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

Creation Time	April 30, 2024, 3:09 p.m.
Last Access Time	Aug. 23, 2025, 1:31 p.m.
File Size	5.9 GB
Keywords	A-Z™ Data Python Hands-On - Science Udemy Machine R GigaCourse Learning In com
Total Requests	47
Total Files	575
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1. Welcome to the course!/6.1 Machine_Learning_A-Z_New.zip 228.4 MB
36. Kernel PCA/3. Kernel PCA in R.mp4 56.6 MB
1. Welcome to the course!/7. Updates on Udemy Reviews.mp4 52.9 MB
39. XGBoost/5. THANK YOU bonus video.mp4 52.2 MB
12. Logistic Regression/14. Logistic Regression in R - Step 5.mp4 51.7 MB
35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 51.3 MB
17. Decision Tree Classification/4. Decision Tree Classification in R.mp4 51.2 MB
18. Random Forest Classification/4. Random Forest Classification in R.mp4 49.4 MB
31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 48.1 MB
39. XGBoost/4. XGBoost in R.mp4 47.3 MB
27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4 47.2 MB
18. Random Forest Classification/3. Random Forest Classification in Python.mp4 47.2 MB
32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4 46.9 MB
7. Support Vector Regression (SVR)/2. SVR Intuition.mp4 46.6 MB
7. Support Vector Regression (SVR)/3. SVR in Python.mp4 46.2 MB
35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 45.4 MB
8. Decision Tree Regression/4. Decision Tree Regression in R.mp4 44.4 MB
16. Naive Bayes/1. Bayes Theorem.mp4 43.9 MB
24. Apriori/5. Apriori in R - Step 3.mp4 43.8 MB
38. Model Selection/3. k-Fold Cross Validation in R.mp4 43.6 MB
6. Polynomial Regression/11. Polynomial Regression in R - Step 3.mp4 43.3 MB
28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 43.1 MB
6. Polynomial Regression/6. Polynomial Regression in Python - Step 3.mp4 43.0 MB
24. Apriori/3. Apriori in R - Step 1.mp4 42.9 MB
32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 42.7 MB
12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 42.5 MB
15. Kernel SVM/6. Kernel SVM in Python.mp4 41.6 MB
13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 41.4 MB
29. Part 7 Natural Language Processing/24. Natural Language Processing in R - Step 10.mp4 41.2 MB
27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 41.1 MB
28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4 40.9 MB
2. Part 1 Data Preprocessing/7. Categorical Data.mp4 40.8 MB
15. Kernel SVM/7. Kernel SVM in R.mp4 40.5 MB
29. Part 7 Natural Language Processing/15. Natural Language Processing in R - Step 1.mp4 40.4 MB
9. Random Forest Regression/4. Random Forest Regression in R.mp4 40.3 MB
32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 40.2 MB
21. K-Means Clustering/5. K-Means Clustering in Python.mp4 39.8 MB
5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 39.7 MB
5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 39.6 MB
29. Part 7 Natural Language Processing/11. Natural Language Processing in Python - Step 8.mp4 39.5 MB
9. Random Forest Regression/3. Random Forest Regression in Python.mp4 39.5 MB
2. Part 1 Data Preprocessing/9. Splitting the Dataset into the Training set and Test set.mp4 39.0 MB
31. Artificial Neural Networks/22. ANN in R - Step 1.mp4 38.6 MB
38. Model Selection/4. Grid Search in Python - Step 1.mp4 38.2 MB
24. Apriori/6. Apriori in Python - Step 1.mp4 38.0 MB
4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 37.4 MB
16. Naive Bayes/7. Naive Bayes in R.mp4 37.3 MB
28. Thompson Sampling/1. Thompson Sampling Intuition.mp4 37.3 MB
34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4 36.7 MB
38. Model Selection/6. Grid Search in R.mp4 35.5 MB
27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 35.4 MB
13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 35.2 MB
29. Part 7 Natural Language Processing/4. Natural Language Processing in Python - Step 1.mp4 35.2 MB
24. Apriori/1. Apriori Intuition.mp4 35.0 MB
2. Part 1 Data Preprocessing/10. Feature Scaling.mp4 34.6 MB
8. Decision Tree Regression/3. Decision Tree Regression in Python.mp4 33.5 MB
31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp4 33.4 MB
36. Kernel PCA/2. Kernel PCA in Python.mp4 33.4 MB
32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 33.2 MB
38. Model Selection/2. k-Fold Cross Validation in Python.mp4 32.8 MB
5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 32.6 MB
14. Support Vector Machine (SVM)/4. SVM in R.mp4 32.3 MB
