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文档列表

37. Convolutional Neural Networks/10.1 Section 40 - Convolutional Neural Networks (CNN).zip 234.9 MB
26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.srt 175.4 MB
29. Apriori/6. Apriori in Python - Step 4.mp4 172.3 MB
37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.mp4 160.2 MB
43. Model Selection/3. Grid Search in Python.mp4 159.2 MB
17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 153.7 MB
22. Random Forest Classification/3. Random Forest Classification in Python.srt 149.4 MB
23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.mp4 142.6 MB
27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.mp4 142.5 MB
13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.mp4 129.6 MB
26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.mp4 126.4 MB
16. Logistic Regression/9. Logistic Regression in Python - Step 7.mp4 124.4 MB
37. Convolutional Neural Networks/13. CNN in Python - Step 3.mp4 124.3 MB
39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 118.4 MB
43. Model Selection/2. k-Fold Cross Validation in Python.mp4 117.8 MB
36. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 116.4 MB
21. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 113.3 MB
29. Apriori/4. Apriori in Python - Step 2.mp4 112.9 MB
37. Convolutional Neural Networks/12. CNN in Python - Step 2.mp4 112.1 MB
18. Support Vector Machine (SVM)/4. SVM in Python.mp4 109.9 MB
34/5. Bag-Of-Words Model.mp4 108.5 MB
40. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 107.0 MB
3. Data Preprocessing in Python/9. Feature Scaling.mp4 106.7 MB
36. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 106.3 MB
20. Naive Bayes/6. Naive Bayes in Python.mp4 105.3 MB
37. Convolutional Neural Networks/15. CNN in Python - Step 5.mp4 102.4 MB
22. Random Forest Classification/3. Random Forest Classification in Python.mp4 101.4 MB
1. Welcome to the course!/9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.mp4 99.4 MB
16. Logistic Regression/15. Logistic Regression in R - Step 5.mp4 98.3 MB
9. Support Vector Regression (SVR)/8. SVR in Python - Step 5.mp4 98.2 MB
44. XGBoost/2. XGBoost in Python.mp4 94.4 MB
34/11. Natural Language Processing in Python - Step 5.mp4 94.0 MB
3. Data Preprocessing in Python/7. Encoding Categorical Data.mp4 92.9 MB
19. Kernel SVM/7. Kernel SVM in Python.mp4 92.7 MB
9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.mp4 91.1 MB
4. Data Preprocessing in R/8. Splitting the dataset into the Training set and Test set.mp4 90.7 MB
32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 89.5 MB
16. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 88.8 MB
34/4. Classical vs Deep Learning Models.mp4 88.0 MB
20. Naive Bayes/7. Naive Bayes in R.srt 86.3 MB
26. K-Means Clustering/7. K-Means Clustering in Python - Step 3.mp4 85.3 MB
4. Data Preprocessing in R/9. Feature Scaling.mp4 82.7 MB
33. Thompson Sampling/6. Thompson Sampling in Python - Step 3.mp4 82.5 MB
8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 81.6 MB
41. Kernel PCA/2. Kernel PCA in Python.mp4 81.3 MB
30. Eclat/3. Eclat in Python.mp4 79.2 MB
27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.mp4 78.9 MB
36. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 78.7 MB
6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.mp4 78.2 MB
11. Random Forest Regression/3. Random Forest Regression in Python.mp4 78.0 MB
7. Multiple Linear Regression/12. Multiple Linear Regression in Python - Step 4.mp4 76.0 MB
3. Data Preprocessing in Python/4. Importing the Dataset.mp4 75.3 MB
37. Convolutional Neural Networks/11. CNN in Python - Step 1.mp4 74.2 MB
33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 73.4 MB
29. Apriori/3. Apriori in Python - Step 1.mp4 73.2 MB
8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4 72.7 MB
29. Apriori/5. Apriori in Python - Step 3.mp4 72.6 MB
3. Data Preprocessing in Python/6. Taking care of Missing Data.mp4 72.4 MB
21. Decision Tree Classification/4. Decision Tree Classification in R.mp4 71.5 MB
3. Data Preprocessing in Python/8. Splitting the dataset into the Training set and Test set.mp4 70.9 MB
36. Artificial Neural Networks/11. ANN in Python - Step 1.mp4 69.7 MB
19. Kernel SVM/5. Non-Linear Kernel SVR (Advanced).mp4 68.8 MB
36. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 68.5 MB
18. Support Vector Machine (SVM)/5. SVM in R.mp4 68.5 MB
22. Random Forest Classification/4. Random Forest Classification in R.mp4 67.2 MB
7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 65.4 MB
34/9. Natural Language Processing in Python - Step 3.mp4 63.5 MB
34/10. Natural Language Processing in Python - Step 4.mp4 63.0 MB
