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| [CourseClub.ME].url |
122б |
| [FCS Forum].url |
133б |
| [FreeCourseSite.com].url |
127б |
| 1. Applications of Machine Learning.mp4 |
7.99Мб |
| 1. Applications of Machine Learning.vtt |
4.64Кб |
| 1. Apriori Intuition.mp4 |
35.02Мб |
| 1. Apriori Intuition.vtt |
22.59Кб |
| 1. Bayes Theorem.mp4 |
43.90Мб |
| 1. Bayes Theorem.vtt |
30.66Кб |
| 1. Decision Tree Classification Intuition.mp4 |
18.79Мб |
| 1. Decision Tree Classification Intuition.vtt |
18.80Мб |
| 1. Decision Tree Regression Intuition.mp4 |
22.69Мб |
| 1. Decision Tree Regression Intuition.vtt |
15.25Кб |
| 1. Eclat Intuition.mp4 |
10.65Мб |
| 1. Eclat Intuition.vtt |
7.12Кб |
| 1. False Positives & False Negatives.mp4 |
13.65Мб |
| 1. False Positives & False Negatives.vtt |
10.19Кб |
| 1. Hierarchical Clustering Intuition.mp4 |
16.53Мб |
| 1. Hierarchical Clustering Intuition.vtt |
16.53Мб |
| 1. How to get the dataset.mp4 |
11.71Мб |
| 1. How to get the dataset.mp4 |
11.72Мб |
| 1. How to get the dataset.mp4 |
11.71Мб |
| 1. How to get the dataset.mp4 |
11.71Мб |
| 1. How to get the dataset.mp4 |
11.71Мб |
| 1. How to get the dataset.mp4 |
11.71Мб |
| 1. How to get the dataset.vtt |
4.23Кб |
| 1. How to get the dataset.vtt |
11.72Мб |
| 1. How to get the dataset.vtt |
4.23Кб |
| 1. How to get the dataset.vtt |
4.23Кб |
| 1. How to get the dataset.vtt |
4.23Кб |
| 1. How to get the dataset.vtt |
4.23Кб |
| 1. Kernel SVM Intuition.mp4 |
5.79Мб |
| 1. Kernel SVM Intuition.vtt |
3.92Кб |
| 1. K-Means Clustering Intuition.mp4 |
26.86Мб |
| 1. K-Means Clustering Intuition.vtt |
20.91Кб |
| 1. K-Nearest Neighbor Intuition.mp4 |
9.28Мб |
| 1. K-Nearest Neighbor Intuition.vtt |
7.23Кб |
| 1. Linear Discriminant Analysis (LDA) Intuition.mp4 |
26.98Мб |
| 1. Linear Discriminant Analysis (LDA) Intuition.vtt |
4.53Кб |
| 1. Logistic Regression Intuition.mp4 |
29.17Мб |
| 1. Logistic Regression Intuition.vtt |
20.91Кб |
| 1. Plan of attack.mp4 |
4.74Мб |
| 1. Plan of attack.mp4 |
5.90Мб |
| 1. Plan of attack.vtt |
3.54Кб |
| 1. Plan of attack.vtt |
4.63Кб |
| 1. Polynomial Regression Intuition.mp4 |
9.44Мб |
| 1. Polynomial Regression Intuition.vtt |
7.07Кб |
| 1. Principal Component Analysis (PCA) Intuition.mp4 |
32.11Мб |
| 1. Principal Component Analysis (PCA) Intuition.vtt |
4.45Кб |
| 1. Random Forest Classification Intuition.mp4 |
19.43Мб |
| 1. Random Forest Classification Intuition.vtt |
6.41Кб |
| 1. Random Forest Regression Intuition.mp4 |
13.82Мб |
| 1. Random Forest Regression Intuition.vtt |
9.29Кб |
| 1. R-Squared Intuition.mp4 |
8.85Мб |
| 1. R-Squared Intuition.vtt |
6.46Кб |
| 1. SVM Intuition.mp4 |
18.01Мб |
| 1. SVM Intuition.vtt |
14.19Кб |
| 1. The Multi-Armed Bandit Problem.mp4 |
30.19Мб |
| 1. The Multi-Armed Bandit Problem.vtt |
19.44Кб |
| 1. Thompson Sampling Intuition.mp4 |
37.27Мб |
| 1. Thompson Sampling Intuition.vtt |
24.09Кб |
| 1. Welcome to Part 10 - Model Selection & Boosting.html |
899б |
| 1. Welcome to Part 1 - Data Preprocessing.mp4 |
2.99Мб |
| 1. Welcome to Part 1 - Data Preprocessing.vtt |
2.29Кб |
| 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 |
804б |
| 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 |
4.54Кб |
| 10. Business Problem Description.mp4 |
16.38Мб |
| 10. Business Problem Description.vtt |
6.47Кб |
| 10. Feature Scaling.mp4 |
34.62Мб |
| 10. Feature Scaling.vtt |
20.79Кб |
| 10. HC in R - Step 1.mp4 |
7.38Мб |
| 10. HC in R - Step 1.vtt |
5.67Кб |
| 10. How to get the dataset.mp4 |
11.71Мб |
| 10. How to get the dataset.vtt |
