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

Creation Time	June 2, 2025, 10:18 a.m.
Last Access Time	June 7, 2025, 9:39 p.m.
File Size	11.5 GB
Keywords	A-Z™ Data Python DesireCourse - Science Udemy Machine R Learning In Net Hands-On
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Total Files	629
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37. Convolutional Neural Networks/10.1 Section 40 - Convolutional Neural Networks (CNN).zip 224.0 MB
29. Apriori/6. Apriori in Python - Step 4.mp4 164.3 MB
37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.mp4 152.8 MB
43. Model Selection/3. Grid Search in Python.mp4 151.8 MB
17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 146.6 MB
23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.mp4 136.0 MB
27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.mp4 135.9 MB
13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.mp4 123.6 MB
26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.mp4 120.5 MB
16. Logistic Regression/9. Logistic Regression in Python - Step 7.mp4 118.6 MB
37. Convolutional Neural Networks/13. CNN in Python - Step 3.mp4 118.6 MB
39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 112.9 MB
43. Model Selection/2. k-Fold Cross Validation in Python.mp4 112.4 MB
36. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 111.0 MB
21. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 108.1 MB
29. Apriori/4. Apriori in Python - Step 2.mp4 107.7 MB
37. Convolutional Neural Networks/12. CNN in Python - Step 2.mp4 106.9 MB
18. Support Vector Machine (SVM)/4. SVM in Python.mp4 104.8 MB
34. -------------------- Part 7 Natural Language Processing --------------------/5. Bag-Of-Words Model.mp4 103.5 MB
40. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 102.0 MB
3. Data Preprocessing in Python/9. Feature Scaling.mp4 101.7 MB
36. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 101.4 MB
20. Naive Bayes/6. Naive Bayes in Python.mp4 100.5 MB
37. Convolutional Neural Networks/15. CNN in Python - Step 5.mp4 97.7 MB
22. Random Forest Classification/3. Random Forest Classification in Python.mp4 96.7 MB
1. Welcome to the course!/10. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.mp4 94.8 MB
16. Logistic Regression/15. Logistic Regression in R - Step 5.mp4 93.8 MB
9. Support Vector Regression (SVR)/8. SVR in Python - Step 5.mp4 93.6 MB
44. XGBoost/2. XGBoost in Python.mp4 90.0 MB
34. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 5.mp4 89.6 MB
3. Data Preprocessing in Python/7. Encoding Categorical Data.mp4 88.6 MB
19. Kernel SVM/7. Kernel SVM in Python.mp4 88.4 MB
9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.mp4 86.9 MB
4. Data Preprocessing in R/8. Splitting the dataset into the Training set and Test set.mp4 86.5 MB
32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 85.4 MB
9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.srt 85.2 MB
16. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 84.7 MB
34. -------------------- Part 7 Natural Language Processing --------------------/4. Classical vs Deep Learning Models.mp4 84.0 MB
26. K-Means Clustering/7. K-Means Clustering in Python - Step 3.mp4 81.3 MB
4. Data Preprocessing in R/9. Feature Scaling.mp4 78.9 MB
33. Thompson Sampling/6. Thompson Sampling in Python - Step 3.mp4 78.7 MB
8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 77.9 MB
41. Kernel PCA/2. Kernel PCA in Python.mp4 77.5 MB
30. Eclat/3. Eclat in Python.mp4 75.6 MB
27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.mp4 75.3 MB
36. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 75.1 MB
6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.mp4 74.6 MB
11. Random Forest Regression/3. Random Forest Regression in Python.mp4 74.4 MB
7. Multiple Linear Regression/12. Multiple Linear Regression in Python - Step 4.mp4 72.5 MB
3. Data Preprocessing in Python/4. Importing the Dataset.mp4 71.8 MB
37. Convolutional Neural Networks/11. CNN in Python - Step 1.mp4 70.8 MB
33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 70.0 MB
29. Apriori/3. Apriori in Python - Step 1.mp4 69.9 MB
8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4 69.3 MB
29. Apriori/5. Apriori in Python - Step 3.mp4 69.2 MB
3. Data Preprocessing in Python/6. Taking care of Missing Data.mp4 69.0 MB
21. Decision Tree Classification/4. Decision Tree Classification in R.mp4 68.2 MB
3. Data Preprocessing in Python/8. Splitting the dataset into the Training set and Test set.mp4 67.6 MB
36. Artificial Neural Networks/11. ANN in Python - Step 1.mp4 66.5 MB
19. Kernel SVM/5. Non-Linear Kernel SVR (Advanced).mp4 65.6 MB
36. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 65.4 MB
18. Support Vector Machine (SVM)/5. SVM in R.mp4 65.3 MB
22. Random Forest Classification/4. Random Forest Classification in R.mp4 64.1 MB
7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 62.3 MB
34. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 3.mp4 60.6 MB
34. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 4.mp4 60.1 MB
32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 58.8 MB
8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4 58.3 MB
7. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 58.2 MB
32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.mp4 57.8 MB
4. Data Preprocessing in R/7. Encoding Categorical Data.mp4 57.3 MB
13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.mp4 56.8 MB
41. Kernel PCA/3. Kernel PCA in R.mp4 56.6 MB
29. Apriori/9. Apriori in R - Step 3.mp4 56.5 MB
7. Multiple Linear Regression/6. Understanding the P-Value.mp4 56.5 MB
10. Decision Tree Regression/7. Decision Tree Regression in R.mp4 56.2 MB
17. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 55.8 MB
8. Polynomial Regression/9. Polynomial Regression in R - Step 3.mp4 54.8 MB
10. Decision Tree Regression/6. Decision Tree Regression in Python - Step 4.mp4 54.8 MB
3. Data Preprocessing in Python/2. Getting Started.mp4 54.3 MB
34. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4 54.2 MB
26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.mp4 54.1 MB
16. Logistic Regression/8. Logistic Regression in Python - Step 6.mp4 53.0 MB
34. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 6.mp4 52.9 MB
29. Apriori/7. Apriori in R - Step 1.mp4 52.8 MB
19. Kernel SVM/8. Kernel SVM in R.mp4 52.8 MB
44. XGBoost/5. THANK YOU bonus video.mp4 52.2 MB
11. Random Forest Regression/4. Random Forest Regression in R.mp4 51.9 MB
40. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 51.3 MB
34. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4 51.2 MB
33. Thompson Sampling/9. Thompson Sampling in R - Step 1.mp4 51.0 MB
7. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 50.9 MB
7. Multiple Linear Regression/18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 50.8 MB
4. Data Preprocessing in R/10. Data Preprocessing Template.mp4 50.7 MB
20. Naive Bayes/1. Bayes Theorem.mp4 50.4 MB
36. Artificial Neural Networks/17. ANN in R - Step 1.mp4 49.9 MB
20. Naive Bayes/7. Naive Bayes in R.mp4 49.8 MB
6. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 49.2 MB
6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.mp4 48.6 MB
44. XGBoost/4. XGBoost in R.mp4 47.3 MB
9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.mp4 46.3 MB
7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.mp4 45.2 MB
16. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 45.2 MB
32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.mp4 44.9 MB
33. Thompson Sampling/7. Thompson Sampling in Python - Step 4.mp4 44.6 MB
16. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 44.6 MB
36. Artificial Neural Networks/20. ANN in R - Step 4 (Last step).mp4 43.7 MB
43. Model Selection/4. k-Fold Cross Validation in R.mp4 43.6 MB
32. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in Python - Step 7.mp4 43.3 MB
16. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 43.1 MB
37. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 42.8 MB
9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.mp4 42.6 MB
10. Decision Tree Regression/3. Decision Tree Regression in Python - Step 1.mp4 42.4 MB
39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 40.8 MB
34. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 2.mp4 40.5 MB
37. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 40.2 MB
27. Hierarchical Clustering/6. Hierarchical Clustering in Python - Step 1.mp4 40.2 MB
37. Convolutional Neural Networks/14. CNN in Python - Step 4.mp4 40.0 MB
6. Simple Linear Regression/5. Simple Linear Regression in Python - Step 2.mp4 39.8 MB
4. Data Preprocessing in R/6. Taking care of Missing Data.mp4 39.8 MB
29. Apriori/8. Apriori in R - Step 2.mp4 38.8 MB
8. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4 38.8 MB
32. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 38.5 MB
26. K-Means Clustering/5. K-Means Clustering in Python - Step 1.mp4 38.1 MB
36. Artificial Neural Networks/19. ANN in R - Step 3.mp4 37.9 MB
34. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp4 37.7 MB
33. Thompson Sampling/1. Thompson Sampling Intuition.mp4 37.3 MB
26. K-Means Clustering/10. K-Means Clustering in R.mp4 36.9 MB
9. Support Vector Regression (SVR)/1. SVR Intuition (Updated!).mp4 36.9 MB
39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.mp4 36.7 MB
43. Model Selection/5. Grid Search in R.mp4 35.5 MB
26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.mp4 35.1 MB
29. Apriori/1. Apriori Intuition.mp4 35.0 MB
9. Support Vector Regression (SVR)/6. SVR in Python - Step 3.mp4 34.8 MB
19. Kernel SVM/3. The Kernel Trick.mp4 34.7 MB
32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.mp4 34.1 MB
34. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 1.mp4 34.1 MB
32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.mp4 34.0 MB
9. Support Vector Regression (SVR)/9. SVR in R.mp4 33.7 MB
37. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 33.2 MB
7. Multiple Linear Regression/7. Multiple Linear Regression Intuition - Step 5.mp4 32.8 MB
32. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in Python - Step 5.mp4 32.4 MB
8. Polynomial Regression/8. Polynomial Regression in R - Step 2.srt 32.3 MB
8. Polynomial Regression/8. Polynomial Regression in R - Step 2.mp4 32.3 MB
39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 32.1 MB
8. Polynomial Regression/11. R Regression Template.mp4 31.3 MB
35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4 31.3 MB
20. Naive Bayes/2. Naive Bayes Intuition.mp4 31.1 MB
37. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 31.0 MB
39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.mp4 30.7 MB
16. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 30.6 MB
33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 30.6 MB
32. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 30.2 MB
26. K-Means Clustering/1. K-Means Clustering Intuition.mp4 30.0 MB
36. Artificial Neural Networks/2. The Neuron.mp4 29.9 MB
37. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 29.5 MB
32. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 29.3 MB
36. Artificial Neural Networks/9. Business Problem Description.mp4 29.2 MB
16. Logistic Regression/1. Logistic Regression Intuition.mp4 29.2 MB
39. Principal Component Analysis (PCA)/6. PCA in R - Step 2.mp4 29.0 MB
8. Polynomial Regression/10. Polynomial Regression in R - Step 4.mp4 28.5 MB
14. Regression Model Selection in R/1. Evaluating Regression Models Performance - Homework's Final Part.mp4 28.3 MB
6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.mp4 28.2 MB
16. Logistic Regression/12. Logistic Regression in R - Step 3.mp4 27.4 MB
14. Regression Model Selection in R/2. Interpreting Linear Regression Coefficients.mp4 27.4 MB
40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 27.0 MB
36. Artificial Neural Networks/5. How do Neural Networks learn.mp4 26.6 MB
10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.mp4 26.3 MB
26. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 25.7 MB
22. Random Forest Classification/1. Random Forest Classification Intuition.mp4 25.7 MB
10. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 25.3 MB
30. Eclat/4. Eclat in R.mp4 25.3 MB
6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 24.9 MB
36. Artificial Neural Networks/4. How do Neural Networks work.mp4 23.5 MB
7. Multiple Linear Regression/15. Multiple Linear Regression in R - Step 1.mp4 23.4 MB
1. Welcome to the course!/11. Installing R and R Studio (Mac, Linux & Windows).mp4 23.2 MB
27. Hierarchical Clustering/4. Hierarchical Clustering Using Dendrograms.mp4 22.8 MB
34. -------------------- Part 7 Natural Language Processing --------------------/3. Types of Natural Language Processing.mp4 22.5 MB
7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 22.0 MB
34. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp4 21.7 MB
21. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 21.6 MB
12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 21.4 MB
8. Polynomial Regression/7. Polynomial Regression in R - Step 1.mp4 21.2 MB
1. Welcome to the course!/8. Updates on Udemy Reviews.mp4 20.4 MB
24. Evaluating Classification Models Performance/4. CAP Curve.mp4 20.3 MB
18. Support Vector Machine (SVM)/2. SVM Intuition.mp4 19.9 MB
9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.mp4 19.8 MB
10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.mp4 19.5 MB
20. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 18.9 MB
36. Artificial Neural Networks/6. Gradient Descent.mp4 18.5 MB
36. Artificial Neural Networks/18. ANN in R - Step 2.mp4 18.3 MB
32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 17.7 MB
16. Logistic Regression/16. R Classification Template.mp4 17.5 MB
27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.mp4 17.5 MB
34. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4 17.2 MB
34. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp4 16.9 MB
36. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 16.8 MB
7. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 3.mp4 16.6 MB
27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.mp4 16.5 MB
4. Data Preprocessing in R/5. Importing the Dataset.mp4 16.4 MB
34. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4 16.1 MB
3. Data Preprocessing in Python/3. Importing the Libraries.mp4 16.0 MB
16. Logistic Regression/10. Logistic Regression in R - Step 1.mp4 15.7 MB
19. Kernel SVM/4. Types of Kernel Functions.mp4 15.7 MB
11. Random Forest Regression/1. Random Forest Regression Intuition.mp4 15.7 MB
19. Kernel SVM/2. Mapping to a higher dimension.mp4 15.4 MB
26. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 15.4 MB
24. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 15.1 MB
16. Logistic Regression/11. Logistic Regression in R - Step 2.mp4 14.9 MB
