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

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最近下载:2021-01-07 19:06:47
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文件列表

1. Welcome to the course!/5.1 Machine_Learning_A-Z_New.zip.zip 228MB
36. Kernel PCA/3. Kernel PCA in R.mp4 57MB
1. Welcome to the course!/6. Updates on Udemy Reviews.mp4 53MB
39. XGBoost/5. THANK YOU bonus video.mp4 52MB
12. Logistic Regression/13. Logistic Regression in R - Step 5.mp4 52MB
35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 51MB
17. Decision Tree Classification/4. Decision Tree Classification in R.mp4 51MB
18. Random Forest Classification/4. Random Forest Classification in R.mp4 49MB
31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 48MB
39. XGBoost/4. XGBoost in R.mp4 47MB
27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4 47MB
18. Random Forest Classification/3. Random Forest Classification in Python.mp4 47MB
32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4 47MB
7. Support Vector Regression (SVR)/2. SVR Intuition.mp4 47MB
7. Support Vector Regression (SVR)/3. SVR in Python.mp4 46MB
35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 45MB
8. Decision Tree Regression/4. Decision Tree Regression in R.mp4 44MB
16. Naive Bayes/1. Bayes Theorem.mp4 44MB
24. Apriori/5. Apriori in R - Step 3.mp4 44MB
38. Model Selection/3. k-Fold Cross Validation in R.mp4 44MB
6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp4 43MB
28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 43MB
6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 43MB
24. Apriori/3. Apriori in R - Step 1.mp4 43MB
32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 43MB
12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 43MB
15. Kernel SVM/6. Kernel SVM in Python.mp4 42MB
13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 41MB
29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4 41MB
27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 41MB
28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4 41MB
2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp4 41MB
15. Kernel SVM/7. Kernel SVM in R.mp4 40MB
29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4 40MB
9. Random Forest Regression/4. Random Forest Regression in R.mp4 40MB
32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 40MB
21. K-Means Clustering/5. K-Means Clustering in Python.mp4 40MB
5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 40MB
5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 40MB
29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp4 39MB
9. Random Forest Regression/3. Random Forest Regression in Python.mp4 39MB
2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp4 39MB
31. Artificial Neural Networks/22. ANN in R - Step 1.mp4 39MB
38. Model Selection/4. Grid Search in Python - Step 1.mp4 38MB
24. Apriori/6. Apriori in Python - Step 1.mp4 38MB
4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 37MB
16. Naive Bayes/7. Naive Bayes in R.mp4 37MB
28. Thompson Sampling/1. Thompson Sampling Intuition.mp4 37MB
34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4 37MB
38. Model Selection/6. Grid Search in R.vtt 36MB
38. Model Selection/6. Grid Search in R.mp4 36MB
27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 35MB
13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 35MB
29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.mp4 35MB
24. Apriori/1. Apriori Intuition.mp4 35MB
2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.mp4 35MB
8. Decision Tree Regression/3. Decision Tree Regression in Python.mp4 34MB
31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp4 33MB
36. Kernel PCA/2. Kernel PCA in Python.mp4 33MB
32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 33MB
38. Model Selection/2. k-Fold Cross Validation in Python.mp4 33MB
5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 33MB
14. Support Vector Machine (SVM)/4. SVM in R.mp4 32MB
2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.mp4 32MB
34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 32MB
39. XGBoost/3. XGBoost in Python - Step 2.mp4 32MB
34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 32MB
27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 32MB
30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4 31MB
14. Support Vector Machine (SVM)/3. SVM in Python.mp4 31MB
32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 31MB
4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp4 31MB
34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp4 31MB
24. Apriori/4. Apriori in R - Step 2.mp4 31MB
27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 30MB
31. Artificial Neural Networks/2. The Neuron.mp4 30MB
17. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 30MB
29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.mp4 30MB
31. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 30MB
24. Apriori/7. Apriori in Python - Step 2.mp4 30MB
38. Model Selection/5. Grid Search in Python - Step 2.mp4 30MB
32. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 29MB
27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 29MB
31. Artificial Neural Networks/12. ANN in Python - Step 1.mp4 29MB
15. Kernel SVM/3. The Kernel Trick.mp4 29MB
12. Logistic Regression/1. Logistic Regression Intuition.mp4 29MB