2. Part 1 Data Preprocessing/6. Missing Data.mp4 32.2 MB
34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 32.1 MB
39. XGBoost/3. XGBoost in Python - Step 2.mp4 32.0 MB
34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 31.9 MB
27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 31.5 MB
30. Part 8 Deep Learning/2. What is Deep Learning.mp4 31.3 MB
14. Support Vector Machine (SVM)/3. SVM in Python.mp4 31.2 MB
32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 31.0 MB
4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp4 30.8 MB
34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp4 30.6 MB
24. Apriori/4. Apriori in R - Step 2.mp4 30.5 MB
27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 30.2 MB
31. Artificial Neural Networks/2. The Neuron.mp4 29.9 MB
17. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 29.8 MB
29. Part 7 Natural Language Processing/2. Natural Language Processing Intuition.mp4 29.7 MB
31. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 29.6 MB
24. Apriori/7. Apriori in Python - Step 2.mp4 29.5 MB
38. Model Selection/5. Grid Search in Python - Step 2.mp4 29.5 MB
32. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 29.5 MB
27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 29.3 MB
31. Artificial Neural Networks/12. ANN in Python - Step 1.mp4 29.3 MB
15. Kernel SVM/3. The Kernel Trick.mp4 29.3 MB
12. Logistic Regression/1. Logistic Regression Intuition.mp4 29.2 MB
34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp4 29.0 MB
27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp4 29.0 MB
21. K-Means Clustering/6. K-Means Clustering in R.mp4 29.0 MB
29. Part 7 Natural Language Processing/23. Natural Language Processing in R - Step 9.mp4 29.0 MB
31. Artificial Neural Networks/24. ANN in R - Step 3.mp4 28.9 MB
5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp4 28.8 MB
27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp4 28.1 MB
16. Naive Bayes/2. Naive Bayes Intuition.mp4 27.8 MB
6. Polynomial Regression/8. Python Regression Template.mp4 27.4 MB
32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp4 27.2 MB
5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 27.2 MB
6. Polynomial Regression/5. Polynomial Regression in Python - Step 2.mp4 27.1 MB
35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 27.0 MB
24. Apriori/8. Apriori in Python - Step 3.mp4 27.0 MB
21. K-Means Clustering/1. K-Means Clustering Intuition.mp4 26.9 MB
31. Artificial Neural Networks/5. How do Neural Networks learn.mp4 26.6 MB
5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp4 25.9 MB
7. Support Vector Regression (SVR)/4. SVR in R.mp4 25.9 MB
34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp4 25.5 MB
6. Polynomial Regression/13. R Regression Template.mp4 25.4 MB
32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4 24.9 MB
6. Polynomial Regression/4. Polynomial Regression in Python - Step 1.mp4 24.9 MB
10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp4 24.2 MB
29. Part 7 Natural Language Processing/13. Natural Language Processing in Python - Step 10.mp4 24.1 MB
29. Part 7 Natural Language Processing/7. Natural Language Processing in Python - Step 4.mp4 24.0 MB
6. Polynomial Regression/10. Polynomial Regression in R - Step 2.mp4 23.9 MB
5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 23.8 MB
31. Artificial Neural Networks/4. How do Neural Networks work.mp4 23.5 MB
16. Naive Bayes/6. Naive Bayes in Python.mp4 23.4 MB
2. Part 1 Data Preprocessing/4. Importing the Dataset.mp4 23.3 MB
21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 23.1 MB
22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp4 22.8 MB
8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 22.7 MB
6. Polynomial Regression/12. Polynomial Regression in R - Step 4.mp4 22.3 MB
34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 22.1 MB
29. Part 7 Natural Language Processing/5. Natural Language Processing in Python - Step 2.mp4 22.0 MB
10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp4 21.9 MB
4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp4 21.7 MB
39. XGBoost/2. XGBoost in Python - Step 1.mp4 21.4 MB
2. Part 1 Data Preprocessing/2. Get the dataset.mp4 21.1 MB
25. Eclat/3. Eclat in R.mp4 20.7 MB
32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp4 20.6 MB
2. Part 1 Data Preprocessing/11. And here is our Data Preprocessing Template!.mp4 19.7 MB
