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

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文件列表

37. Convolutional Neural Networks/10.1 Section 40 - Convolutional Neural Networks (CNN).zip 224MB
29. Apriori/6. Apriori in Python - Step 4.mp4 164MB
35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.srt 158MB
37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.mp4 153MB
43. Model Selection/3. Grid Search in Python.mp4 152MB
17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 147MB
23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.mp4 136MB
27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.mp4 136MB
13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.mp4 124MB
26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.mp4 121MB
16. Logistic Regression/9. Logistic Regression in Python - Step 7.mp4 119MB
37. Convolutional Neural Networks/13. CNN in Python - Step 3.mp4 119MB
39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 113MB
43. Model Selection/2. k-Fold Cross Validation in Python.mp4 112MB
36. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 111MB
21. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 108MB
29. Apriori/4. Apriori in Python - Step 2.mp4 108MB
37. Convolutional Neural Networks/12. CNN in Python - Step 2.mp4 107MB
18. Support Vector Machine (SVM)/4. SVM in Python.mp4 105MB
34. -------------------- Part 7 Natural Language Processing --------------------/5. Bag-Of-Words Model.mp4 103MB
40. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 102MB
3. Data Preprocessing in Python/9. Feature Scaling.mp4 102MB
36. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 101MB
20. Naive Bayes/6. Naive Bayes in Python.mp4 100MB
37. Convolutional Neural Networks/15. CNN in Python - Step 5.mp4 98MB
22. Random Forest Classification/3. Random Forest Classification in Python.mp4 97MB
1. Welcome to the course!/9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.mp4 95MB
16. Logistic Regression/15. Logistic Regression in R - Step 5.mp4 94MB
9. Support Vector Regression (SVR)/8. SVR in Python - Step 5.mp4 94MB
44. XGBoost/2. XGBoost in Python.mp4 90MB
34. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 5.mp4 90MB
3. Data Preprocessing in Python/7. Encoding Categorical Data.mp4 89MB
19. Kernel SVM/7. Kernel SVM in Python.mp4 88MB
9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.mp4 87MB
4. Data Preprocessing in R/8. Splitting the dataset into the Training set and Test set.mp4 87MB
32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 85MB
16. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 85MB
34. -------------------- Part 7 Natural Language Processing --------------------/4. Classical vs Deep Learning Models.mp4 84MB
26. K-Means Clustering/7. K-Means Clustering in Python - Step 3.mp4 81MB
4. Data Preprocessing in R/9. Feature Scaling.mp4 79MB
33. Thompson Sampling/6. Thompson Sampling in Python - Step 3.mp4 79MB
8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 78MB
41. Kernel PCA/2. Kernel PCA in Python.mp4 77MB
30. Eclat/3. Eclat in Python.mp4 76MB
27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.mp4 75MB
36. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 75MB
6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.mp4 75MB
11. Random Forest Regression/3. Random Forest Regression in Python.mp4 74MB
7. Multiple Linear Regression/12. Multiple Linear Regression in Python - Step 4.mp4 73MB
3. Data Preprocessing in Python/4. Importing the Dataset.mp4 72MB
37. Convolutional Neural Networks/11. CNN in Python - Step 1.mp4 71MB
33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 70MB
29. Apriori/3. Apriori in Python - Step 1.mp4 70MB
8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4 69MB
29. Apriori/5. Apriori in Python - Step 3.mp4 69MB
3. Data Preprocessing in Python/6. Taking care of Missing Data.mp4 69MB
21. Decision Tree Classification/4. Decision Tree Classification in R.mp4 68MB
3. Data Preprocessing in Python/8. Splitting the dataset into the Training set and Test set.mp4 68MB
36. Artificial Neural Networks/11. ANN in Python - Step 1.mp4 66MB
19. Kernel SVM/5. Non-Linear Kernel SVR (Advanced).mp4 66MB
36. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 65MB
18. Support Vector Machine (SVM)/5. SVM in R.mp4 65MB
22. Random Forest Classification/4. Random Forest Classification in R.mp4 64MB
7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 62MB
34. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 3.mp4 61MB
34. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 4.mp4 60MB
32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 59MB
8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4 58MB
7. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 58MB
32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.mp4 58MB
4. Data Preprocessing in R/7. Encoding Categorical Data.mp4 57MB
13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.mp4 57MB
41. Kernel PCA/3. Kernel PCA in R.mp4 57MB
29. Apriori/9. Apriori in R - Step 3.mp4 57MB
7. Multiple Linear Regression/6. Understanding the P-Value.mp4 56MB
10. Decision Tree Regression/7. Decision Tree Regression in R.mp4 56MB
17. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 56MB
8. Polynomial Regression/9. Polynomial Regression in R - Step 3.mp4 55MB
10. Decision Tree Regression/6. Decision Tree Regression in Python - Step 4.mp4 55MB