4.23Кб |
| 10. Installing R and R Studio (Mac, Linux & Windows).mp4 |
17.55Мб |
| 10. Installing R and R Studio (Mac, Linux & Windows).vtt |
7.94Кб |
| 10. Logistic Regression in R - Step 2.mp4 |
7.85Мб |
| 10. Logistic Regression in R - Step 2.vtt |
3.92Кб |
| 10. Multiple Linear Regression in Python - Step 2.mp4 |
7.23Мб |
| 10. Multiple Linear Regression in Python - Step 2.vtt |
3.63Кб |
| 10. Natural Language Processing in Python - Step 7.mp4 |
17.10Мб |
| 10. Natural Language Processing in Python - Step 7.vtt |
8.60Кб |
| 10. Polynomial Regression in R - Step 3.mp4 |
43.31Мб |
| 10. Polynomial Regression in R - Step 3.vtt |
27.44Кб |
| 10. Simple Linear Regression in R - Step 2.mp4 |
14.36Мб |
| 10. Simple Linear Regression in R - Step 2.vtt |
8.00Кб |
| 10. Upper Confidence Bound in R - Step 3.mp4 |
47.20Мб |
| 10. Upper Confidence Bound in R - Step 3.vtt |
21.98Кб |
| 11. And here is our Data Preprocessing Template!.mp4 |
19.67Мб |
| 11. And here is our Data Preprocessing Template!.vtt |
12.67Кб |
| 11. BONUS Meet your instructors.html |
1.04Кб |
| 11. HC in R - Step 2.mp4 |
11.15Мб |
| 11. HC in R - Step 2.vtt |
7.32Кб |
| 11. Installing Keras.html |
1.42Кб |
| 11. Installing Keras.html |
927б |
| 11. Logistic Regression in R - Step 3.mp4 |
14.59Мб |
| 11. Logistic Regression in R - Step 3.vtt |
6.65Кб |
| 11. Multiple Linear Regression in Python - Step 3.mp4 |
14.29Мб |
| 11. Multiple Linear Regression in Python - Step 3.vtt |
7.38Кб |
| 11. Natural Language Processing in Python - Step 8.mp4 |
39.48Мб |
| 11. Natural Language Processing in Python - Step 8.vtt |
20.80Кб |
| 11. Polynomial Regression in R - Step 4.mp4 |
22.34Мб |
| 11. Polynomial Regression in R - Step 4.vtt |
22.36Мб |
| 11. Simple Linear Regression in R - Step 3.mp4 |
8.64Мб |
| 11. Simple Linear Regression in R - Step 3.vtt |
4.94Кб |
| 11. Upper Confidence Bound in R - Step 4.mp4 |
7.41Мб |
| 11. Upper Confidence Bound in R - Step 4.vtt |
3.86Кб |
| 12. ANN in Python - Step 1.mp4 |
29.31Мб |
| 12. ANN in Python - Step 1.vtt |
17.41Кб |
| 12. CNN in Python - Step 1.mp4 |
24.93Мб |
| 12. CNN in Python - Step 1.vtt |
16.16Кб |
| 12. Data Preprocessing.html |
118б |
| 12. HC in R - Step 3.mp4 |
7.81Мб |
| 12. HC in R - Step 3.vtt |
4.29Кб |
| 12. Logistic Regression in R - Step 4.mp4 |
6.91Мб |
| 12. Logistic Regression in R - Step 4.vtt |
3.55Кб |
| 12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 |
23.82Мб |
| 12. Multiple Linear Regression in Python - Backward Elimination - Preparation.vtt |
13.13Кб |
| 12. Natural Language Processing in Python - Step 9.mp4 |
14.01Мб |
| 12. Natural Language Processing in Python - Step 9.vtt |
7.25Кб |
| 12. R Regression Template.mp4 |
25.41Мб |
| 12. R Regression Template.vtt |
16.72Кб |
| 12. Simple Linear Regression in R - Step 4.mp4 |
37.37Мб |
| 12. Simple Linear Regression in R - Step 4.vtt |
21.21Кб |
| 12. Some Additional Resources.html |
551б |
| 13. ANN in Python - Step 2.mp4 |
48.09Мб |
| 13. ANN in Python - Step 2.vtt |
24.77Кб |
| 13. CNN in Python - Step 2.mp4 |
5.86Мб |
| 13. CNN in Python - Step 2.vtt |
3.92Кб |
| 13. FAQBot!.html |
1.76Кб |
| 13. HC in R - Step 4.mp4 |
7.44Мб |
| 13. HC in R - Step 4.vtt |
3.49Кб |
| 13. Logistic Regression in R - Step 5.mp4 |
51.68Мб |
| 13. Logistic Regression in R - Step 5.vtt |
26.00Кб |
| 13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 |
32.59Мб |
| 13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.vtt |
17.57Кб |
| 13. Natural Language Processing in Python - Step 10.mp4 |
24.13Мб |