1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).mp4 19.5 MB
18. Random Forest Classification/1. Random Forest Classification Intuition.mp4 19.4 MB
10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 19.3 MB
16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 18.9 MB
17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 18.8 MB
4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp4 18.8 MB
19. Evaluating Classification Models Performance/4. CAP Curve.mp4 18.7 MB
31. Artificial Neural Networks/6. Gradient Descent.mp4 18.5 MB
31. Artificial Neural Networks/19. ANN in Python - Step 8.mp4 18.2 MB
14. Support Vector Machine (SVM)/1. SVM Intuition.mp4 18.0 MB
5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp4 18.0 MB
6. Polynomial Regression/9. Polynomial Regression in R - Step 1.mp4 17.8 MB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp4 17.6 MB
29. Part 7 Natural Language Processing/16. Natural Language Processing in R - Step 2.mp4 17.5 MB
22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp4 17.5 MB
5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 17.2 MB
29. Part 7 Natural Language Processing/10. Natural Language Processing in Python - Step 7.mp4 17.1 MB
31. Artificial Neural Networks/21. ANN in Python - Step 10.mp4 17.1 MB
31. Artificial Neural Networks/20. ANN in Python - Step 9.mp4 16.9 MB
31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 16.8 MB
22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp4 16.5 MB
31. Artificial Neural Networks/10. Business Problem Description.mp4 16.4 MB
4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp4 15.6 MB
21. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 15.4 MB
29. Part 7 Natural Language Processing/8. Natural Language Processing in Python - Step 5.mp4 14.9 MB
31. Artificial Neural Networks/3. The Activation Function.mp4 14.8 MB
12. Logistic Regression/11. Logistic Regression in R - Step 3.mp4 14.6 MB
4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 14.4 MB
5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 14.3 MB
5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp4 14.3 MB
31. Artificial Neural Networks/23. ANN in R - Step 2.mp4 14.2 MB
32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14.1 MB
28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14.1 MB
29. Part 7 Natural Language Processing/12. Natural Language Processing in Python - Step 9.mp4 14.0 MB
9. Random Forest Regression/1. Random Forest Regression Intuition.mp4 13.8 MB
15. Kernel SVM/2. Mapping to a higher dimension.mp4 13.7 MB
19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 13.6 MB
29. Part 7 Natural Language Processing/17. Natural Language Processing in R - Step 3.mp4 13.5 MB
6. Polynomial Regression/7. Polynomial Regression in Python - Step 4.mp4 13.5 MB
16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13.3 MB
29. Part 7 Natural Language Processing/22. Natural Language Processing in R - Step 8.mp4 13.3 MB
32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp4 12.9 MB
12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 12.9 MB
1. Welcome to the course!/3. Why Machine Learning is the Future.mp4 12.8 MB
29. Part 7 Natural Language Processing/20. Natural Language Processing in R - Step 6.mp4 12.7 MB
22. Hierarchical Clustering/6. HC in Python - Step 2.mp4 12.7 MB
12. Logistic Regression/9. Logistic Regression in R - Step 1.mp4 12.6 MB
12. Logistic Regression/15. R Classification Template.mp4 12.5 MB
15. Kernel SVM/4. Types of Kernel Functions.mp4 12.3 MB
22. Hierarchical Clustering/7. HC in Python - Step 3.mp4 12.3 MB
12. Logistic Regression/8. Python Classification Template.mp4 12.1 MB
22. Hierarchical Clustering/8. HC in Python - Step 4.mp4 12.0 MB
16. Naive Bayes/5. How to get the dataset.mp4 11.7 MB
18. Random Forest Classification/2. How to get the dataset.mp4 11.7 MB
21. K-Means Clustering/4. How to get the dataset.mp4 11.7 MB
22. Hierarchical Clustering/4. How to get the dataset.mp4 11.7 MB
31. Artificial Neural Networks/9. How to get the dataset.mp4 11.7 MB
32. Convolutional Neural Networks/10. How to get the dataset.mp4 11.7 MB
38. Model Selection/1. How to get the dataset.mp4 11.7 MB
4. Simple Linear Regression/1. How to get the dataset.mp4 11.7 MB
9. Random Forest Regression/2. How to get the dataset.mp4 11.7 MB
12. Logistic Regression/2. How to get the dataset.mp4 11.7 MB
13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp4 11.7 MB