32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 61.6 MB
8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4 61.1 MB
7. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 61.0 MB
32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.mp4 60.7 MB
4. Data Preprocessing in R/7. Encoding Categorical Data.mp4 60.1 MB
13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.mp4 59.5 MB
41. Kernel PCA/3. Kernel PCA in R.mp4 59.3 MB
29. Apriori/9. Apriori in R - Step 3.mp4 59.3 MB
7. Multiple Linear Regression/6. Understanding the P-Value.mp4 59.2 MB
10. Decision Tree Regression/7. Decision Tree Regression in R.mp4 59.0 MB
17. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 58.5 MB
8. Polynomial Regression/9. Polynomial Regression in R - Step 3.mp4 57.5 MB
10. Decision Tree Regression/6. Decision Tree Regression in Python - Step 4.mp4 57.4 MB
3. Data Preprocessing in Python/2. Getting Started.mp4 57.0 MB
34/24. Natural Language Processing in R - Step 10.mp4 56.8 MB
26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.mp4 56.7 MB
16. Logistic Regression/8. Logistic Regression in Python - Step 6.mp4 55.5 MB
34/12. Natural Language Processing in Python - Step 6.mp4 55.5 MB
29. Apriori/7. Apriori in R - Step 1.mp4 55.4 MB
19. Kernel SVM/8. Kernel SVM in R.mp4 55.4 MB
44. XGBoost/5. THANK YOU Bonus Video.mp4 54.8 MB
11. Random Forest Regression/4. Random Forest Regression in R.mp4 54.4 MB
40. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 53.8 MB
34/15. Natural Language Processing in R - Step 1.mp4 53.7 MB
33. Thompson Sampling/9. Thompson Sampling in R - Step 1.mp4 53.5 MB
7. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 53.4 MB
7. Multiple Linear Regression/18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 53.3 MB
4. Data Preprocessing in R/10. Data Preprocessing Template.mp4 53.2 MB
20. Naive Bayes/1. Bayes Theorem.mp4 52.9 MB
36. Artificial Neural Networks/17. ANN in R - Step 1.mp4 52.3 MB
20. Naive Bayes/7. Naive Bayes in R.mp4 52.2 MB
6. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 51.5 MB
6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.mp4 51.0 MB
44. XGBoost/4. XGBoost in R.mp4 49.6 MB
9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.mp4 48.5 MB
7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.mp4 47.4 MB
16. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 47.4 MB
32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.mp4 47.1 MB
33. Thompson Sampling/7. Thompson Sampling in Python - Step 4.mp4 46.8 MB
16. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 46.8 MB
36. Artificial Neural Networks/20. ANN in R - Step 4 (Last step).mp4 45.9 MB
43. Model Selection/4. k-Fold Cross Validation in R.mp4 45.8 MB
32. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in Python - Step 7.mp4 45.4 MB
16. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 45.1 MB
37. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 44.8 MB
9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.mp4 44.6 MB
10. Decision Tree Regression/3. Decision Tree Regression in Python - Step 1.mp4 44.5 MB
39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 42.8 MB
34/8. Natural Language Processing in Python - Step 2.mp4 42.4 MB
37. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 42.2 MB
27. Hierarchical Clustering/6. Hierarchical Clustering in Python - Step 1.mp4 42.2 MB
37. Convolutional Neural Networks/14. CNN in Python - Step 4.mp4 42.0 MB
6. Simple Linear Regression/5. Simple Linear Regression in Python - Step 2.mp4 41.8 MB
4. Data Preprocessing in R/6. Taking care of Missing Data.mp4 41.7 MB
29. Apriori/8. Apriori in R - Step 2.mp4 40.7 MB
8. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4 40.7 MB
32. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 40.3 MB
26. K-Means Clustering/5. K-Means Clustering in Python - Step 1.mp4 39.9 MB
36. Artificial Neural Networks/19. ANN in R - Step 3.mp4 39.7 MB
34/23. Natural Language Processing in R - Step 9.mp4 39.5 MB
33. Thompson Sampling/1. Thompson Sampling Intuition.mp4 39.1 MB
26. K-Means Clustering/10. K-Means Clustering in R.mp4 38.7 MB
9. Support Vector Regression (SVR)/1. SVR Intuition (Updated!).mp4 38.6 MB
39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.mp4 38.5 MB
43. Model Selection/5. Grid Search in R.mp4 37.3 MB
26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.mp4 36.8 MB
29. Apriori/1. Apriori Intuition.mp4 36.7 MB
9. Support Vector Regression (SVR)/6. SVR in Python - Step 3.mp4 36.5 MB
19. Kernel SVM/3. The Kernel Trick.mp4 36.4 MB
32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.mp4 35.8 MB
34/7. Natural Language Processing in Python - Step 1.mp4 35.7 MB
32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.mp4 35.7 MB
