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