Python - Step 4.srt |
15.47Кб |
| 6. Gradient Descent.mp4 |
18.54Мб |
| 6. Gradient Descent.srt |
14.02Кб |
| 6. Hierarchical Clustering in Python - Step 1.mp4 |
40.23Мб |
| 6. Hierarchical Clustering in Python - Step 1.srt |
10.57Кб |
| 6. Important notes, tips & tricks for this course.html |
3.30Кб |
| 6. K-Means Clustering in Python - Step 2.mp4 |
54.08Мб |
| 6. K-Means Clustering in Python - Step 2.srt |
15.61Кб |
| 6. Logistic Regression in Python - Step 4.mp4 |
45.20Мб |
| 6. Logistic Regression in Python - Step 4.srt |
11.19Кб |
| 6. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 6. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 6. Naive Bayes in Python.mp4 |
100.47Мб |
| 6. Naive Bayes in Python.srt |
22.25Кб |
| 6. PCA in R - Step 2.mp4 |
29.03Мб |
| 6. PCA in R - Step 2.srt |
16.89Кб |
| 6. Polynomial Regression in Python - Step 4.mp4 |
38.79Мб |
| 6. Polynomial Regression in Python - Step 4.srt |
12.31Кб |
| 6. Simple Linear Regression in Python - Step 3.mp4 |
28.22Мб |
| 6. Simple Linear Regression in Python - Step 3.srt |
7.34Кб |
| 6. Step 3 - Flattening.mp4 |
3.27Мб |
| 6. Step 3 - Flattening.srt |
2.54Кб |
| 6. SVR in Python - Step 3.mp4 |
34.80Мб |
| 6. SVR in Python - Step 3.srt |
9.69Кб |
| 6. Taking care of Missing Data.mp4 |
69.02Мб |
| 6. Taking care of Missing Data.mp4 |
39.79Мб |
| 6. Taking care of Missing Data.srt |
18.05Кб |
| 6. Taking care of Missing Data.srt |
9.05Кб |
| 6. Thompson Sampling in Python - Step 3.mp4 |
78.66Мб |
| 6. Thompson Sampling in Python - Step 3.srt |
20.54Кб |
| 6. Understanding the P-Value.mp4 |
56.48Мб |
| 6. Understanding the P-Value.srt |
19.49Кб |
| 6. Upper Confidence Bound in Python - Step 3.mp4 |
38.47Мб |
| 6. Upper Confidence Bound in Python - Step 3.srt |
11.02Кб |
| 7.1 Machine_Learning_A_Z_Q_A.pdf |
2.26Мб |
| 7. Apriori in R - Step 1.mp4 |
52.84Мб |
| 7. Apriori in R - Step 1.srt |
31.05Кб |
| 7. Decision Tree Regression in R.mp4 |
56.24Мб |
| 7. Decision Tree Regression in R.srt |
32.15Кб |
| 7. Encoding Categorical Data.mp4 |
88.63Мб |
| 7. Encoding Categorical Data.mp4 |
57.32Мб |
| 7. Encoding Categorical Data.srt |
21.98Кб |
| 7. Encoding Categorical Data.srt |
8.51Кб |
| 7. Hierarchical Clustering in Python - Step 2.mp4 |
135.92Мб |
| 7. Hierarchical Clustering in Python - Step 2.srt |
26.22Кб |
| 7. Kernel SVM in Python.mp4 |
88.37Мб |
| 7. Kernel SVM in Python.srt |
20.43Кб |
| 7. K-Means Clustering in Python - Step 3.mp4 |
81.33Мб |
| 7. K-Means Clustering in Python - Step 3.srt |
23.64Кб |
| 7. Logistic Regression in Python - Step 5.mp4 |
30.59Мб |
| 7. Logistic Regression in Python - Step 5.srt |
9.50Кб |
| 7. Multiple Linear Regression Intuition - Step 5.mp4 |
32.81Мб |
| 7. Multiple Linear Regression Intuition - Step 5.srt |
23.56Кб |
| 7. Naive Bayes in R.mp4 |
49.80Мб |
| 7. Naive Bayes in R.srt |
21.90Кб |
| 7. Natural Language Processing in Python - Step 1.mp4 |
34.07Мб |
| 7. Natural Language Processing in Python - Step 1.srt |
11.14Кб |
| 7. PCA in R - Step 3.mp4 |
36.74Мб |
| 7. PCA in R - Step 3.srt |
19.76Кб |
| 7. Polynomial Regression in R - Step 1.mp4 |
21.21Мб |
| 7. Polynomial Regression in R - Step 1.srt |
14.12Кб |
| 7. Simple Linear Regression in Python - Step 4.mp4 |
74.57Мб |
| 7. Simple Linear Regression in Python - Step 4.srt |
19.40Кб |
| 7. Step 4 - Full Connection.mp4 |
42.75Мб |
| 7. Step 4 - Full Connection.srt |
28.57Кб |
| 7. Stochastic Gradient Descent.mp4 |
16.82Мб |
| 7. Stochastic Gradient Descent.srt |
12.14Кб |
| 7. SVR in Python - Step 4.mp4 |
46.30Мб |
| 7. SVR in Python - Step 4.srt |
11.86Кб |
| 7. This PDF resource will help you a lot!.html |
1.49Кб |
| 7. Thompson Sampling in Python - Step 4.mp4 |
44.64Мб |