34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp4 29MB
27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp4 29MB
29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp4 29MB
21. K-Means Clustering/6. K-Means Clustering in R.mp4 29MB
31. Artificial Neural Networks/24. ANN in R - Step 3.mp4 29MB
5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp4 29MB
27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp4 28MB
16. Naive Bayes/2. Naive Bayes Intuition.mp4 28MB
6. Polynomial Regression/7. Python Regression Template.mp4 27MB
32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp4 27MB
5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 27MB
6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4 27MB
35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 27MB
24. Apriori/8. Apriori in Python - Step 3.mp4 27MB
21. K-Means Clustering/1. K-Means Clustering Intuition.mp4 27MB
31. Artificial Neural Networks/5. How do Neural Networks learn.mp4 27MB
5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp4 26MB
7. Support Vector Regression (SVR)/4. SVR in R.mp4 26MB
34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp4 26MB
6. Polynomial Regression/12. R Regression Template.mp4 25MB
32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4 25MB
6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4 25MB
10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp4 24MB
29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.mp4 24MB
29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.mp4 24MB
6. Polynomial Regression/9. Polynomial Regression in R - Step 2.mp4 24MB
5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.vtt 24MB
5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 24MB
31. Artificial Neural Networks/4. How do Neural Networks work.mp4 24MB
16. Naive Bayes/6. Naive Bayes in Python.mp4 23MB
2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.mp4 23MB
21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 23MB
22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp4 23MB
8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 23MB
6. Polynomial Regression/11. Polynomial Regression in R - Step 4.mp4 22MB
34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 22MB
31. Artificial Neural Networks/13. ANN in Python - Step 2.vtt 22MB
29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.vtt 22MB
29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.mp4 22MB
10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp4 22MB
4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp4 22MB
39. XGBoost/2. XGBoost in Python - Step 1.mp4 21MB
2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.mp4 21MB
25. Eclat/3. Eclat in R.mp4 21MB
32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp4 21MB
2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.mp4 20MB
1. Welcome to the course!/7. Installing Python and Anaconda (Mac, Linux & Windows).mp4 20MB
18. Random Forest Classification/1. Random Forest Classification Intuition.mp4 19MB
10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 19MB
16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 19MB
17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 19MB
4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp4 19MB
19. Evaluating Classification Models Performance/4. CAP Curve.mp4 19MB
31. Artificial Neural Networks/6. Gradient Descent.mp4 19MB
31. Artificial Neural Networks/19. ANN in Python - Step 8.mp4 18MB
14. Support Vector Machine (SVM)/1. SVM Intuition.mp4 18MB
5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp4 18MB
6. Polynomial Regression/8. Polynomial Regression in R - Step 1.mp4 18MB
1. Welcome to the course!/8. Installing R and R Studio (Mac, Linux & Windows).mp4 18MB
29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp4 17MB
22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp4 17MB
5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 17MB
29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.mp4 17MB
31. Artificial Neural Networks/21. ANN in Python - Step 10.mp4 17MB
31. Artificial Neural Networks/20. ANN in Python - Step 9.mp4 17MB
31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 17MB
22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp4 17MB
31. Artificial Neural Networks/10. Business Problem Description.mp4 16MB
4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp4 16MB
21. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 15MB
29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.vtt 15MB
29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.mp4 15MB
31. Artificial Neural Networks/3. The Activation Function.mp4 15MB
12. Logistic Regression/11. Logistic Regression in R - Step 3.mp4 15MB
4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 14MB
5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 14MB
5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp4 14MB
31. Artificial Neural Networks/23. ANN in R - Step 2.mp4 14MB
32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14MB
28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14MB
29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.mp4 14MB
9. Random Forest Regression/1. Random Forest Regression Intuition.mp4 14MB
15. Kernel SVM/2. Mapping to a higher dimension.mp4 14MB
19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 14MB