| 13. Natural Language Processing in Python - Step 10.vtt |
12.49Кб |
| 13. Simple Linear Regression.html |
118б |
| 14. ANN in Python - Step 3.mp4 |
8.38Мб |
| 14. ANN in Python - Step 3.vtt |
4.62Кб |
| 14. CNN in Python - Step 3.mp4 |
2.22Мб |
| 14. CNN in Python - Step 3.vtt |
1.56Кб |
| 14. HC in R - Step 5.mp4 |
6.89Мб |
| 14. HC in R - Step 5.vtt |
3.66Кб |
| 14. Homework Challenge.html |
1.37Кб |
| 14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 |
27.17Мб |
| 14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.vtt |
12.68Кб |
| 14. R Classification Template.mp4 |
12.47Мб |
| 14. R Classification Template.vtt |
6.06Кб |
| 15. ANN in Python - Step 4.mp4 |
5.88Мб |
| 15. ANN in Python - Step 4.vtt |
3.46Кб |
| 15. CNN in Python - Step 4.mp4 |
27.18Мб |
| 15. CNN in Python - Step 4.vtt |
16.89Кб |
| 15. Hierarchical Clustering.html |
118б |
| 15. Logistic Regression.html |
118б |
| 15. Multiple Linear Regression in Python - Automatic Backward Elimination.html |
2.14Кб |
| 15. Natural Language Processing in R - Step 1.mp4 |
40.38Мб |
| 15. Natural Language Processing in R - Step 1.vtt |
40.38Мб |
| 16.1 Clustering-Pros-Cons.pdf.pdf |
25.76Кб |
| 16. ANN in Python - Step 5.mp4 |
29.58Мб |
| 16. ANN in Python - Step 5.vtt |
17.06Кб |
| 16. CNN in Python - Step 5.mp4 |
9.91Мб |
| 16. CNN in Python - Step 5.vtt |
6.59Кб |
| 16. Conclusion of Part 4 - Clustering.html |
516б |
| 16. Multiple Linear Regression in R - Step 1.mp4 |
17.94Мб |
| 16. Multiple Linear Regression in R - Step 1.vtt |
10.50Кб |
| 16. Natural Language Processing in R - Step 2.mp4 |
17.48Мб |
| 16. Natural Language Processing in R - Step 2.vtt |
11.33Кб |
| 17. ANN in Python - Step 6.mp4 |
7.06Мб |
| 17. ANN in Python - Step 6.vtt |
4.03Кб |
| 17. CNN in Python - Step 6.mp4 |
9.71Мб |
| 17. CNN in Python - Step 6.vtt |
6.71Кб |
| 17. Multiple Linear Regression in R - Step 2.mp4 |
25.93Мб |
| 17. Multiple Linear Regression in R - Step 2.vtt |
13.83Кб |
| 17. Natural Language Processing in R - Step 3.mp4 |
13.52Мб |
| 17. Natural Language Processing in R - Step 3.vtt |
8.83Кб |
| 18. ANN in Python - Step 7.mp4 |
8.99Мб |
| 18. ANN in Python - Step 7.vtt |
5.17Кб |
| 18. CNN in Python - Step 7.mp4 |
12.93Мб |
| 18. CNN in Python - Step 7.vtt |
8.02Кб |
| 18. Multiple Linear Regression in R - Step 3.mp4 |
10.41Мб |
| 18. Multiple Linear Regression in R - Step 3.vtt |
6.29Кб |
| 18. Natural Language Processing in R - Step 4.mp4 |
6.51Мб |
| 18. Natural Language Processing in R - Step 4.vtt |
4.20Кб |
| 19. ANN in Python - Step 8.mp4 |
18.17Мб |
| 19. ANN in Python - Step 8.vtt |
18.18Мб |
| 19. CNN in Python - Step 8.mp4 |
6.80Мб |
| 19. CNN in Python - Step 8.vtt |
3.91Кб |
| 19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 |
39.73Мб |
| 19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.vtt |
24.57Кб |
| 19. Natural Language Processing in R - Step 5.mp4 |
4.57Мб |
| 19. Natural Language Processing in R - Step 5.vtt |
2.83Кб |
| 2. Adjusted R-Squared Intuition.mp4 |
19.28Мб |
| 2. Adjusted R-Squared Intuition.vtt |
12.99Кб |
| 2. Algorithm Comparison UCB vs Thompson Sampling.mp4 |
14.09Мб |
| 2. Algorithm Comparison UCB vs Thompson Sampling.vtt |
9.89Кб |
| 2. BONUS Learning Paths.html |
2.37Кб |
| 2. Confusion Matrix.mp4 |
8.22Мб |
| 2. Confusion Matrix.vtt |
6.74Кб |
| 2. Dataset + Business Problem Description.mp4 |
6.63Мб |
| 2. Dataset + Business Problem Description.mp4 |
9.98Мб |
| 2. Dataset + Business Problem Description.vtt |
3.71Кб |
| 2. Dataset + Business Problem Description.vtt |
5.11Кб |