9. Support Vector Regression (SVR)/9. SVR in R.mp4 35.4 MB
37. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 34.9 MB
7. Multiple Linear Regression/7. Multiple Linear Regression Intuition - Step 5.mp4 34.4 MB
32. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in Python - Step 5.mp4 34.0 MB
8. Polynomial Regression/8. Polynomial Regression in R - Step 2.mp4 33.9 MB
18. Support Vector Machine (SVM)/4. SVM in Python.srt 33.8 MB
39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 33.7 MB
8. Polynomial Regression/11. R Regression Template.mp4 32.9 MB
35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4 32.8 MB
20. Naive Bayes/2. Naive Bayes Intuition.mp4 32.6 MB
37. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 32.5 MB
39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.mp4 32.1 MB
33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 32.1 MB
16. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 32.1 MB
32. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 31.7 MB
26. K-Means Clustering/1. K-Means Clustering Intuition.mp4 31.4 MB
36. Artificial Neural Networks/2. The Neuron.mp4 31.3 MB
37. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 30.9 MB
32. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 30.8 MB
36. Artificial Neural Networks/9. Business Problem Description.mp4 30.7 MB
16. Logistic Regression/1. Logistic Regression Intuition.mp4 30.6 MB
39. Principal Component Analysis (PCA)/6. PCA in R - Step 2.mp4 30.4 MB
8. Polynomial Regression/10. Polynomial Regression in R - Step 4.mp4 29.9 MB
14. Regression Model Selection in R/1. Evaluating Regression Models Performance - Homework's Final Part.mp4 29.7 MB
6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.mp4 29.6 MB
16. Logistic Regression/12. Logistic Regression in R - Step 3.mp4 28.8 MB
14. Regression Model Selection in R/2. Interpreting Linear Regression Coefficients.mp4 28.7 MB
40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 28.3 MB
36. Artificial Neural Networks/5. How do Neural Networks learn.mp4 27.9 MB
10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.srt 27.5 MB
10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.mp4 27.5 MB
26. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 26.9 MB
22. Random Forest Classification/1. Random Forest Classification Intuition.mp4 26.9 MB
10. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 26.6 MB
30. Eclat/4. Eclat in R.mp4 26.5 MB
6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 26.1 MB
36. Artificial Neural Networks/4. How do Neural Networks work.mp4 24.7 MB
7. Multiple Linear Regression/15. Multiple Linear Regression in R - Step 1.mp4 24.6 MB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp4 24.3 MB
27. Hierarchical Clustering/4. Hierarchical Clustering Using Dendrograms.mp4 23.9 MB
34/3. Types of Natural Language Processing.mp4 23.6 MB
6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.srt 23.5 MB
7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 23.0 MB
34/16. Natural Language Processing in R - Step 2.mp4 22.7 MB
21. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 22.7 MB
12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 22.5 MB
8. Polynomial Regression/7. Polynomial Regression in R - Step 1.mp4 22.2 MB
24. Evaluating Classification Models Performance/4. CAP Curve.mp4 21.3 MB
18. Support Vector Machine (SVM)/2. SVM Intuition.mp4 20.9 MB
9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.mp4 20.7 MB
10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.mp4 20.4 MB
20. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 19.9 MB
36. Artificial Neural Networks/6. Gradient Descent.mp4 19.4 MB
36. Artificial Neural Networks/18. ANN in R - Step 2.mp4 19.1 MB
32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 18.6 MB
16. Logistic Regression/16. R Classification Template.mp4 18.4 MB
27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.mp4 18.3 MB
34/22. Natural Language Processing in R - Step 8.mp4 18.1 MB
34/17. Natural Language Processing in R - Step 3.mp4 17.7 MB
36. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 17.6 MB
7. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 3.mp4 17.4 MB
27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.mp4 17.3 MB
4. Data Preprocessing in R/5. Importing the Dataset.mp4 17.2 MB
34/20. Natural Language Processing in R - Step 6.mp4 16.9 MB
3. Data Preprocessing in Python/3. Importing the Libraries.mp4 16.7 MB
19. Kernel SVM/4. Types of Kernel Functions.mp4 16.5 MB
16. Logistic Regression/10. Logistic Regression in R - Step 1.mp4 16.5 MB
11. Random Forest Regression/1. Random Forest Regression Intuition.mp4 16.4 MB