29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp4 14MB
6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4 14MB
16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13MB
29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4 13MB
32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp4 13MB
12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 13MB
1. Welcome to the course!/2. Why Machine Learning is the Future.mp4 13MB
29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4 13MB
22. Hierarchical Clustering/6. HC in Python - Step 2.mp4 13MB
12. Logistic Regression/9. Logistic Regression in R - Step 1.mp4 13MB
12. Logistic Regression/14. R Classification Template.mp4 12MB
15. Kernel SVM/4. Types of Kernel Functions.mp4 12MB
22. Hierarchical Clustering/7. HC in Python - Step 3.mp4 12MB
12. Logistic Regression/8. Python Classification Template.mp4 12MB
22. Hierarchical Clustering/8. HC in Python - Step 4.mp4 12MB
13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp4 12MB
14. Support Vector Machine (SVM)/2. How to get the dataset.mp4 12MB
15. Kernel SVM/5. How to get the dataset.mp4 12MB
17. Decision Tree Classification/2. How to get the dataset.mp4 12MB
18. Random Forest Classification/2. How to get the dataset.mp4 12MB
22. Hierarchical Clustering/4. How to get the dataset.mp4 12MB
25. Eclat/2. How to get the dataset.mp4 12MB
32. Convolutional Neural Networks/10. How to get the dataset.mp4 12MB
35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp4 12MB
36. Kernel PCA/1. How to get the dataset.mp4 12MB
39. XGBoost/1. How to get the dataset.mp4 12MB
6. Polynomial Regression/2. How to get the dataset.mp4 12MB
7. Support Vector Regression (SVR)/1. How to get the dataset.mp4 12MB
12. Logistic Regression/2. How to get the dataset.mp4 12MB
16. Naive Bayes/5. How to get the dataset.mp4 12MB
21. K-Means Clustering/4. How to get the dataset.mp4 12MB
24. Apriori/2. How to get the dataset.mp4 12MB
27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4 12MB
28. Thompson Sampling/3. How to get the dataset.mp4 12MB
29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.mp4 12MB
31. Artificial Neural Networks/9. How to get the dataset.mp4 12MB
34. Principal Component Analysis (PCA)/2. How to get the dataset.mp4 12MB
38. Model Selection/1. How to get the dataset.mp4 12MB
4. Simple Linear Regression/1. How to get the dataset.mp4 12MB
5. Multiple Linear Regression/1. How to get the dataset.mp4 12MB
8. Decision Tree Regression/2. How to get the dataset.mp4 12MB
9. Random Forest Regression/2. How to get the dataset.mp4 12MB
19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 12MB
22. Hierarchical Clustering/11. HC in R - Step 2.mp4 11MB
2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.mp4 11MB
31. Artificial Neural Networks/8. Backpropagation.mp4 11MB
22. Hierarchical Clustering/5. HC in Python - Step 1.mp4 11MB
25. Eclat/1. Eclat Intuition.mp4 11MB
5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp4 10MB
12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 10MB
5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp4 10MB
32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp4 10MB
32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp4 10MB
4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 10MB
4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp4 9MB
6. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9MB
13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 9MB
27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 9MB
31. Artificial Neural Networks/18. ANN in Python - Step 7.mp4 9MB
10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 9MB
4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 9MB
28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 8MB
22. Hierarchical Clustering/9. HC in Python - Step 5.mp4 8MB
31. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 8MB
12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 8MB
19. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 8MB
1. Welcome to the course!/1. Applications of Machine Learning.mp4 8MB
32. Convolutional Neural Networks/8. Summary.mp4 8MB
12. Logistic Regression/10. Logistic Regression in R - Step 2.mp4 8MB
22. Hierarchical Clustering/12. HC in R - Step 3.mp4 8MB
29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp4 8MB
28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp4 7MB
22. Hierarchical Clustering/13. HC in R - Step 4.mp4 7MB
27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp4 7MB
22. Hierarchical Clustering/10. HC in R - Step 1.mp4 7MB
5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 7MB
31. Artificial Neural Networks/17. ANN in Python - Step 6.mp4 7MB
12. Logistic Regression/12. Logistic Regression in R - Step 4.mp4 7MB
22. Hierarchical Clustering/14. HC in R - Step 5.mp4 7MB
32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp4 7MB
4. Simple Linear Regression/2. Dataset + Business Problem Description.mp4 7MB
29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp4 7MB
29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.mp4 6MB
12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 6MB
32. Convolutional Neural Networks/1. Plan of attack.mp4 6MB
31. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 6MB
32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp4 6MB