| 2. Get the dataset.mp4 |
21.15Мб |
| 2. Get the dataset.vtt |
9.39Кб |
| 2. Hierarchical Clustering How Dendrograms Work.mp4 |
17.47Мб |
| 2. Hierarchical Clustering How Dendrograms Work.vtt |
12.84Кб |
| 2. How to get the dataset.mp4 |
11.71Мб |
| 2. How to get the dataset.mp4 |
11.71Мб |
| 2. How to get the dataset.mp4 |
11.72Мб |
| 2. How to get the dataset.mp4 |
11.71Мб |
| 2. How to get the dataset.mp4 |
11.71Мб |
| 2. How to get the dataset.mp4 |
11.72Мб |
| 2. How to get the dataset.mp4 |
11.72Мб |
| 2. How to get the dataset.mp4 |
11.71Мб |
| 2. How to get the dataset.mp4 |
11.71Мб |
| 2. How to get the dataset.mp4 |
11.71Мб |
| 2. How to get the dataset.mp4 |
11.71Мб |
| 2. How to get the dataset.mp4 |
11.72Мб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. How to get the dataset.vtt |
4.23Кб |
| 2. Kernel PCA in Python.mp4 |
33.38Мб |
| 2. Kernel PCA in Python.vtt |
18.79Кб |
| 2. k-Fold Cross Validation in Python.mp4 |
32.83Мб |
| 2. k-Fold Cross Validation in Python.vtt |
17.62Кб |
| 2. K-Means Random Initialization Trap.mp4 |
15.36Мб |
| 2. K-Means Random Initialization Trap.vtt |
11.61Кб |
| 2. Mapping to a higher dimension.mp4 |
13.74Мб |
| 2. Mapping to a higher dimension.vtt |
9.32Кб |
| 2. Naive Bayes Intuition.mp4 |
27.79Мб |
| 2. Naive Bayes Intuition.vtt |
20.89Кб |
| 2. Natural Language Processing Intuition.mp4 |
29.69Мб |
| 2. Natural Language Processing Intuition.vtt |
6.26Кб |
| 2. SVR Intuition.mp4 |
46.59Мб |
| 2. SVR Intuition.vtt |
10.11Кб |
| 2. The Neuron.mp4 |
29.87Мб |
| 2. The Neuron.vtt |
21.91Кб |
| 2. Upper Confidence Bound (UCB) Intuition.mp4 |
29.33Мб |
| 2. Upper Confidence Bound (UCB) Intuition.vtt |
29.33Мб |
| 2. What are convolutional neural networks.mp4 |
29.50Мб |
| 2. What are convolutional neural networks.vtt |
19.34Кб |
| 2. What is Deep Learning.mp4 |
31.31Мб |
| 2. What is Deep Learning.vtt |
15.89Кб |
| 2. XGBoost in Python - Step 1.mp4 |
21.39Мб |
| 2. XGBoost in Python - Step 1.vtt |
12.05Кб |
| 20. ANN in Python - Step 9.mp4 |
16.90Мб |
| 20. ANN in Python - Step 9.vtt |
8.24Кб |
| 20. CNN in Python - Step 9.mp4 |
46.85Мб |
| 20. CNN in Python - Step 9.vtt |
25.49Кб |
| 20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 |
17.24Мб |
| 20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.vtt |
10.59Кб |
| 20. Natural Language Processing in R - Step 6.mp4 |
12.74Мб |
| 20. Natural Language Processing in R - Step 6.vtt |
7.32Кб |
| 21. ANN in Python - Step 10.mp4 |
17.09Мб |
| 21. ANN in Python - Step 10.vtt |
9.03Кб |
| 21. CNN in Python - Step 10.mp4 |
20.60Мб |
| 21. CNN in Python - Step 10.vtt |
11.28Кб |
| 21. Multiple Linear Regression in R - Automatic Backward Elimination.html |
726б |
| 21. Natural Language Processing in R - Step 7.mp4 |
7.52Мб |
| 21. Natural Language Processing in R - Step 7.vtt |
4.99Кб |
| 22. ANN in R - Step 1.mp4 |
38.55Мб |
| 22. ANN in R - Step 1.vtt |
23.03Кб |
| 22. CNN in R.html |
2.38Кб |
| 22. Multiple Linear Regression.html |
118б |
| 22. Natural Language Processing in R - Step 8.mp4 |
13.27Мб |
| 22. Natural Language Processing in R - Step 8.vtt |
6.97Кб |
| 23. ANN in R - Step 2.mp4 |
14.17Мб |
| 23. ANN in R - Step 2.vtt |
8.85Кб |
| 23. Natural Language Processing in R - Step 9.mp4 |
28.99Мб |
| 23. Natural Language Processing in R - Step 9.vtt |
17.18Кб |
| 24. ANN in R - Step 3.mp4 |
28.94Мб |
| 24. ANN in R - Step 3.vtt |
16.38Кб |
| 24. Natural Language Processing in R - Step 10.mp4 |
41.19Мб |
| 24. Natural Language Processing in R - Step 10.vtt |
22.89Кб |
| 25. ANN in R - Step 4 (Last step).mp4 |