19. Kernel SVM/2. Mapping to a higher dimension.mp4 16.2 MB
26. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 16.1 MB
24. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 15.9 MB
16. Logistic Regression/11. Logistic Regression in R - Step 2.mp4 15.6 MB
36. Artificial Neural Networks/3. The Activation Function.mp4 15.5 MB
1. Welcome to the course!/5. Why Machine Learning is the Future.mp4 15.2 MB
37. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14.8 MB
33. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14.8 MB
27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.mp4 14.6 MB
7. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 3.mp4 14.5 MB
27. Hierarchical Clustering/13. Hierarchical Clustering in R - Step 5.mp4 14.3 MB
20. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13.9 MB
24. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 13.6 MB
34/2. NLP Intuition.mp4 13.3 MB
7. Multiple Linear Regression/1. Dataset + Business Problem Description.mp4 13.2 MB
4. Data Preprocessing in R/4. Dataset Description.mp4 12.4 MB
16. Logistic Regression/13. Logistic Regression in R - Step 4.mp4 12.3 MB
6. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 12.1 MB
6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 12.0 MB
36. Artificial Neural Networks/8. Backpropagation.mp4 11.5 MB
30. Eclat/1. Eclat Intuition.mp4 11.2 MB
6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.mp4 11.0 MB
17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 11.0 MB
27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.mp4 10.7 MB
27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.mp4 10.4 MB
1. Welcome to the course!/1. Applications of Machine Learning.mp4 10.3 MB
4. Data Preprocessing in R/2. Getting Started.mp4 10.3 MB
12. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 10.3 MB
34/21. Natural Language Processing in R - Step 7.mp4 10.1 MB
33. Thompson Sampling/10. Thompson Sampling in R - Step 2.mp4 10.0 MB
32. Upper Confidence Bound (UCB)/14. Upper Confidence Bound in R - Step 4.mp4 10.0 MB
8. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9.9 MB
24. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 9.3 MB
27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.srt 9.0 MB
27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.mp4 9.0 MB
34/18. Natural Language Processing in R - Step 4.mp4 8.6 MB
37. Convolutional Neural Networks/8. Summary.mp4 8.3 MB
19. Kernel SVM/1. Kernel SVM Intuition.mp4 6.7 MB
6. Simple Linear Regression/2. Simple Linear Regression Intuition - Step 2.mp4 6.3 MB
37. Convolutional Neural Networks/1. Plan of attack.mp4 6.2 MB
34/19. Natural Language Processing in R - Step 5.mp4 6.1 MB
7. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 4.mp4 5.6 MB
1. Welcome to the course!/8.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
11. Random Forest Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
17. K-Nearest Neighbors (K-NN)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
19. Kernel SVM/6.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
20. Naive Bayes/5.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
21. Decision Tree Classification/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
22. Random Forest Classification/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
27. Hierarchical Clustering/5.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
3. Data Preprocessing in Python/1.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
30. Eclat/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
32. Upper Confidence Bound (UCB)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
34/6.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
36. Artificial Neural Networks/10.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
41. Kernel PCA/1.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
43. Model Selection/1.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
44. XGBoost/1.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
6. Simple Linear Regression/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
10. Decision Tree Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
16. Logistic Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
18. Support Vector Machine (SVM)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
26. K-Means Clustering/4.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
29. Apriori/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
33. Thompson Sampling/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
39. Principal Component Analysis (PCA)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
40. Linear Discriminant Analysis (LDA)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