| 7. Thompson Sampling in Python - Step 4.srt |
11.33Кб |
| 7. Upper Confidence Bound in Python - Step 4.mp4 |
85.38Мб |
| 7. Upper Confidence Bound in Python - Step 4.srt |
25.12Кб |
| 8.1 Machine Learning A-Z (Codes and Datasets).zip |
5.27Мб |
| 8.1 Machine Learning A-Z (Codes and Datasets).zip |
5.28Мб |
| 8. Additional Resource for this Section.html |
2.24Кб |
| 8. Apriori in R - Step 2.mp4 |
38.82Мб |
| 8. Apriori in R - Step 2.srt |
23.02Кб |
| 8. Backpropagation.mp4 |
10.93Мб |
| 8. Backpropagation.srt |
7.11Кб |
| 8. GET ALL THE CODES AND DATASETS HERE!.html |
1.83Кб |
| 8. Hierarchical Clustering in Python - Step 3.mp4 |
75.29Мб |
| 8. Hierarchical Clustering in Python - Step 3.srt |
18.17Кб |
| 8. Kernel SVM in R.mp4 |
52.82Мб |
| 8. Kernel SVM in R.srt |
25.44Кб |
| 8. K-Means Clustering in Python - Step 4.mp4 |
35.10Мб |
| 8. K-Means Clustering in Python - Step 4.srt |
9.40Кб |
| 8. Logistic Regression in Python - Step 6.mp4 |
52.96Мб |
| 8. Logistic Regression in Python - Step 6.srt |
13.66Кб |
| 8. Make sure you have your Machine Learning A-Z folder ready.html |
776б |
| 8. Natural Language Processing in Python - Step 2.mp4 |
40.48Мб |
| 8. Natural Language Processing in Python - Step 2.srt |
10.77Кб |
| 8. Polynomial Regression in R - Step 2.mp4 |
32.28Мб |
| 8. Polynomial Regression in R - Step 2.srt |
15.27Кб |
| 8. Simple Linear Regression in Python - BONUS.html |
1.12Кб |
| 8. Splitting the dataset into the Training set and Test set.mp4 |
67.63Мб |
| 8. Splitting the dataset into the Training set and Test set.mp4 |
86.50Мб |
| 8. Splitting the dataset into the Training set and Test set.srt |
20.02Кб |
| 8. Splitting the dataset into the Training set and Test set.srt |
14.81Кб |
| 8. Summary.mp4 |
7.92Мб |
| 8. Summary.srt |
6.02Кб |
| 8. SVR in Python - Step 5.mp4 |
93.64Мб |
| 8. SVR in Python - Step 5.srt |
22.27Кб |
| 8. Upper Confidence Bound in Python - Step 5.mp4 |
32.43Мб |
| 8. Upper Confidence Bound in Python - Step 5.srt |
9.50Кб |
| 9. Apriori in R - Step 3.mp4 |
56.51Мб |
| 9. Apriori in R - Step 3.srt |
31.16Кб |
| 9. Business Problem Description.mp4 |
29.24Мб |
| 9. Business Problem Description.srt |
7.30Кб |
| 9. Feature Scaling.mp4 |
101.72Мб |
| 9. Feature Scaling.mp4 |
78.89Мб |
| 9. Feature Scaling.srt |
30.26Кб |
| 9. Feature Scaling.srt |
13.08Кб |
| 9. Hierarchical Clustering in R - Step 1.mp4 |
8.59Мб |
| 9. Hierarchical Clustering in R - Step 1.srt |
6.35Кб |
| 9. K-Means Clustering in Python - Step 5.mp4 |
120.50Мб |
| 9. K-Means Clustering in Python - Step 5.srt |
29.09Кб |
| 9. Logistic Regression in Python - Step 7.mp4 |
118.63Мб |
| 9. Logistic Regression in Python - Step 7.srt |
22.52Кб |
| 9. Multiple Linear Regression in Python - Step 1.mp4 |
50.92Мб |
| 9. Multiple Linear Regression in Python - Step 1.srt |
13.16Кб |
| 9. Natural Language Processing in Python - Step 3.mp4 |
60.61Мб |
| 9. Natural Language Processing in Python - Step 3.srt |
19.11Кб |
| 9. Polynomial Regression in R - Step 3.mp4 |
54.81Мб |
| 9. Polynomial Regression in R - Step 3.srt |
30.86Кб |
| 9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.mp4 |
94.80Мб |
| 9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.srt |
28.27Кб |
| 9. Simple Linear Regression in R - Step 1.mp4 |
11.53Мб |
| 9. Simple Linear Regression in R - Step 1.srt |
7.71Кб |
| 9. Softmax & Cross-Entropy.mp4 |
33.24Мб |
| 9. Softmax & Cross-Entropy.srt |
25.27Кб |
| 9. SVR in R.mp4 |
33.73Мб |
| 9. SVR in R.srt |
18.70Кб |
| 9. Thompson Sampling in R - Step 1.mp4 |
51.04Мб |
| 9. Thompson Sampling in R - Step 1.srt |
27.86Кб |
| 9. Upper Confidence Bound in Python - Step 6.mp4 |
44.90Мб |
| 9. Upper Confidence Bound in Python - Step 6.srt |
11.24Кб |