33.44Мб |
| 25. ANN in R - Step 4 (Last step).vtt |
17.95Кб |
| 25. Homework Challenge.html |
1.40Кб |
| 3.1 Eclat.zip.zip |
48.54Кб |
| 3. Accuracy Paradox.mp4 |
3.80Мб |
| 3. Accuracy Paradox.vtt |
2.94Кб |
| 3. Apriori in R - Step 1.mp4 |
42.87Мб |
| 3. Apriori in R - Step 1.vtt |
42.89Мб |
| 3. Decision Tree Classification in Python.mp4 |
29.80Мб |
| 3. Decision Tree Classification in Python.vtt |
17.21Кб |
| 3. Decision Tree Regression in Python.mp4 |
33.54Мб |
| 3. Decision Tree Regression in Python.vtt |
21.13Кб |
| 3. Eclat in R.mp4 |
20.68Мб |
| 3. Eclat in R.vtt |
14.11Кб |
| 3. Evaluating Regression Models Performance - Homework's Final Part.mp4 |
21.89Мб |
| 3. Evaluating Regression Models Performance - Homework's Final Part.vtt |
11.59Кб |
| 3. Hierarchical Clustering Using Dendrograms.mp4 |
22.81Мб |
| 3. Hierarchical Clustering Using Dendrograms.vtt |
15.86Кб |
| 3. How to get the dataset.mp4 |
11.71Мб |
| 3. How to get the dataset.mp4 |
11.72Мб |
| 3. How to get the dataset.mp4 |
11.71Мб |
| 3. How to get the dataset.vtt |
4.23Кб |
| 3. How to get the dataset.vtt |
4.23Кб |
| 3. How to get the dataset.vtt |
4.23Кб |
| 3. Importing the Libraries.mp4 |
11.08Мб |
| 3. Importing the Libraries.vtt |
6.98Кб |
| 3. Kernel PCA in R.mp4 |
56.57Мб |
| 3. Kernel PCA in R.vtt |
26.63Кб |
| 3. k-Fold Cross Validation in R.mp4 |
43.63Мб |
| 3. k-Fold Cross Validation in R.vtt |
24.24Кб |
| 3. K-Means Selecting The Number Of Clusters.mp4 |
23.13Мб |
| 3. K-Means Selecting The Number Of Clusters.vtt |
16.55Кб |
| 3. K-NN in Python.mp4 |
35.21Мб |
| 3. K-NN in Python.vtt |
18.76Кб |
| 3. LDA in Python.mp4 |
45.42Мб |
| 3. LDA in Python.vtt |
23.05Кб |
| 3. Logistic Regression in Python - Step 1.mp4 |
12.93Мб |
| 3. Logistic Regression in Python - Step 1.vtt |
2.26Мб |
| 3. Multiple Linear Regression Intuition - Step 1.mp4 |
1.82Мб |
| 3. Multiple Linear Regression Intuition - Step 1.vtt |
1.43Кб |
| 3. Naive Bayes Intuition (Challenge Reveal).mp4 |
13.28Мб |
| 3. Naive Bayes Intuition (Challenge Reveal).vtt |
8.57Кб |
| 3. PCA in Python - Step 1.mp4 |
31.96Мб |
| 3. PCA in Python - Step 1.vtt |
15.42Кб |
| 3. Polynomial Regression in Python - Step 1.mp4 |
24.89Мб |
| 3. Polynomial Regression in Python - Step 1.vtt |
15.69Кб |
| 3. Random Forest Classification in Python.mp4 |
47.15Мб |
| 3. Random Forest Classification in Python.vtt |
27.43Кб |
| 3. Random Forest Regression in Python.mp4 |
39.47Мб |
| 3. Random Forest Regression in Python.vtt |
24.45Кб |
| 3. Simple Linear Regression Intuition - Step 1.mp4 |
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| 3. Step 1 - Convolution Operation.mp4 |
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| 3. SVM in Python.mp4 |
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| 3. SVR in Python.mp4 |
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| 3. The Activation Function.mp4 |
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| 3. The Kernel Trick.mp4 |
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| 3. Why Machine Learning is the Future.mp4 |
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| 3. XGBoost in Python - Step 2.mp4 |
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| 4.1 SVM.zip.zip |
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| 4. Apriori in R - Step 2.mp4 |
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| 4. CAP Curve.mp4 |
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| 4. Decision Tree Classification in R.mp4 |