15. Kernel SVM/1. Kernel SVM Intuition.mp4 6MB
4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp4 5MB
31. Artificial Neural Networks/1. Plan of attack.mp4 5MB
29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp4 5MB
5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp4 5MB
19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp4 4MB
29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.mp4 3MB
32. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3MB
2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.mp4 3MB
1. Welcome to the course!/4.1 Machine_Learning_A_Z_Q_A.pdf.pdf 2MB
32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp4 2MB
5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp4 2MB
5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp4 2MB
25. Eclat/3.1 Eclat.zip.zip 49KB
16. Naive Bayes/1. Bayes Theorem.vtt 31KB
18. Random Forest Classification/4. Random Forest Classification in R.vtt 29KB
8. Decision Tree Regression/4. Decision Tree Regression in R.vtt 29KB
6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.vtt 28KB
24. Apriori/5. Apriori in R - Step 3.vtt 28KB
24. Apriori/3. Apriori in R - Step 1.vtt 28KB
7. Support Vector Regression (SVR)/3. SVR in Python.vtt 27KB
6. Polynomial Regression/10. Polynomial Regression in R - Step 3.vtt 27KB
18. Random Forest Classification/3. Random Forest Classification in Python.vtt 27KB
36. Kernel PCA/3. Kernel PCA in R.vtt 27KB
12. Logistic Regression/7. Logistic Regression in Python - Step 5.vtt 26KB
12. Logistic Regression/13. Logistic Regression in R - Step 5.vtt 26KB
17. Decision Tree Classification/4. Decision Tree Classification in R.vtt 26KB
22. Hierarchical Clustering/16.1 Clustering-Pros-Cons.pdf.pdf 26KB
35. Linear Discriminant Analysis (LDA)/4. LDA in R.vtt 26KB
32. Convolutional Neural Networks/20. CNN in Python - Step 9.vtt 25KB
21. K-Means Clustering/5. K-Means Clustering in Python.vtt 25KB
28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.vtt 25KB
9. Random Forest Regression/4. Random Forest Regression in R.vtt 25KB
32. Convolutional Neural Networks/7. Step 4 - Full Connection.vtt 25KB
15. Kernel SVM/6. Kernel SVM in Python.vtt 25KB
24. Apriori/6. Apriori in Python - Step 1.vtt 25KB
5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.vtt 25KB
9. Random Forest Regression/3. Random Forest Regression in Python.vtt 24KB
28. Thompson Sampling/6. Thompson Sampling in R - Step 1.vtt 24KB
38. Model Selection/3. k-Fold Cross Validation in R.vtt 24KB
28. Thompson Sampling/1. Thompson Sampling Intuition.vtt 24KB
2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.vtt 24KB
2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.vtt 24KB
27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.vtt 23KB
35. Linear Discriminant Analysis (LDA)/3. LDA in Python.vtt 23KB
31. Artificial Neural Networks/22. ANN in R - Step 1.vtt 23KB
29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.vtt 23KB
15. Kernel SVM/7. Kernel SVM in R.vtt 23KB
24. Apriori/1. Apriori Intuition.vtt 23KB
39. XGBoost/4. XGBoost in R.vtt 23KB
32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.vtt 22KB
27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.vtt 22KB
31. Artificial Neural Networks/2. The Neuron.vtt 22KB
27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.vtt 22KB
5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.vtt 22KB
4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.vtt 21KB
8. Decision Tree Regression/3. Decision Tree Regression in Python.vtt 21KB
5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.vtt 21KB
29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.vtt 21KB
21. K-Means Clustering/1. K-Means Clustering Intuition.vtt 21KB
12. Logistic Regression/1. Logistic Regression Intuition.vtt 21KB
16. Naive Bayes/2. Naive Bayes Intuition.vtt 21KB
29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.vtt 21KB
2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.vtt 21KB
13. K-Nearest Neighbors (K-NN)/4. K-NN in R.vtt 21KB
24. Apriori/4. Apriori in R - Step 2.vtt 21KB
32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.vtt 20KB
24. Apriori/7. Apriori in Python - Step 2.vtt 20KB
4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.vtt 20KB
16. Naive Bayes/7. Naive Bayes in R.vtt 19KB
27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.vtt 19KB
2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.vtt 19KB
32. Convolutional Neural Networks/2. What are convolutional neural networks.vtt 19KB
38. Model Selection/4. Grid Search in Python - Step 1.vtt 19KB
27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.vtt 19KB
27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.vtt 19KB
27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.vtt 19KB
36. Kernel PCA/2. Kernel PCA in Python.vtt 19KB
13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.vtt 19KB
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29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.vtt 17KB
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32. Convolutional Neural Networks/21. CNN in Python - Step 10.vtt 11KB
4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.vtt 11KB
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14. Support Vector Machine (SVM)/4.1 SVM.zip.zip 8KB
31. Artificial Neural Networks/20. ANN in Python - Step 9.vtt 8KB