14. Support Vector Machine (SVM)/2. How to get the dataset.mp4 11.7 MB
15. Kernel SVM/5. How to get the dataset.mp4 11.7 MB
17. Decision Tree Classification/2. How to get the dataset.mp4 11.7 MB
24. Apriori/2. How to get the dataset.mp4 11.7 MB
25. Eclat/2. How to get the dataset.mp4 11.7 MB
27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4 11.7 MB
28. Thompson Sampling/3. How to get the dataset.mp4 11.7 MB
29. Part 7 Natural Language Processing/3. How to get the dataset.mp4 11.7 MB
34. Principal Component Analysis (PCA)/2. How to get the dataset.mp4 11.7 MB
35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp4 11.7 MB
36. Kernel PCA/1. How to get the dataset.mp4 11.7 MB
39. XGBoost/1. How to get the dataset.mp4 11.7 MB
5. Multiple Linear Regression/1. How to get the dataset.mp4 11.7 MB
6. Polynomial Regression/2. How to get the dataset.mp4 11.7 MB
7. Support Vector Regression (SVR)/1. How to get the dataset.mp4 11.7 MB
8. Decision Tree Regression/2. How to get the dataset.mp4 11.7 MB
19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 11.5 MB
22. Hierarchical Clustering/11. HC in R - Step 2.mp4 11.2 MB
2. Part 1 Data Preprocessing/3. Importing the Libraries.mp4 11.1 MB
31. Artificial Neural Networks/8. Backpropagation.mp4 10.9 MB
22. Hierarchical Clustering/5. HC in Python - Step 1.mp4 10.7 MB
25. Eclat/1. Eclat Intuition.mp4 10.6 MB
5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp4 10.4 MB
12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 10.4 MB
5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp4 10.0 MB
32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp4 9.9 MB
32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp4 9.7 MB
4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 9.5 MB
4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp4 9.5 MB
6. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9.4 MB
13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 9.3 MB
27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 9.1 MB
31. Artificial Neural Networks/18. ANN in Python - Step 7.mp4 9.0 MB
10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 8.9 MB
4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 8.6 MB
28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 8.4 MB
22. Hierarchical Clustering/9. HC in Python - Step 5.mp4 8.4 MB
31. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 8.4 MB
12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 8.2 MB
19. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 8.2 MB
1. Welcome to the course!/1. Applications of Machine Learning.mp4 8.0 MB
32. Convolutional Neural Networks/8. Summary.mp4 7.9 MB
12. Logistic Regression/10. Logistic Regression in R - Step 2.mp4 7.8 MB
22. Hierarchical Clustering/12. HC in R - Step 3.mp4 7.8 MB
29. Part 7 Natural Language Processing/21. Natural Language Processing in R - Step 7.mp4 7.5 MB
28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp4 7.5 MB
22. Hierarchical Clustering/13. HC in R - Step 4.mp4 7.4 MB
27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp4 7.4 MB
22. Hierarchical Clustering/10. HC in R - Step 1.mp4 7.4 MB
5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 7.2 MB
31. Artificial Neural Networks/17. ANN in Python - Step 6.mp4 7.1 MB
12. Logistic Regression/12. Logistic Regression in R - Step 4.mp4 6.9 MB
22. Hierarchical Clustering/14. HC in R - Step 5.mp4 6.9 MB
32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp4 6.8 MB
4. Simple Linear Regression/2. Dataset + Business Problem Description.mp4 6.6 MB
29. Part 7 Natural Language Processing/18. Natural Language Processing in R - Step 4.mp4 6.5 MB
29. Part 7 Natural Language Processing/9. Natural Language Processing in Python - Step 6.mp4 6.5 MB
12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 6.0 MB
32. Convolutional Neural Networks/1. Plan of attack.mp4 5.9 MB
31. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 5.9 MB
32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp4 5.9 MB
15. Kernel SVM/1. Kernel SVM Intuition.mp4 5.8 MB
4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp4 5.4 MB
31. Artificial Neural Networks/1. Plan of attack.mp4 4.7 MB
29. Part 7 Natural Language Processing/19. Natural Language Processing in R - Step 5.mp4 4.6 MB
5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp4 4.5 MB
19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 3.8 MB
29. Part 7 Natural Language Processing/6. Natural Language Processing in Python - Step 3.mp4 3.4 MB
32. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3.3 MB
2. Part 1 Data Preprocessing/1. Welcome to Part 1 - Data Preprocessing.mp4 3.0 MB
1. Welcome to the course!/5.1 Machine_Learning_A_Z_Q_A.pdf 2.3 MB
32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp4 2.2 MB
5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp4 1.8 MB
5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp4 1.8 MB
25. Eclat/3.1 Eclat.zip 48.5 KB
16. Naive Bayes/1. Bayes Theorem.srt 34.5 KB
18. Random Forest Classification/4. Random Forest Classification in R.srt 32.4 KB
8. Decision Tree Regression/4. Decision Tree Regression in R.srt 32.1 KB
6. Polynomial Regression/6. Polynomial Regression in Python - Step 3.srt 31.4 KB
24. Apriori/5. Apriori in R - Step 3.srt 31.2 KB
24. Apriori/3. Apriori in R - Step 1.srt 31.1 KB
7. Support Vector Regression (SVR)/3. SVR in Python.srt 30.9 KB
6. Polynomial Regression/11. Polynomial Regression in R - Step 3.srt 30.9 KB
36. Kernel PCA/3. Kernel PCA in R.srt 30.8 KB
18. Random Forest Classification/3. Random Forest Classification in Python.srt 30.7 KB
12. Logistic Regression/7. Logistic Regression in Python - Step 5.srt 29.7 KB
35. Linear Discriminant Analysis (LDA)/4. LDA in R.srt 29.7 KB
32. Convolutional Neural Networks/20. CNN in Python - Step 9.srt 29.4 KB
17. Decision Tree Classification/4. Decision Tree Classification in R.srt 29.1 KB
12. Logistic Regression/14. Logistic Regression in R - Step 5.srt 29.1 KB
31. Artificial Neural Networks/13. ANN in Python - Step 2.srt 29.0 KB
28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.srt 28.9 KB
32. Convolutional Neural Networks/7. Step 4 - Full Connection.srt 28.6 KB
15. Kernel SVM/6. Kernel SVM in Python.srt 28.2 KB
21. K-Means Clustering/5. K-Means Clustering in Python.srt 28.2 KB
9. Random Forest Regression/4. Random Forest Regression in R.srt 28.1 KB
24. Apriori/6. Apriori in Python - Step 1.srt 28.0 KB
38. Model Selection/3. k-Fold Cross Validation in R.srt 27.9 KB
28. Thompson Sampling/6. Thompson Sampling in R - Step 1.srt 27.9 KB
9. Random Forest Regression/3. Random Forest Regression in Python.srt 27.5 KB
28. Thompson Sampling/1. Thompson Sampling Intuition.srt 27.5 KB
5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.srt 27.5 KB
2. Part 1 Data Preprocessing/7. Categorical Data.srt 27.0 KB
27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.srt 26.9 KB
2. Part 1 Data Preprocessing/9. Splitting the Dataset into the Training set and Test set.srt 26.9 KB
31. Artificial Neural Networks/22. ANN in R - Step 1.srt 26.8 KB
35. Linear Discriminant Analysis (LDA)/3. LDA in Python.srt 26.5 KB
29. Part 7 Natural Language Processing/24. Natural Language Processing in R - Step 10.srt 26.3 KB
39. XGBoost/4. XGBoost in R.srt 26.0 KB
24. Apriori/1. Apriori Intuition.srt 25.9 KB
22. Hierarchical Clustering/16.1 Clustering-Pros-Cons.pdf 25.8 KB
15. Kernel SVM/7. Kernel SVM in R.srt 25.4 KB
27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.srt 25.3 KB
32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.srt 25.3 KB
27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.srt 25.3 KB
31. Artificial Neural Networks/2. The Neuron.srt 25.0 KB
5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.srt 24.4 KB
29. Part 7 Natural Language Processing/15. Natural Language Processing in R - Step 1.srt 24.0 KB
12. Logistic Regression/1. Logistic Regression Intuition.srt 23.9 KB
4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.srt 23.9 KB
29. Part 7 Natural Language Processing/11. Natural Language Processing in Python - Step 8.srt 23.8 KB
8. Decision Tree Regression/3. Decision Tree Regression in Python.srt 23.7 KB
5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.srt 23.6 KB
2. Part 1 Data Preprocessing/10. Feature Scaling.srt 23.5 KB
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40. Bonus Lectures/1. YOUR SPECIAL BONUS.html 4.7 KB
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1. Welcome to the course!/13. FAQBot!.html 1.8 KB
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29. Part 7 Natural Language Processing/14. Homework Challenge.html 1.4 KB
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26. Part 6 Reinforcement Learning/1. Welcome to Part 6 - Reinforcement Learning.html 1.2 KB
1. Welcome to the course!/11. BONUS Meet your instructors.html 1.0 KB
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