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| 4. Grid Search in Python - Step 1.mp4 |
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| 4. How do Neural Networks work.mp4 |
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| 4. How to get the dataset.mp4 |
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| 4. Important notes, tips & tricks for this course.html |
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| 4. Importing the Dataset.mp4 |
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| 4. Interpreting Linear Regression Coefficients.mp4 |
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| 4. K-NN in R.mp4 |
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| 4. LDA in R.mp4 |
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| 4. Logistic Regression in Python - Step 2.mp4 |
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| 4. Multiple Linear Regression Intuition - Step 2.mp4 |
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| 4. Naive Bayes Intuition (Extras).mp4 |
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| 4. Natural Language Processing in Python - Step 1.mp4 |
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| 4. PCA in Python - Step 2.mp4 |
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| 4. Polynomial Regression in Python - Step 2.mp4 |
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| 4. Random Forest Classification in R.mp4 |
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| 4. Simple Linear Regression Intuition - Step 2.mp4 |
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| 4. Step 1(b) - ReLU Layer.mp4 |
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| 4. Thompson Sampling in Python - Step 1.mp4 |
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| 4. Types of Kernel Functions.mp4 |
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| 4. Upper Confidence Bound in Python - Step 1.mp4 |
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| 4. XGBoost in R.mp4 |
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| 5.1 Machine_Learning_A_Z_Q_A.pdf.pdf |
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| 5. Apriori in R - Step 3.mp4 |
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| 5. CAP Curve Analysis.mp4 |
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| 5. Conclusion of Part 2 - Regression.html |
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| 5. HC in Python - Step 1.mp4 |
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| 5. How do Neural Networks learn.mp4 |
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| 5. How to get the dataset.mp4 |
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| 5. How to get the dataset.vtt |
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| 5. K-Means Clustering in Python.mp4 |
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| 5. K-Nearest Neighbor.html |
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| 5. Logistic Regression in Python - Step 3.mp4 |
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| 5. Multiple Linear Regression Intuition - Step 3.mp4 |
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| 5. Natural Language Processing in Python - Step 2.mp4 |
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| 5. PCA in Python - Step 3.mp4 |
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| 5. Polynomial Regression in Python - Step 3.mp4 |
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| 5. Simple Linear Regression in Python - Step 1.mp4 |
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| 5. Step 2 - Pooling.mp4 |