3. Data Preprocessing in Python/2. Getting Started.mp4 54MB
34. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4 54MB
26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.mp4 54MB
16. Logistic Regression/8. Logistic Regression in Python - Step 6.mp4 53MB
34. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 6.mp4 53MB
29. Apriori/7. Apriori in R - Step 1.mp4 53MB
19. Kernel SVM/8. Kernel SVM in R.mp4 53MB
44. XGBoost/5. THANK YOU Bonus Video.mp4 52MB
11. Random Forest Regression/4. Random Forest Regression in R.mp4 52MB
40. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 51MB
34. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4 51MB
33. Thompson Sampling/9. Thompson Sampling in R - Step 1.mp4 51MB
7. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 51MB
7. Multiple Linear Regression/18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 51MB
4. Data Preprocessing in R/10. Data Preprocessing Template.mp4 51MB
20. Naive Bayes/1. Bayes Theorem.mp4 50MB
36. Artificial Neural Networks/17. ANN in R - Step 1.mp4 50MB
20. Naive Bayes/7. Naive Bayes in R.mp4 50MB
6. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 49MB
6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.mp4 49MB
44. XGBoost/4. XGBoost in R.mp4 47MB
9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.mp4 46MB
7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.mp4 45MB
16. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 45MB
32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.mp4 45MB
33. Thompson Sampling/7. Thompson Sampling in Python - Step 4.mp4 45MB
16. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 45MB
36. Artificial Neural Networks/20. ANN in R - Step 4 (Last step).mp4 44MB
43. Model Selection/4. k-Fold Cross Validation in R.mp4 44MB
32. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in Python - Step 7.mp4 43MB
16. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 43MB
37. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 43MB
9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.mp4 43MB
10. Decision Tree Regression/3. Decision Tree Regression in Python - Step 1.mp4 42MB
39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 41MB
34. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 2.mp4 40MB
37. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 40MB
27. Hierarchical Clustering/6. Hierarchical Clustering in Python - Step 1.mp4 40MB
37. Convolutional Neural Networks/14. CNN in Python - Step 4.mp4 40MB
6. Simple Linear Regression/5. Simple Linear Regression in Python - Step 2.mp4 40MB
4. Data Preprocessing in R/6. Taking care of Missing Data.mp4 40MB
29. Apriori/8. Apriori in R - Step 2.mp4 39MB
8. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4 39MB
32. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 38MB
26. K-Means Clustering/5. K-Means Clustering in Python - Step 1.mp4 38MB
36. Artificial Neural Networks/19. ANN in R - Step 3.mp4 38MB
34. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp4 38MB
33. Thompson Sampling/1. Thompson Sampling Intuition.mp4 37MB
26. K-Means Clustering/10. K-Means Clustering in R.mp4 37MB
9. Support Vector Regression (SVR)/1. SVR Intuition (Updated!).mp4 37MB
39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.mp4 37MB
43. Model Selection/5. Grid Search in R.mp4 36MB
26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.mp4 35MB
29. Apriori/1. Apriori Intuition.mp4 35MB
9. Support Vector Regression (SVR)/6. SVR in Python - Step 3.mp4 35MB
19. Kernel SVM/3. The Kernel Trick.mp4 35MB
32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.mp4 34MB
34. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 1.mp4 34MB
32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.mp4 34MB
9. Support Vector Regression (SVR)/9. SVR in R.mp4 34MB
37. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 33MB
7. Multiple Linear Regression/7. Multiple Linear Regression Intuition - Step 5.mp4 33MB
32. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in Python - Step 5.mp4 32MB
8. Polynomial Regression/8. Polynomial Regression in R - Step 2.mp4 32MB
39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 32MB
8. Polynomial Regression/11. R Regression Template.mp4 31MB
35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4 31MB
20. Naive Bayes/2. Naive Bayes Intuition.mp4 31MB
37. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 31MB
39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.mp4 31MB
16. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 31MB
33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 31MB
32. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 30MB
26. K-Means Clustering/1. K-Means Clustering Intuition.mp4 30MB
36. Artificial Neural Networks/2. The Neuron.mp4 30MB
37. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 30MB
32. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 29MB
36. Artificial Neural Networks/9. Business Problem Description.mp4 29MB
16. Logistic Regression/1. Logistic Regression Intuition.mp4 29MB
39. Principal Component Analysis (PCA)/6. PCA in R - Step 2.mp4 29MB
8. Polynomial Regression/10. Polynomial Regression in R - Step 4.mp4 29MB
14. Regression Model Selection in R/1. Evaluating Regression Models Performance - Homework's Final Part.mp4 28MB
6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.mp4 28MB