7. Multiple Linear Regression/8.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
8. Polynomial Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
9. Support Vector Regression (SVR)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.5 MB
36. Artificial Neural Networks/1. Plan of attack.mp4 5.0 MB
24. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 4.4 MB
37. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3.4 MB
1. Welcome to the course!/7.1 Machine_Learning_A_Z_Q_A.pdf 2.4 MB
7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.mp4 2.1 MB
7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.mp4 2.1 MB
13. Regression Model Selection in Python/4.1 Regression_Bonus.zip 373.2 kB
14. Regression Model Selection in R/3.1 Regression_Bonus.zip 373.2 kB
13. Regression Model Selection in Python/1.1 Machine Learning A-Z (Model Selection).zip 163.8 kB
23. Classification Model Selection in Python/1.1 Machine Learning A-Z (Model Selection).zip 163.8 kB
30. Eclat/4.1 Eclat.zip 49.7 kB
37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.srt 39.7 kB
43. Model Selection/3. Grid Search in Python.srt 35.4 kB
20. Naive Bayes/1. Bayes Theorem.srt 35.3 kB
23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.srt 35.3 kB
22. Random Forest Classification/4. Random Forest Classification in R.srt 33.2 kB
10. Decision Tree Regression/7. Decision Tree Regression in R.srt 32.9 kB
29. Apriori/6. Apriori in Python - Step 4.srt 32.0 kB
29. Apriori/9. Apriori in R - Step 3.srt 31.9 kB
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8. Polynomial Regression/9. Polynomial Regression in R - Step 3.srt 31.6 kB
41. Kernel PCA/3. Kernel PCA in R.srt 31.5 kB
17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.srt 31.5 kB
3. Data Preprocessing in Python/9. Feature Scaling.srt 31.0 kB
13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.srt 30.9 kB
40. Linear Discriminant Analysis (LDA)/4. LDA in R.srt 30.4 kB
24. Evaluating Classification Models Performance/6.1 Classification_Pros_Cons.pdf 30.0 kB
21. Decision Tree Classification/4. Decision Tree Classification in R.srt 29.8 kB
16. Logistic Regression/15. Logistic Regression in R - Step 5.srt 29.8 kB
26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.srt 29.8 kB
37. Convolutional Neural Networks/12. CNN in Python - Step 2.srt 29.7 kB
37. Convolutional Neural Networks/13. CNN in Python - Step 3.srt 29.6 kB
43. Model Selection/2. k-Fold Cross Validation in Python.srt 29.3 kB
37. Convolutional Neural Networks/7. Step 4 - Full Connection.srt 29.3 kB
34/5. Bag-Of-Words Model.srt 29.0 kB
1. Welcome to the course!/9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.srt 28.9 kB
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43. Model Selection/4. k-Fold Cross Validation in R.srt 28.6 kB
33. Thompson Sampling/9. Thompson Sampling in R - Step 1.srt 28.5 kB
33. Thompson Sampling/1. Thompson Sampling Intuition.srt 28.2 kB
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39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.srt 27.1 kB
29. Apriori/4. Apriori in Python - Step 2.srt 27.0 kB
34/11. Natural Language Processing in Python - Step 5.srt 27.0 kB
34/24. Natural Language Processing in R - Step 10.srt 26.9 kB
27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.srt 26.8 kB
44. XGBoost/4. XGBoost in R.srt 26.6 kB
29. Apriori/1. Apriori Intuition.srt 26.5 kB
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19. Kernel SVM/8. Kernel SVM in R.srt 26.1 kB
32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.srt 25.9 kB
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32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.srt 25.7 kB
36. Artificial Neural Networks/2. The Neuron.srt 25.6 kB
3. Data Preprocessing in Python/4. Importing the Dataset.srt 24.7 kB
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17. K-Nearest Neighbors (K-NN)/4. K-NN in R.srt 23.9 kB
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20. Naive Bayes/2. Naive Bayes Intuition.srt 23.9 kB
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44. XGBoost/2. XGBoost in Python.srt 23.6 kB
29. Apriori/8. Apriori in R - Step 2.srt 23.6 kB
16. Logistic Regression/9. Logistic Regression in Python - Step 7.srt 23.1 kB
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21. Decision Tree Classification/3. Decision Tree Classification in Python.srt 22.8 kB
20. Naive Bayes/6. Naive Bayes in Python.srt 22.8 kB
32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.srt 22.7 kB
9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.srt 22.7 kB
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3. Data Preprocessing in Python/7. Encoding Categorical Data.srt 22.5 kB
