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

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

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收录时间：2020-05-24
最近下载：2024-03-22

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文件列表

  • 1. Welcome to the course!/6.1 Machine_Learning_A-Z_New.zip.zip 239.5 MB
  • 36. Kernel PCA/3. Kernel PCA in R.mp4 59.3 MB
  • 1. Welcome to the course!/7. Updates on Udemy Reviews.mp4 55.5 MB
  • 39. XGBoost/5. THANK YOU bonus video.mp4 54.8 MB
  • 12. Logistic Regression/13. Logistic Regression in R - Step 5.mp4 54.2 MB
  • 35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp4 53.8 MB
  • 17. Decision Tree Classification/4. Decision Tree Classification in R.mp4 53.7 MB
  • 18. Random Forest Classification/4. Random Forest Classification in R.mp4 51.8 MB
  • 31. Artificial Neural Networks/13. ANN in Python - Step 2.mp4 50.4 MB
  • 39. XGBoost/4. XGBoost in R.mp4 49.6 MB
  • 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.srt 49.5 MB
  • 27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp4 49.5 MB
  • 18. Random Forest Classification/3. Random Forest Classification in Python.mp4 49.4 MB
  • 32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp4 49.1 MB
  • 7. Support Vector Regression (SVR)/2. SVR Intuition.mp4 48.9 MB
  • 7. Support Vector Regression (SVR)/3. SVR in Python.mp4 48.4 MB
  • 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4 47.6 MB
  • 8. Decision Tree Regression/4. Decision Tree Regression in R.mp4 46.5 MB
  • 16. Naive Bayes/1. Bayes Theorem.mp4 46.0 MB
  • 24. Apriori/5. Apriori in R - Step 3.mp4 46.0 MB
  • 38. Model Selection/3. k-Fold Cross Validation in R.mp4 45.8 MB
  • 6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp4 45.4 MB
  • 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp4 45.2 MB
  • 6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp4 45.1 MB
  • 24. Apriori/3. Apriori in R - Step 1.mp4 45.0 MB
  • 32. Convolutional Neural Networks/7. Step 4 - Full Connection.srt 44.9 MB
  • 32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp4 44.8 MB
  • 12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp4 44.6 MB
  • 15. Kernel SVM/6. Kernel SVM in Python.mp4 43.6 MB
  • 13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp4 43.4 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp4 43.2 MB
  • 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp4 43.1 MB
  • 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp4 42.9 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp4 42.8 MB
  • 15. Kernel SVM/7. Kernel SVM in R.mp4 42.4 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp4 42.3 MB
  • 9. Random Forest Regression/4. Random Forest Regression in R.mp4 42.3 MB
  • 32. Convolutional Neural Networks/5. Step 2 - Pooling.mp4 42.2 MB
  • 21. K-Means Clustering/5. K-Means Clustering in Python.mp4 41.7 MB
  • 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp4 41.7 MB
  • 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp4 41.5 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp4 41.4 MB
  • 9. Random Forest Regression/3. Random Forest Regression in Python.mp4 41.4 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp4 40.9 MB
  • 31. Artificial Neural Networks/22. ANN in R - Step 1.mp4 40.4 MB
  • 38. Model Selection/4. Grid Search in Python - Step 1.mp4 40.1 MB
  • 24. Apriori/6. Apriori in Python - Step 1.mp4 39.8 MB
  • 4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp4 39.2 MB
  • 16. Naive Bayes/7. Naive Bayes in R.mp4 39.1 MB
  • 28. Thompson Sampling/1. Thompson Sampling Intuition.mp4 39.1 MB
  • 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp4 38.5 MB
  • 38. Model Selection/6. Grid Search in R.mp4 37.3 MB
  • 27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp4 37.2 MB
  • 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4 36.9 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.mp4 36.9 MB
  • 24. Apriori/1. Apriori Intuition.mp4 36.7 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.mp4 36.3 MB
  • 8. Decision Tree Regression/3. Decision Tree Regression in Python.mp4 35.2 MB
  • 31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp4 35.1 MB
  • 36. Kernel PCA/2. Kernel PCA in Python.mp4 35.0 MB
  • 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp4 34.8 MB
  • 38. Model Selection/2. k-Fold Cross Validation in Python.mp4 34.4 MB
  • 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp4 34.2 MB
  • 14. Support Vector Machine (SVM)/4. SVM in R.mp4 33.8 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.mp4 33.7 MB
  • 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp4 33.7 MB
  • 39. XGBoost/3. XGBoost in Python - Step 2.mp4 33.5 MB
  • 34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4 33.5 MB
  • 27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp4 33.1 MB
  • 30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp4 32.8 MB
  • 14. Support Vector Machine (SVM)/3. SVM in Python.mp4 32.7 MB
  • 32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp4 32.5 MB
  • 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp4 32.3 MB