16. Logistic Regression/12. Logistic Regression in R - Step 3.mp4 27MB
14. Regression Model Selection in R/2. Interpreting Linear Regression Coefficients.mp4 27MB
40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 27MB
36. Artificial Neural Networks/5. How do Neural Networks learn.mp4 27MB
10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.mp4 26MB
26. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 26MB
22. Random Forest Classification/1. Random Forest Classification Intuition.mp4 26MB
10. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 25MB
30. Eclat/4. Eclat in R.mp4 25MB
6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 25MB
36. Artificial Neural Networks/4. How do Neural Networks work.mp4 24MB
7. Multiple Linear Regression/15. Multiple Linear Regression in R - Step 1.mp4 23MB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp4 23MB
27. Hierarchical Clustering/4. Hierarchical Clustering Using Dendrograms.mp4 23MB
34. -------------------- Part 7 Natural Language Processing --------------------/3. Types of Natural Language Processing.mp4 22MB
7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 22MB
34. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp4 22MB
21. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 22MB
12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 21MB
8. Polynomial Regression/7. Polynomial Regression in R - Step 1.mp4 21MB
24. Evaluating Classification Models Performance/4. CAP Curve.mp4 20MB
18. Support Vector Machine (SVM)/2. SVM Intuition.mp4 20MB
9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.mp4 20MB
10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.mp4 19MB
20. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 19MB
36. Artificial Neural Networks/6. Gradient Descent.mp4 19MB
36. Artificial Neural Networks/18. ANN in R - Step 2.mp4 18MB
32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 18MB
16. Logistic Regression/16. R Classification Template.mp4 18MB
27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.mp4 17MB
34. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4 17MB
34. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp4 17MB
36. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 17MB
7. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 3.mp4 17MB
27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.mp4 17MB
4. Data Preprocessing in R/5. Importing the Dataset.mp4 16MB
34. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4 16MB
3. Data Preprocessing in Python/3. Importing the Libraries.mp4 16MB
16. Logistic Regression/10. Logistic Regression in R - Step 1.mp4 16MB
19. Kernel SVM/4. Types of Kernel Functions.mp4 16MB
11. Random Forest Regression/1. Random Forest Regression Intuition.mp4 16MB
19. Kernel SVM/2. Mapping to a higher dimension.mp4 15MB
26. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 15MB
24. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 15MB
16. Logistic Regression/11. Logistic Regression in R - Step 2.mp4 15MB
36. Artificial Neural Networks/3. The Activation Function.mp4 15MB
1. Welcome to the course!/5. Why Machine Learning is the Future.mp4 14MB
37. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14MB
33. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14MB
27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.mp4 14MB
7. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 3.mp4 14MB
27. Hierarchical Clustering/13. Hierarchical Clustering in R - Step 5.mp4 14MB
20. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13MB
24. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 13MB
34. -------------------- Part 7 Natural Language Processing --------------------/2. NLP Intuition.mp4 13MB
7. Multiple Linear Regression/1. Dataset + Business Problem Description.mp4 13MB
4. Data Preprocessing in R/4. Dataset Description.mp4 12MB
16. Logistic Regression/13. Logistic Regression in R - Step 4.mp4 12MB
6. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 12MB
6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 11MB
36. Artificial Neural Networks/8. Backpropagation.mp4 11MB
30. Eclat/1. Eclat Intuition.mp4 11MB
6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.mp4 11MB
17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 10MB
27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.mp4 10MB
27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.mp4 10MB
1. Welcome to the course!/1. Applications of Machine Learning.mp4 10MB
12. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 10MB
4. Data Preprocessing in R/2. Getting Started.mp4 10MB
34. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp4 10MB
33. Thompson Sampling/10. Thompson Sampling in R - Step 2.mp4 10MB
32. Upper Confidence Bound (UCB)/14. Upper Confidence Bound in R - Step 4.mp4 10MB
8. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9MB
24. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 9MB
27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.mp4 9MB
34. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp4 8MB
37. Convolutional Neural Networks/8. Summary.mp4 8MB
19. Kernel SVM/1. Kernel SVM Intuition.mp4 6MB
6. Simple Linear Regression/2. Simple Linear Regression Intuition - Step 2.mp4 6MB
37. Convolutional Neural Networks/1. Plan of attack.mp4 6MB
34. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp4 6MB
7. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 4.mp4 5MB
10. Decision Tree Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
11. Random Forest Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
17. K-Nearest Neighbors (K-NN)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
18. Support Vector Machine (SVM)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
19. Kernel SVM/6.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
20. Naive Bayes/5.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
22. Random Forest Classification/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
27. Hierarchical Clustering/5.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
29. Apriori/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
32. Upper Confidence Bound (UCB)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
36. Artificial Neural Networks/10.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
41. Kernel PCA/1.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
43. Model Selection/1.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
44. XGBoost/1.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
7. Multiple Linear Regression/8.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
8. Polynomial Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
1. Welcome to the course!/8.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
16. Logistic Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
21. Decision Tree Classification/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
26. K-Means Clustering/4.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
3. Data Preprocessing in Python/1.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
30. Eclat/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
33. Thompson Sampling/3.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
34. -------------------- Part 7 Natural Language Processing --------------------/6.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
39. Principal Component Analysis (PCA)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
40. Linear Discriminant Analysis (LDA)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
6. Simple Linear Regression/3.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
9. Support Vector Regression (SVR)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5MB
36. Artificial Neural Networks/1. Plan of attack.mp4 5MB
24. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 4MB
37. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3MB
1. Welcome to the course!/7.1 Machine_Learning_A_Z_Q_A.pdf 2MB
7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.mp4 2MB
7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.mp4 2MB
13. Regression Model Selection in Python/4.1 Regression_Bonus.zip 364KB
14. Regression Model Selection in R/3.1 Regression_Bonus.zip 364KB
13. Regression Model Selection in Python/1.1 Machine Learning A-Z (Model Selection).zip 160KB
23. Classification Model Selection in Python/1.1 Machine Learning A-Z (Model Selection).zip 160KB
30. Eclat/4.1 Eclat.zip 49KB
37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.srt 39KB
43. Model Selection/3. Grid Search in Python.srt 35KB
20. Naive Bayes/1. Bayes Theorem.srt 34KB
23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.srt 34KB
22. Random Forest Classification/4. Random Forest Classification in R.srt 32KB
10. Decision Tree Regression/7. Decision Tree Regression in R.srt 32KB
29. Apriori/6. Apriori in Python - Step 4.srt 31KB
29. Apriori/9. Apriori in R - Step 3.srt 31KB
29. Apriori/7. Apriori in R - Step 1.srt 31KB
36. Artificial Neural Networks/13. ANN in Python - Step 2.srt 31KB
8. Polynomial Regression/9. Polynomial Regression in R - Step 3.srt 31KB
41. Kernel PCA/3. Kernel PCA in R.srt 31KB
17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.srt 31KB
3. Data Preprocessing in Python/9. Feature Scaling.srt 30KB
13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.srt 30KB
40. Linear Discriminant Analysis (LDA)/4. LDA in R.srt 30KB
24. Evaluating Classification Models Performance/6.1 Classification_Pros_Cons.pdf 29KB
21. Decision Tree Classification/4. Decision Tree Classification in R.srt 29KB
16. Logistic Regression/15. Logistic Regression in R - Step 5.srt 29KB
26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.srt 29KB
37. Convolutional Neural Networks/12. CNN in Python - Step 2.srt 29KB
37. Convolutional Neural Networks/13. CNN in Python - Step 3.srt 29KB
43. Model Selection/2. k-Fold Cross Validation in Python.srt 29KB
37. Convolutional Neural Networks/7. Step 4 - Full Connection.srt 29KB
34. -------------------- Part 7 Natural Language Processing --------------------/5. Bag-Of-Words Model.srt 28KB
1. Welcome to the course!/9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.srt 28KB
11. Random Forest Regression/4. Random Forest Regression in R.srt 28KB
43. Model Selection/4. k-Fold Cross Validation in R.srt 28KB
33. Thompson Sampling/9. Thompson Sampling in R - Step 1.srt 28KB
33. Thompson Sampling/1. Thompson Sampling Intuition.srt 28KB
7. Multiple Linear Regression/18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.srt 27KB
36. Artificial Neural Networks/17. ANN in R - Step 1.srt 27KB
39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.srt 26KB
29. Apriori/4. Apriori in Python - Step 2.srt 26KB
34. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 5.srt 26KB
34. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.srt 26KB
27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.srt 26KB
44. XGBoost/4. XGBoost in R.srt 26KB
29. Apriori/1. Apriori Intuition.srt 26KB
27. Hierarchical Clustering/15.1 Clustering-Pros-Cons.pdf 26KB
36. Artificial Neural Networks/16. ANN in Python - Step 5.srt 26KB
19. Kernel SVM/8. Kernel SVM in R.srt 25KB
32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.srt 25KB
37. Convolutional Neural Networks/9. Softmax & Cross-Entropy.srt 25KB
32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.srt 25KB
36. Artificial Neural Networks/2. The Neuron.srt 25KB
3. Data Preprocessing in Python/4. Importing the Dataset.srt 24KB
34. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.srt 24KB
18. Support Vector Machine (SVM)/4. SVM in Python.srt 24KB
16. Logistic Regression/1. Logistic Regression Intuition.srt 24KB
6. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.srt 24KB
26. K-Means Clustering/7. K-Means Clustering in Python - Step 3.srt 24KB
7. Multiple Linear Regression/7. Multiple Linear Regression Intuition - Step 5.srt 24KB
36. Artificial Neural Networks/14. ANN in Python - Step 3.srt 23KB
40. Linear Discriminant Analysis (LDA)/3. LDA in Python.srt 23KB
17. K-Nearest Neighbors (K-NN)/4. K-NN in R.srt 23KB
26. K-Means Clustering/1. K-Means Clustering Intuition.srt 23KB
20. Naive Bayes/2. Naive Bayes Intuition.srt 23KB
37. Convolutional Neural Networks/3. Step 1 - Convolution Operation.srt 23KB
44. XGBoost/2. XGBoost in Python.srt 23KB
29. Apriori/8. Apriori in R - Step 2.srt 23KB
16. Logistic Regression/9. Logistic Regression in Python - Step 7.srt 23KB
37. Convolutional Neural Networks/15. CNN in Python - Step 5.srt 22KB
32. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.srt 22KB
9. Support Vector Regression (SVR)/8. SVR in Python - Step 5.srt 22KB
21. Decision Tree Classification/3. Decision Tree Classification in Python.srt 22KB
20. Naive Bayes/6. Naive Bayes in Python.srt 22KB
32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.srt 22KB
9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.srt 22KB
37. Convolutional Neural Networks/2. What are convolutional neural networks.srt 22KB
3. Data Preprocessing in Python/7. Encoding Categorical Data.srt 22KB
32. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.srt 22KB
20. Naive Bayes/7. Naive Bayes in R.srt 22KB
22. Random Forest Classification/3. Random Forest Classification in Python.srt 21KB
16. Logistic Regression/4. Logistic Regression in Python - Step 2.srt 21KB
11. Random Forest Regression/3. Random Forest Regression in Python.srt 21KB
37. Convolutional Neural Networks/5. Step 2 - Pooling.srt 21KB
43. Model Selection/5. Grid Search in R.srt 21KB
8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.srt 21KB
36. Artificial Neural Networks/20. ANN in R - Step 4 (Last step).srt 21KB
32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.srt 21KB
32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.srt 21KB
33. Thompson Sampling/6. Thompson Sampling in Python - Step 3.srt 21KB
19. Kernel SVM/7. Kernel SVM in Python.srt 20KB
36. Artificial Neural Networks/15. ANN in Python - Step 4.srt 20KB
3. Data Preprocessing in Python/8. Splitting the dataset into the Training set and Test set.srt 20KB
7. Multiple Linear Regression/12. Multiple Linear Regression in Python - Step 4.srt 20KB
8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.srt 20KB
6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.srt 20KB
39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.srt 20KB
34. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.srt 20KB
7. Multiple Linear Regression/6. Understanding the P-Value.srt 19KB
26. K-Means Clustering/10. K-Means Clustering in R.srt 19KB
6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.srt 19KB
29. Apriori/5. Apriori in Python - Step 3.srt 19KB
34. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 3.srt 19KB
36. Artificial Neural Networks/4. How do Neural Networks work.srt 19KB
36. Artificial Neural Networks/5. How do Neural Networks learn.srt 19KB
36. Artificial Neural Networks/19. ANN in R - Step 3.srt 19KB
30. Eclat/3. Eclat in Python.srt 19KB
39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.srt 19KB
9. Support Vector Regression (SVR)/9. SVR in R.srt 19KB
8. Polynomial Regression/11. R Regression Template.srt 19KB
26. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.srt 18KB
18. Support Vector Machine (SVM)/5. SVM in R.srt 18KB
37. Convolutional Neural Networks/11. CNN in Python - Step 1.srt 18KB
27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.srt 18KB
3. Data Preprocessing in Python/6. Taking care of Missing Data.srt 18KB
33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.srt 18KB
8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.srt 18KB
27. Hierarchical Clustering/4. Hierarchical Clustering Using Dendrograms.srt 18KB
41. Kernel PCA/2. Kernel PCA in Python.srt 17KB
36. Artificial Neural Networks/11. ANN in Python - Step 1.srt 17KB
10. Decision Tree Regression/1. Decision Tree Regression Intuition.srt 17KB
39. Principal Component Analysis (PCA)/6. PCA in R - Step 2.srt 17KB
34. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 4.srt 17KB
3. Data Preprocessing in Python/2. Getting Started.srt 17KB
7. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.srt 17KB