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16. Logistic Regression/4. Logistic Regression in Python - Step 2.srt 21.9 kB
11. Random Forest Regression/3. Random Forest Regression in Python.srt 21.6 kB
37. Convolutional Neural Networks/5. Step 2 - Pooling.srt 21.5 kB
43. Model Selection/5. Grid Search in R.srt 21.4 kB
8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.srt 21.3 kB
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32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.srt 21.1 kB
32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.srt 21.0 kB
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19. Kernel SVM/7. Kernel SVM in Python.srt 20.9 kB
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8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.srt 20.4 kB
6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.srt 20.2 kB
39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.srt 20.2 kB
34/23. Natural Language Processing in R - Step 9.srt 20.1 kB
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6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.srt 19.9 kB
29. Apriori/5. Apriori in Python - Step 3.srt 19.7 kB
34/9. Natural Language Processing in Python - Step 3.srt 19.6 kB
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36. Artificial Neural Networks/5. How do Neural Networks learn.srt 19.4 kB
36. Artificial Neural Networks/19. ANN in R - Step 3.srt 19.3 kB
30. Eclat/3. Eclat in Python.srt 19.3 kB
39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.srt 19.1 kB
9. Support Vector Regression (SVR)/9. SVR in R.srt 19.1 kB
8. Polynomial Regression/11. R Regression Template.srt 19.1 kB
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18. Support Vector Machine (SVM)/5. SVM in R.srt 18.8 kB
37. Convolutional Neural Networks/11. CNN in Python - Step 1.srt 18.7 kB
27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.srt 18.6 kB
35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.srt 18.6 kB
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33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.srt 18.3 kB
8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.srt 18.0 kB
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41. Kernel PCA/2. Kernel PCA in Python.srt 17.9 kB
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34/10. Natural Language Processing in Python - Step 4.srt 17.2 kB
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19. Kernel SVM/3. The Kernel Trick.srt 16.9 kB
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30. Eclat/4. Eclat in R.srt 16.2 kB
18. Support Vector Machine (SVM)/2. SVM Intuition.srt 16.1 kB
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8. Polynomial Regression/10. Polynomial Regression in R - Step 4.srt 15.8 kB
7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.srt 15.8 kB
8. Polynomial Regression/8. Polynomial Regression in R - Step 2.srt 15.6 kB
34/12. Natural Language Processing in Python - Step 6.srt 15.4 kB
7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.srt 15.2 kB
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27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.srt 14.9 kB
16. Logistic Regression/3. Logistic Regression in Python - Step 1.srt 14.8 kB
12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.srt 14.8 kB
27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.srt 14.7 kB
29. Apriori/3. Apriori in Python - Step 1.srt 14.6 kB
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36. Artificial Neural Networks/6. Gradient Descent.srt 14.4 kB
9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.srt 14.3 kB
16. Logistic Regression/8. Logistic Regression in Python - Step 6.srt 14.0 kB
13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.srt 13.9 kB
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34/16. Natural Language Processing in R - Step 2.srt 13.2 kB
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36. Artificial Neural Networks/3. The Activation Function.srt 12.3 kB
9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.srt 12.1 kB
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32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.srt 11.5 kB
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16. Logistic Regression/5. Logistic Regression in Python - Step 3.srt 11.1 kB
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19. Kernel SVM/2. Mapping to a higher dimension.srt 10.8 kB
11. Random Forest Regression/1. Random Forest Regression Intuition.srt 10.5 kB
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36. Artificial Neural Networks/18. ANN in R - Step 2.srt 10.4 kB