36. Artificial Neural Networks/3. The Activation Function.mp4 14.8 MB
1. Welcome to the course!/5. Why Machine Learning is the Future.mp4 14.5 MB
37. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14.1 MB
33. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14.1 MB
27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.mp4 13.9 MB
7. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 3.mp4 13.9 MB
27. Hierarchical Clustering/13. Hierarchical Clustering in R - Step 5.mp4 13.7 MB
20. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13.3 MB
24. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 12.9 MB
34. -------------------- Part 7 Natural Language Processing --------------------/2. NLP Intuition.mp4 12.7 MB
7. Multiple Linear Regression/1. Dataset + Business Problem Description.mp4 12.6 MB
4. Data Preprocessing in R/4. Dataset Description.mp4 11.8 MB
16. Logistic Regression/13. Logistic Regression in R - Step 4.mp4 11.7 MB
6. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 11.5 MB
6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 11.4 MB
36. Artificial Neural Networks/8. Backpropagation.mp4 10.9 MB
30. Eclat/1. Eclat Intuition.mp4 10.7 MB
6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.mp4 10.5 MB
17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 10.5 MB
27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.mp4 10.2 MB
27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.mp4 10.0 MB
4. Data Preprocessing in R/2. Getting Started.mp4 9.8 MB
1. Welcome to the course!/1. Applications of Machine Learning.mp4 9.8 MB
12. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 9.8 MB
34. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp4 9.6 MB
33. Thompson Sampling/10. Thompson Sampling in R - Step 2.mp4 9.6 MB
32. Upper Confidence Bound (UCB)/14. Upper Confidence Bound in R - Step 4.mp4 9.6 MB
8. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9.4 MB
24. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 8.9 MB
27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.mp4 8.6 MB
34. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp4 8.2 MB
37. Convolutional Neural Networks/8. Summary.mp4 7.9 MB
19. Kernel SVM/1. Kernel SVM Intuition.mp4 6.4 MB
6. Simple Linear Regression/2. Simple Linear Regression Intuition - Step 2.mp4 6.0 MB
37. Convolutional Neural Networks/1. Plan of attack.mp4 5.9 MB
34. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp4 5.8 MB
7. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 4.mp4 5.3 MB
10. Decision Tree Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
11. Random Forest Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
17. K-Nearest Neighbors (K-NN)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
20. Naive Bayes/5.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
21. Decision Tree Classification/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
22. Random Forest Classification/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
26. K-Means Clustering/4.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
27. Hierarchical Clustering/5.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
29. Apriori/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
3. Data Preprocessing in Python/1.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
30. Eclat/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
32. Upper Confidence Bound (UCB)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
33. Thompson Sampling/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
34. -------------------- Part 7 Natural Language Processing --------------------/6.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
36. Artificial Neural Networks/10.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
39. Principal Component Analysis (PCA)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
40. Linear Discriminant Analysis (LDA)/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
41. Kernel PCA/1.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
6. Simple Linear Regression/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
7. Multiple Linear Regression/8.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
9. Support Vector Regression (SVR)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
1. Welcome to the course!/9.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
16. Logistic Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
18. Support Vector Machine (SVM)/3.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
19. Kernel SVM/6.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
43. Model Selection/1.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
44. XGBoost/1.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
8. Polynomial Regression/2.1 Machine Learning A-Z (Codes and Datasets).zip 5.3 MB
36. Artificial Neural Networks/1. Plan of attack.mp4 4.8 MB
24. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 4.2 MB
37. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3.3 MB
1. Welcome to the course!/7.1 Machine_Learning_A_Z_Q_A.pdf 2.3 MB
7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.mp4 2.0 MB
7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.mp4 2.0 MB
13. Regression Model Selection in Python/4.1 Regression_Bonus.zip 364.5 KB
14. Regression Model Selection in R/3.1 Regression_Bonus.zip 364.5 KB
13. Regression Model Selection in Python/1.1 Machine Learning A-Z (Model Selection).zip 160.0 KB
23. Classification Model Selection in Python/1.1 Machine Learning A-Z (Model Selection).zip 160.0 KB
30. Eclat/4.1 Eclat.zip 48.5 KB
37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.srt 38.8 KB
43. Model Selection/3. Grid Search in Python.srt 34.6 KB
20. Naive Bayes/1. Bayes Theorem.srt 34.5 KB
23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.srt 34.4 KB
22. Random Forest Classification/4. Random Forest Classification in R.srt 32.4 KB
10. Decision Tree Regression/7. Decision Tree Regression in R.srt 32.1 KB
29. Apriori/6. Apriori in Python - Step 4.srt 31.3 KB
29. Apriori/9. Apriori in R - Step 3.srt 31.2 KB
29. Apriori/7. Apriori in R - Step 1.srt 31.1 KB
36. Artificial Neural Networks/13. ANN in Python - Step 2.srt 31.0 KB
8. Polynomial Regression/9. Polynomial Regression in R - Step 3.srt 30.9 KB
41. Kernel PCA/3. Kernel PCA in R.srt 30.8 KB
17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.srt 30.8 KB
3. Data Preprocessing in Python/9. Feature Scaling.srt 30.3 KB
13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.srt 30.2 KB
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26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.srt 29.1 KB
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33. Thompson Sampling/9. Thompson Sampling in R - Step 1.srt 27.9 KB
33. Thompson Sampling/1. Thompson Sampling Intuition.srt 27.5 KB
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44. XGBoost/4. XGBoost in R.srt 26.0 KB
29. Apriori/1. Apriori Intuition.srt 25.9 KB
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19. Kernel SVM/8. Kernel SVM in R.srt 25.4 KB
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34. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.srt 24.0 KB
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30. Eclat/3. Eclat in Python.srt 18.9 KB
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9. Support Vector Regression (SVR)/9. SVR in R.srt 18.7 KB
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27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.srt 18.2 KB
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8. Polynomial Regression/10. Polynomial Regression in R - Step 4.srt 15.4 KB
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30. Eclat/1. Eclat Intuition.srt 8.1 KB
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27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.srt 4.7 KB
45. Bonus Lectures/1. YOUR SPECIAL BONUS.html 4.7 KB
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36. Artificial Neural Networks/1. Plan of attack.srt 4.0 KB
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6. Simple Linear Regression/8. Simple Linear Regression in Python - BONUS.html 1.1 KB
34. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - BONUS.html 1.1 KB
1. Welcome to the course!/12. BONUS Meet your instructors.html 1.0 KB
36. Artificial Neural Networks/21. Deep Learning BONUS #1.html 1011 bytes
23. Classification Model Selection in Python/1. Make sure you have this Model Selection folder ready.html 985 bytes
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11. Random Forest Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776 bytes
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17. K-Nearest Neighbors (K-NN)/2. Make sure you have your Machine Learning A-Z folder ready.html 776 bytes
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20. Naive Bayes/5. Make sure you have your Machine Learning A-Z folder ready.html 776 bytes
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30. Eclat/2. Make sure you have your Machine Learning A-Z folder ready.html 776 bytes
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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
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8. Polynomial Regression/2. Make sure you have your Machine Learning A-Z folder ready.html 776 bytes
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3. Data Preprocessing in Python/1. Make sure you have your Machine Learning A-Z folder ready.html 664 bytes
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4. Data Preprocessing in R/1. Welcome.html 608 bytes
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27. Hierarchical Clustering/15. Conclusion of Part 4 - Clustering.html 516 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
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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