19. Kernel SVM/3. The Kernel Trick.srt 17KB
24. Evaluating Classification Models Performance/4. CAP Curve.srt 16KB
34. -------------------- Part 7 Natural Language Processing --------------------/4. Classical vs Deep Learning Models.srt 16KB
19. Kernel SVM/5. Non-Linear Kernel SVR (Advanced).srt 16KB
20. Naive Bayes/4. Naive Bayes Intuition (Extras).srt 16KB
30. Eclat/4. Eclat in R.srt 16KB
18. Support Vector Machine (SVM)/2. SVM Intuition.srt 16KB
26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.srt 16KB
10. Decision Tree Regression/6. Decision Tree Regression in Python - Step 4.srt 15KB
8. Polynomial Regression/10. Polynomial Regression in R - Step 4.srt 15KB
7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.srt 15KB
8. Polynomial Regression/8. Polynomial Regression in R - Step 2.srt 15KB
34. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 6.srt 15KB
7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.srt 15KB
4. Data Preprocessing in R/8. Splitting the dataset into the Training set and Test set.srt 15KB
27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.srt 15KB
16. Logistic Regression/3. Logistic Regression in Python - Step 1.srt 14KB
12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.srt 14KB
27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.srt 14KB
29. Apriori/3. Apriori in Python - Step 1.srt 14KB
8. Polynomial Regression/7. Polynomial Regression in R - Step 1.srt 14KB
36. Artificial Neural Networks/6. Gradient Descent.srt 14KB
9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.srt 14KB
16. Logistic Regression/8. Logistic Regression in Python - Step 6.srt 14KB
13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.srt 14KB
14. Regression Model Selection in R/2. Interpreting Linear Regression Coefficients.srt 13KB
10. Decision Tree Regression/3. Decision Tree Regression in Python - Step 1.srt 13KB
7. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.srt 13KB
4. Data Preprocessing in R/9. Feature Scaling.srt 13KB
26. K-Means Clustering/2. K-Means Random Initialization Trap.srt 13KB
14. Regression Model Selection in R/1. Evaluating Regression Models Performance - Homework's Final Part.srt 13KB
26. K-Means Clustering/5. K-Means Clustering in Python - Step 1.srt 13KB
34. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.srt 13KB
21. Decision Tree Classification/1. Decision Tree Classification Intuition.srt 13KB
8. Polynomial Regression/6. Polynomial Regression in Python - Step 4.srt 12KB
36. Artificial Neural Networks/7. Stochastic Gradient Descent.srt 12KB
36. Artificial Neural Networks/3. The Activation Function.srt 12KB
9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.srt 12KB
7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.srt 12KB
7. Multiple Linear Regression/15. Multiple Linear Regression in R - Step 1.srt 12KB
6. Simple Linear Regression/5. Simple Linear Regression in Python - Step 2.srt 12KB
32. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in Python - Step 7.srt 12KB
9. Support Vector Regression (SVR)/1. SVR Intuition (Updated!).srt 12KB
37. Convolutional Neural Networks/14. CNN in Python - Step 4.srt 11KB
33. Thompson Sampling/7. Thompson Sampling in Python - Step 4.srt 11KB
24. Evaluating Classification Models Performance/1. False Positives & False Negatives.srt 11KB
32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.srt 11KB
16. Logistic Regression/6. Logistic Regression in Python - Step 4.srt 11KB
33. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.srt 11KB
34. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 1.srt 11KB
32. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.srt 11KB
16. Logistic Regression/5. Logistic Regression in Python - Step 3.srt 11KB
34. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 2.srt 11KB
7. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 3.srt 11KB
27. Hierarchical Clustering/6. Hierarchical Clustering in Python - Step 1.srt 11KB
19. Kernel SVM/2. Mapping to a higher dimension.srt 11KB
11. Random Forest Regression/1. Random Forest Regression Intuition.srt 10KB
34. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.srt 10KB
36. Artificial Neural Networks/18. ANN in R - Step 2.srt 10KB
33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.srt 10KB
9. Support Vector Regression (SVR)/6. SVR in Python - Step 3.srt 10KB
16. Logistic Regression/7. Logistic Regression in Python - Step 5.srt 9KB
20. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).srt 9KB
32. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in Python - Step 5.srt 9KB
26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.srt 9KB
24. Evaluating Classification Models Performance/5. CAP Curve Analysis.srt 9KB
1. Welcome to the course!/5. Why Machine Learning is the Future.srt 9KB
37. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.srt 9KB
39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.srt 9KB
1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).srt 9KB
4. Data Preprocessing in R/6. Taking care of Missing Data.srt 9KB
16. Logistic Regression/10. Logistic Regression in R - Step 1.srt 9KB
6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.srt 9KB
4. Data Preprocessing in R/7. Encoding Categorical Data.srt 9KB
34. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.srt 8KB
4. Data Preprocessing in R/10. Data Preprocessing Template.srt 8KB