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26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.srt 9.6 kB
24. Evaluating Classification Models Performance/5. CAP Curve Analysis.srt 9.5 kB
1. Welcome to the course!/5. Why Machine Learning is the Future.srt 9.5 kB
37. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.srt 9.4 kB
39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.srt 9.4 kB
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16. Logistic Regression/10. Logistic Regression in R - Step 1.srt 9.1 kB
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34/20. Natural Language Processing in R - Step 6.srt 8.6 kB
4. Data Preprocessing in R/10. Data Preprocessing Template.srt 8.5 kB
6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.srt 8.5 kB
18. Support Vector Machine (SVM)/5.1 SVM.zip 8.5 kB
27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.srt 8.3 kB
30. Eclat/1. Eclat Intuition.srt 8.3 kB
17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.srt 8.2 kB
34/22. Natural Language Processing in R - Step 8.srt 8.2 kB
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16. Logistic Regression/12. Logistic Regression in R - Step 3.srt 7.6 kB
6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.srt 7.5 kB
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36. Artificial Neural Networks/8. Backpropagation.srt 7.3 kB
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22. Random Forest Classification/1. Random Forest Classification Intuition.srt 7.2 kB
16. Logistic Regression/16. R Classification Template.srt 6.9 kB
32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.srt 6.5 kB
37. Convolutional Neural Networks/8. Summary.srt 6.2 kB
34/3. Types of Natural Language Processing.srt 6.1 kB
9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.srt 6.1 kB
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3. Data Preprocessing in Python/3. Importing the Libraries.srt 5.8 kB
34/21. Natural Language Processing in R - Step 7.srt 5.7 kB
6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.srt 5.6 kB
1. Welcome to the course!/1. Applications of Machine Learning.srt 5.4 kB
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40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.srt 5.2 kB
39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.srt 5.2 kB
19. Kernel SVM/4. Types of Kernel Functions.srt 5.1 kB
10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.srt 5.0 kB
45. Bonus Lectures/1. YOUR SPECIAL BONUS.html 4.8 kB
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34/18. Natural Language Processing in R - Step 4.srt 4.8 kB
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16. Logistic Regression/13. Logistic Regression in R - Step 4.srt 4.1 kB
27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.srt 3.9 kB
1. Welcome to the course!/14. Your Shortcut To Becoming A Better Data Scientist!.html 3.8 kB
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24. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html 3.4 kB
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4. Data Preprocessing in R/4. Dataset Description.srt 3.3 kB
1. Welcome to the course!/13. FAQBot!.html 3.1 kB
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44. XGBoost/5. THANK YOU Bonus Video.srt 2.4 kB
33. Thompson Sampling/8. Additional Resource for this Section.html 2.3 kB
1. Welcome to the course!/8. GET ALL THE CODES AND DATASETS HERE!.html 1.9 kB
13. Regression Model Selection in Python/4. Conclusion of Part 2 - Regression.html 1.8 kB
14. Regression Model Selection in R/3. Conclusion of Part 2 - Regression.html 1.8 kB
34/1. Welcome to Part 7 - Natural Language Processing.html 1.7 kB
7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.srt 1.6 kB
31. -------------------- Part 6 Reinforcement Learning --------------------/1. Welcome to Part 6 - Reinforcement Learning.html 1.6 kB
1. Welcome to the course!/7. This PDF resource will help you a lot!.html 1.5 kB
3. Data Preprocessing in Python/5. For Python learners, summary of Object-oriented programming classes & objects.html 1.5 kB
7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.srt 1.5 kB
34/25. Homework Challenge.html 1.4 kB
1. Welcome to the course!/2. BONUS #1 Learning Paths.html 1.4 kB
34/14. Homework Challenge.html 1.4 kB
16. Logistic Regression/14. Warning - Update.html 1.4 kB
38. -------------------- Part 9 Dimensionality Reduction --------------------/1. Welcome to Part 9 - Dimensionality Reduction.html 1.3 kB
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44. XGBoost/3. Model Selection and Boosting BONUS.html 1.2 kB
6. Simple Linear Regression/8. Simple Linear Regression in Python - BONUS.html 1.1 kB
1. Welcome to the course!/11. BONUS Meet your instructors.html 1.1 kB