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| 5. THANK YOU bonus video.mp4 |
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| 5. This PDF resource will help you a lot.html |
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| 5. Thompson Sampling in Python - Step 2.mp4 |
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| 5. Upper Confidence Bound in Python - Step 2.mp4 |
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| 6.1 Machine_Learning_A-Z_New.zip.zip |
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| 6. Apriori in Python - Step 1.mp4 |
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| 6. Conclusion of Part 3 - Classification.html |
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| 6. Gradient Descent.mp4 |
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| 6. Grid Search in R.mp4 |
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| 6. HC in Python - Step 2.mp4 |
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| 6. Kernel SVM in Python.mp4 |
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| 6. Logistic Regression in Python - Step 4.mp4 |
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| 6. Missing Data.mp4 |
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| 6. Multiple Linear Regression Intuition - Step 4.mp4 |
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| 6. Naive Bayes in Python.mp4 |
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| 6. PCA in R - Step 1.mp4 |
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| 6. Polynomial Regression in Python - Step 4.mp4 |
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| 6. Simple Linear Regression in Python - Step 2.mp4 |
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| 6. Step 3 - Flattening.mp4 |
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| 6. The whole code folder of the course.html |
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| 6. Thompson Sampling in R - Step 1.mp4 |
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| 6. Upper Confidence Bound in Python - Step 3.mp4 |
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| 7. Categorical Data.mp4 |
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| 7. HC in Python - Step 3.mp4 |
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| 7. Kernel SVM in R.mp4 |
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| 7. K-Means Clustering.html |
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| 7. Logistic Regression in Python - Step 5.mp4 |
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| 7. Naive Bayes in R.mp4 |
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| 7. Natural Language Processing in Python - Step 4.mp4 |
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| 7. PCA in R - Step 2.mp4 |
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| 7. Prerequisites What is the P-Value.html |
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| 7. Python Regression Template.mp4 |
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| 7. Python Regression Template.vtt |
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| 7. Simple Linear Regression in Python - Step 3.mp4 |
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| 7. Simple Linear Regression in Python - Step 3.vtt |
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| 7. Step 4 - Full Connection.mp4 |
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| 7. Step 4 - Full Connection.vtt |
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| 7. Stochastic Gradient Descent.mp4 |