6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.srt 8KB
18. Support Vector Machine (SVM)/5.1 SVM.zip 8KB
27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.srt 8KB
30. Eclat/1. Eclat Intuition.srt 8KB
17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.srt 8KB
34. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.srt 8KB
8. Polynomial Regression/1. Polynomial Regression Intuition.srt 8KB
6. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.srt 8KB
10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.srt 8KB
24. Evaluating Classification Models Performance/2. Confusion Matrix.srt 8KB
16. Logistic Regression/12. Logistic Regression in R - Step 3.srt 7KB
6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.srt 7KB
36. Artificial Neural Networks/9. Business Problem Description.srt 7KB
12. Evaluating Regression Models Performance/1. R-Squared Intuition.srt 7KB
36. Artificial Neural Networks/8. Backpropagation.srt 7KB
7. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 3.srt 7KB
22. Random Forest Classification/1. Random Forest Classification Intuition.srt 7KB
16. Logistic Regression/16. R Classification Template.srt 7KB
32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.srt 6KB
27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.srt 6KB
37. Convolutional Neural Networks/8. Summary.srt 6KB
34. -------------------- Part 7 Natural Language Processing --------------------/3. Types of Natural Language Processing.srt 6KB
9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.srt 6KB
7. Multiple Linear Regression/1. Dataset + Business Problem Description.srt 6KB
3. Data Preprocessing in Python/3. Importing the Libraries.srt 6KB
34. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.srt 6KB
6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.srt 6KB
1. Welcome to the course!/1. Applications of Machine Learning.srt 5KB
33. Thompson Sampling/10. Thompson Sampling in R - Step 2.srt 5KB
37. Convolutional Neural Networks/1. Plan of attack.srt 5KB
40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.srt 5KB
39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.srt 5KB
19. Kernel SVM/4. Types of Kernel Functions.srt 5KB
10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.srt 5KB
45. Bonus Lectures/1. YOUR SPECIAL BONUS.html 5KB
27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.srt 5KB
34. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.srt 5KB
34. -------------------- Part 7 Natural Language Processing --------------------/2. NLP Intuition.srt 5KB
4. Data Preprocessing in R/5. Importing the Dataset.srt 5KB
32. Upper Confidence Bound (UCB)/14. Upper Confidence Bound in R - Step 4.srt 4KB
19. Kernel SVM/1. Kernel SVM Intuition.srt 4KB
16. Logistic Regression/11. Logistic Regression in R - Step 2.srt 4KB
6. Simple Linear Regression/2. Simple Linear Regression Intuition - Step 2.srt 4KB
27. Hierarchical Clustering/13. Hierarchical Clustering in R - Step 5.srt 4KB
36. Artificial Neural Networks/1. Plan of attack.srt 4KB
16. Logistic Regression/13. Logistic Regression in R - Step 4.srt 4KB
27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.srt 4KB
1. Welcome to the course!/14. Your Shortcut To Becoming A Better Data Scientist!.html 4KB
7. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 4.srt 4KB
7. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination.html 3KB
24. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html 3KB
1. Welcome to the course!/6. Important notes, tips & tricks for this course.html 3KB
34. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.srt 3KB
24. Evaluating Classification Models Performance/3. Accuracy Paradox.srt 3KB
4. Data Preprocessing in R/4. Dataset Description.srt 3KB
1. Welcome to the course!/13. FAQBot!.html 3KB
37. Convolutional Neural Networks/6. Step 3 - Flattening.srt 3KB
4. Data Preprocessing in R/2. Getting Started.srt 2KB
44. XGBoost/5. THANK YOU Bonus Video.srt 2KB
33. Thompson Sampling/8. Additional Resource for this Section.html 2KB
1. Welcome to the course!/8. GET ALL THE CODES AND DATASETS HERE!.html 2KB
13. Regression Model Selection in Python/4. Conclusion of Part 2 - Regression.html 2KB
14. Regression Model Selection in R/3. Conclusion of Part 2 - Regression.html 2KB
34. -------------------- Part 7 Natural Language Processing --------------------/1. Welcome to Part 7 - Natural Language Processing.html 2KB
7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.srt 2KB
31. -------------------- Part 6 Reinforcement Learning --------------------/1. Welcome to Part 6 - Reinforcement Learning.html 2KB
1. Welcome to the course!/7. This PDF resource will help you a lot!.html 1KB
3. Data Preprocessing in Python/5. For Python learners, summary of Object-oriented programming classes & objects.html 1KB
7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.srt 1KB
34. -------------------- Part 7 Natural Language Processing --------------------/25. Homework Challenge.html 1KB
1. Welcome to the course!/2. BONUS #1 Learning Paths.html 1KB
34. -------------------- Part 7 Natural Language Processing --------------------/14. Homework Challenge.html 1KB
16. Logistic Regression/14. Warning - Update.html 1KB
38. -------------------- Part 9 Dimensionality Reduction --------------------/1. Welcome to Part 9 - Dimensionality Reduction.html 1KB