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32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.vtt 8KB
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12. Logistic Regression/8. Python Classification Template.vtt 5KB
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28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.vtt 5KB
31. Artificial Neural Networks/18. ANN in Python - Step 7.vtt 5KB
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28. Thompson Sampling/7. Thompson Sampling in R - Step 2.vtt 5KB
40. Bonus Lectures/1. YOUR SPECIAL BONUS.html 5KB
1. Welcome to the course!/1. Applications of Machine Learning.vtt 5KB
32. Convolutional Neural Networks/1. Plan of attack.vtt 5KB
31. Artificial Neural Networks/14. ANN in Python - Step 3.vtt 5KB
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14. Support Vector Machine (SVM)/2. How to get the dataset.vtt 4KB
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28. Thompson Sampling/3. How to get the dataset.vtt 4KB
29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.vtt 4KB
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32. Convolutional Neural Networks/10. How to get the dataset.vtt 4KB
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39. XGBoost/1. How to get the dataset.vtt 4KB
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9. Random Forest Regression/2. How to get the dataset.vtt 4KB
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31. Artificial Neural Networks/17. ANN in Python - Step 6.vtt 4KB
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15. Kernel SVM/1. Kernel SVM Intuition.vtt 4KB
32. Convolutional Neural Networks/19. CNN in Python - Step 8.vtt 4KB
27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.vtt 4KB
29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.vtt 4KB
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22. Hierarchical Clustering/14. HC in R - Step 5.vtt 4KB
12. Logistic Regression/5. Logistic Regression in Python - Step 3.vtt 4KB
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22. Hierarchical Clustering/13. HC in R - Step 4.vtt 3KB
31. Artificial Neural Networks/15. ANN in Python - Step 4.vtt 3KB
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19. Evaluating Classification Models Performance/3. Accuracy Paradox.vtt 3KB
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32. Convolutional Neural Networks/22. CNN in R.html 2KB
29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.vtt 2KB
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39. XGBoost/5. THANK YOU bonus video.vtt 2KB
29. -------------------- Part 7 Natural Language Processing --------------------/1. Welcome to Part 7 - Natural Language Processing.html 2KB
2. -------------------- Part 1 Data Preprocessing --------------------/5. For Python learners, summary of Object-oriented programming classes & objects.html 2KB
32. Convolutional Neural Networks/14. CNN in Python - Step 3.vtt 2KB
1. Welcome to the course!/4. This PDF resource will help you a lot.html 1KB
5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.vtt 1KB
31. Artificial Neural Networks/11. Installing Keras.html 1KB
29. -------------------- Part 7 Natural Language Processing --------------------/25. Homework Challenge.html 1KB
29. -------------------- Part 7 Natural Language Processing --------------------/14. Homework Challenge.html 1KB
5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.vtt 1KB
33. -------------------- Part 9 Dimensionality Reduction --------------------/1. Welcome to Part 9 - Dimensionality Reduction.html 1KB
1. Welcome to the course!/9. BONUS Meet your instructors.html 1KB
1. Welcome to the course!/5. The whole code folder of the course.html 1KB
32. Convolutional Neural Networks/11. Installing Keras.html 927B
37. -------------------- Part 10 Model Selection & Boosting --------------------/1. Welcome to Part 10 - Model Selection & Boosting.html 899B
3. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html 875B
30. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html 870B
11. -------------------- Part 3 Classification --------------------/1. Welcome to Part 3 - Classification.html 831B
26. -------------------- Part 6 Reinforcement Learning --------------------/1. Welcome to Part 6 - Reinforcement Learning.html 804B
2. -------------------- Part 1 Data Preprocessing --------------------/8. WARNING - Update.html 783B
20. -------------------- Part 4 Clustering --------------------/1. Welcome to Part 4 - Clustering.html 734B
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5. Multiple Linear Regression/7. Prerequisites What is the P-Value.html 676B
22. Hierarchical Clustering/16. Conclusion of Part 4 - Clustering.html 516B
23. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html 425B
[FreeCourseLab.com].url 126B
12. Logistic Regression/15. Logistic Regression.html 118B
13. K-Nearest Neighbors (K-NN)/5. K-Nearest Neighbor.html 118B
2. -------------------- Part 1 Data Preprocessing --------------------/12. Data Preprocessing.html 118B
21. K-Means Clustering/7. K-Means Clustering.html 118B
22. Hierarchical Clustering/15. Hierarchical Clustering.html 118B
4. Simple Linear Regression/13. Simple Linear Regression.html 118B
5. Multiple Linear Regression/22. Multiple Linear Regression.html 118B