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| 7. Thompson Sampling in R - Step 2.mp4 |
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| 7. Updates on Udemy Reviews.mp4 |
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| 7. Upper Confidence Bound in Python - Step 4.mp4 |
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| 8. Apriori in Python - Step 3.mp4 |
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| 8. Backpropagation.mp4 |
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| 8. HC in Python - Step 4.mp4 |
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| 8. Installing Python and Anaconda (Mac, Linux & Windows).mp4 |
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| 8. Installing Python and Anaconda (Mac, Linux & Windows).vtt |
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| 8. Multiple Linear Regression Intuition - Step 5.mp4 |
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| 8. Natural Language Processing in Python - Step 5.mp4 |
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| 8. Natural Language Processing in Python - Step 5.vtt |
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| 8. PCA in R - Step 3.mp4 |
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| 8. Polynomial Regression in R - Step 1.mp4 |
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| 8. Python Classification Template.mp4 |
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| 8. Simple Linear Regression in Python - Step 4.mp4 |
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| 8. Summary.mp4 |
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| 8. Upper Confidence Bound in R - Step 1.mp4 |
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| 8. WARNING - Update.html |
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| 9. HC in Python - Step 5.mp4 |
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| 9. HC in Python - Step 5.vtt |
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| 9. How to get the dataset.mp4 |
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| 9. How to get the dataset.vtt |
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| 9. Logistic Regression in R - Step 1.mp4 |
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| 9. Logistic Regression in R - Step 1.vtt |
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| 9. Multiple Linear Regression in Python - Step 1.mp4 |
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| 9. Natural Language Processing in Python - Step 6.mp4 |
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| 9. Natural Language Processing in Python - Step 6.vtt |
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| 9. Polynomial Regression in R - Step 2.mp4 |
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| 9. Polynomial Regression in R - Step 2.vtt |
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| 9. Simple Linear Regression in R - Step 1.mp4 |
9.54Мб |
| 9. Simple Linear Regression in R - Step 1.vtt |
6.86Кб |
| 9. Softmax & Cross-Entropy.mp4 |
33.23Мб |
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| 9. Splitting the Dataset into the Training set and Test set.mp4 |
39.03Мб |
| 9. Splitting the Dataset into the Training set and Test set.vtt |
23.91Кб |
| 9. Update Recommended Anaconda Version.html |
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| 9. Upper Confidence Bound in R - Step 2.mp4 |
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| 9. Upper Confidence Bound in R - Step 2.vtt |
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