7. Multiple Linear Regression/14. Multiple Linear Regression in Python - BONUS.html 1KB
44. XGBoost/3. Model Selection and Boosting BONUS.html 1KB
6. Simple Linear Regression/8. Simple Linear Regression in Python - BONUS.html 1KB
1. Welcome to the course!/11. BONUS Meet your instructors.html 1KB
34. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - BONUS.html 1KB
36. Artificial Neural Networks/21. Deep Learning BONUS #1.html 1011B
23. Classification Model Selection in Python/1. Make sure you have this Model Selection folder ready.html 985B
13. Regression Model Selection in Python/1. Make sure you have this Model Selection folder ready.html 973B
37. Convolutional Neural Networks/17. Deep Learning BONUS #2.html 923B
34. -------------------- Part 7 Natural Language Processing --------------------/26. BONUS NLP BERT.html 906B
42. -------------------- Part 10 Model Selection & Boosting --------------------/1. Welcome to Part 10 - Model Selection & Boosting.html 899B
5. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html 875B
35. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html 870B
15. -------------------- Part 3 Classification --------------------/1. Welcome to Part 3 - Classification.html 831B
16. Logistic Regression/17. Machine Learning Regression and Classification BONUS.html 819B
37. Convolutional Neural Networks/10. Make sure you have your dataset ready.html 797B
10. Decision Tree Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
11. Random Forest Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
16. Logistic Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
17. K-Nearest Neighbors (K-NN)/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
18. Support Vector Machine (SVM)/3. Make sure you have your Machine Learning A-Z folder ready.html 776B
19. Kernel SVM/6. Make sure you have your Machine Learning A-Z folder ready.html 776B
20. Naive Bayes/5. Make sure you have your Machine Learning A-Z folder ready.html 776B
21. Decision Tree Classification/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
22. Random Forest Classification/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
26. K-Means Clustering/4. Make sure you have your Machine Learning A-Z folder ready.html 776B
27. Hierarchical Clustering/5. Make sure you have your Machine Learning A-Z folder ready.html 776B
29. Apriori/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
30. Eclat/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
32. Upper Confidence Bound (UCB)/3. Make sure you have your Machine Learning A-Z folder ready.html 776B
33. Thompson Sampling/3. Make sure you have your Machine Learning A-Z folder ready.html 776B
34. -------------------- Part 7 Natural Language Processing --------------------/6. Make sure you have your Machine Learning A-Z folder ready.html 776B
36. Artificial Neural Networks/10. Make sure you have your Machine Learning A-Z folder ready.html 776B
39. Principal Component Analysis (PCA)/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
40. Linear Discriminant Analysis (LDA)/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
41. Kernel PCA/1. Make sure you have your Machine Learning A-Z folder ready.html 776B
43. Model Selection/1. Make sure you have your Machine Learning A-Z folder ready.html 776B
44. XGBoost/1. Make sure you have your Machine Learning A-Z folder ready.html 776B
6. Simple Linear Regression/3. Make sure you have your Machine Learning A-Z folder ready.html 776B
7. Multiple Linear Regression/8. Make sure you have your Machine Learning A-Z folder ready.html 776B
8. Polynomial Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776B
9. Support Vector Regression (SVR)/3. Make sure you have your Machine Learning A-Z folder ready.html 776B
25. -------------------- Part 4 Clustering --------------------/1. Welcome to Part 4 - Clustering.html 734B
7. Multiple Linear Regression/20. Multiple Linear Regression in R - Automatic Backward Elimination.html 726B
3. Data Preprocessing in Python/1. Make sure you have your Machine Learning A-Z folder ready.html 664B
16. Logistic Regression/19. BONUS Logistic Regression Practical Case Study.html 619B
4. Data Preprocessing in R/1. Welcome.html 608B
1. Welcome to the course!/12. Some Additional Resources.html 553B
36. Artificial Neural Networks/22. BONUS ANN Case Study.html 544B
36. Artificial Neural Networks/12. Check out our free course on ANN for Regression.html 533B
2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.html 531B
27. Hierarchical Clustering/15. Conclusion of Part 4 - Clustering.html 516B
1. Welcome to the course!/4. BONUS #3 Regression Types.html 511B
1. Welcome to the course!/3. BONUS #2 ML vs. DL vs. AI - What’s the Difference.html 499B
4. Data Preprocessing in R/3. Make sure you have your dataset ready.html 465B
28. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html 425B
0. Websites you may like/[FCS Forum].url 133B
0. Websites you may like/[FreeCourseSite.com].url 127B
16. Logistic Regression/18. Logistic Regression.html 125B
18. Support Vector Machine (SVM)/1. K-Nearest Neighbor.html 125B
27. Hierarchical Clustering/1. K-Means Clustering.html 125B
27. Hierarchical Clustering/14. Hierarchical Clustering.html 125B
6. Simple Linear Regression/13. Simple Linear Regression.html 125B
7. Multiple Linear Regression/21. Multiple Linear Regression.html 125B
0. Websites you may like/[CourseClub.ME].url 122B