34/13. Natural Language Processing in Python - BONUS.html 1.1 kB
36. Artificial Neural Networks/21. Deep Learning BONUS #1.html 1.0 kB
23. Classification Model Selection in Python/1. Make sure you have this Model Selection folder ready.html 985 Bytes
13. Regression Model Selection in Python/1. Make sure you have this Model Selection folder ready.html 973 Bytes
37. Convolutional Neural Networks/17. Deep Learning BONUS #2.html 923 Bytes
34/26. BONUS NLP BERT.html 906 Bytes
42/1. Welcome to Part 10 - Model Selection & Boosting.html 899 Bytes
5. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html 875 Bytes
35. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html 870 Bytes
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16. Logistic Regression/17. Machine Learning Regression and Classification BONUS.html 819 Bytes
37. Convolutional Neural Networks/10. Make sure you have your dataset ready.html 797 Bytes
10. Decision Tree Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
11. Random Forest Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
16. Logistic Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
17. K-Nearest Neighbors (K-NN)/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
18. Support Vector Machine (SVM)/3. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
19. Kernel SVM/6. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
20. Naive Bayes/5. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
21. Decision Tree Classification/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
22. Random Forest Classification/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
26. K-Means Clustering/4. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
27. Hierarchical Clustering/5. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
29. Apriori/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
30. Eclat/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
32. Upper Confidence Bound (UCB)/3. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
33. Thompson Sampling/3. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
34/6. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
36. Artificial Neural Networks/10. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
39. Principal Component Analysis (PCA)/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
40. Linear Discriminant Analysis (LDA)/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
41. Kernel PCA/1. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
43. Model Selection/1. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
44. XGBoost/1. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
6. Simple Linear Regression/3. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
7. Multiple Linear Regression/8. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
8. Polynomial Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
9. Support Vector Regression (SVR)/3. Make sure you have your Machine Learning A-Z folder ready.html 776 Bytes
25. -------------------- Part 4 Clustering --------------------/1. Welcome to Part 4 - Clustering.html 734 Bytes
7. Multiple Linear Regression/20. Multiple Linear Regression in R - Automatic Backward Elimination.html 726 Bytes
3. Data Preprocessing in Python/1. Make sure you have your Machine Learning A-Z folder ready.html 664 Bytes
16. Logistic Regression/19. BONUS Logistic Regression Practical Case Study.html 619 Bytes
4. Data Preprocessing in R/1. Welcome.html 608 Bytes
1. Welcome to the course!/12. Some Additional Resources.html 553 Bytes
36. Artificial Neural Networks/22. BONUS ANN Case Study.html 544 Bytes
36. Artificial Neural Networks/12. Check out our free course on ANN for Regression.html 533 Bytes
2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.html 531 Bytes
27. Hierarchical Clustering/15. Conclusion of Part 4 - Clustering.html 516 Bytes
1. Welcome to the course!/4. BONUS #3 Regression Types.html 511 Bytes
1. Welcome to the course!/3. BONUS #2 ML vs. DL vs. AI - What’s the Difference.html 499 Bytes
4. Data Preprocessing in R/3. Make sure you have your dataset ready.html 465 Bytes
28. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html 425 Bytes
[Tutorialsplanet.NET].url 128 Bytes
16. Logistic Regression/18. Logistic Regression.html 125 Bytes
18. Support Vector Machine (SVM)/1. K-Nearest Neighbor.html 125 Bytes
27. Hierarchical Clustering/1. K-Means Clustering.html 125 Bytes
27. Hierarchical Clustering/14. Hierarchical Clustering.html 125 Bytes
6. Simple Linear Regression/13. Simple Linear Regression.html 125 Bytes
7. Multiple Linear Regression/21. Multiple Linear Regression.html 125 Bytes

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