  • 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp4 32.1 MB
  • 24. Apriori/4. Apriori in R - Step 2.mp4 32.0 MB
  • 27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp4 31.7 MB
  • 31. Artificial Neural Networks/2. The Neuron.mp4 31.3 MB
  • 17. Decision Tree Classification/3. Decision Tree Classification in Python.mp4 31.2 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.mp4 31.1 MB
  • 31. Artificial Neural Networks/16. ANN in Python - Step 5.mp4 31.0 MB
  • 24. Apriori/7. Apriori in Python - Step 2.mp4 31.0 MB
  • 38. Model Selection/5. Grid Search in Python - Step 2.mp4 30.9 MB
  • 32. Convolutional Neural Networks/2. What are convolutional neural networks.mp4 30.9 MB
  • 27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp4 30.7 MB
  • 31. Artificial Neural Networks/12. ANN in Python - Step 1.mp4 30.7 MB
  • 15. Kernel SVM/3. The Kernel Trick.mp4 30.7 MB
  • 12. Logistic Regression/1. Logistic Regression Intuition.mp4 30.6 MB
  • 34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp4 30.4 MB
  • 27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp4 30.4 MB
  • 21. K-Means Clustering/6. K-Means Clustering in R.mp4 30.4 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp4 30.4 MB
  • 31. Artificial Neural Networks/24. ANN in R - Step 3.mp4 30.3 MB
  • 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp4 30.2 MB
  • 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp4 29.4 MB
  • 16. Naive Bayes/2. Naive Bayes Intuition.mp4 29.1 MB
  • 6. Polynomial Regression/7. Python Regression Template.mp4 28.8 MB
  • 32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp4 28.5 MB
  • 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp4 28.5 MB
  • 6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp4 28.4 MB
  • 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp4 28.3 MB
  • 24. Apriori/8. Apriori in Python - Step 3.mp4 28.3 MB
  • 21. K-Means Clustering/1. K-Means Clustering Intuition.mp4 28.2 MB
  • 31. Artificial Neural Networks/5. How do Neural Networks learn.mp4 27.9 MB
  • 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp4 27.2 MB
  • 7. Support Vector Regression (SVR)/4. SVR in R.mp4 27.1 MB
  • 34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp4 26.7 MB
  • 6. Polynomial Regression/12. R Regression Template.mp4 26.6 MB
  • 32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp4 26.1 MB
  • 6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp4 26.1 MB
  • 10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp4 25.4 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.mp4 25.3 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.mp4 25.2 MB
  • 6. Polynomial Regression/9. Polynomial Regression in R - Step 2.mp4 25.0 MB
  • 5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp4 25.0 MB
  • 31. Artificial Neural Networks/4. How do Neural Networks work.mp4 24.7 MB
  • 16. Naive Bayes/6. Naive Bayes in Python.mp4 24.5 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.mp4 24.4 MB
  • 21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp4 24.3 MB
  • 22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp4 23.9 MB
  • 8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp4 23.8 MB
  • 6. Polynomial Regression/11. Polynomial Regression in R - Step 4.mp4 23.4 MB
  • 34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp4 23.1 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.mp4 23.0 MB
  • 10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp4 23.0 MB
  • 4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp4 22.8 MB
  • 39. XGBoost/2. XGBoost in Python - Step 1.mp4 22.4 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.mp4 22.2 MB
  • 25. Eclat/3. Eclat in R.mp4 21.7 MB
  • 32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp4 21.6 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.mp4 20.6 MB
  • 1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).mp4 20.5 MB
  • 18. Random Forest Classification/1. Random Forest Classification Intuition.mp4 20.4 MB
  • 10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp4 20.2 MB
  • 16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp4 19.9 MB
  • 17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp4 19.7 MB
  • 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp4 19.7 MB
  • 19. Evaluating Classification Models Performance/4. CAP Curve.mp4 19.6 MB
  • 31. Artificial Neural Networks/6. Gradient Descent.mp4 19.4 MB
  • 31. Artificial Neural Networks/19. ANN in Python - Step 8.mp4 19.0 MB
  • 14. Support Vector Machine (SVM)/1. SVM Intuition.mp4 18.9 MB
  • 5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp4 18.8 MB
  • 6. Polynomial Regression/8. Polynomial Regression in R - Step 1.mp4 18.6 MB
  • 1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp4 18.4 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp4 18.3 MB
  • 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp4 18.3 MB
  • 5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp4 18.1 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.mp4 17.9 MB
  • 31. Artificial Neural Networks/21. ANN in Python - Step 10.mp4 17.9 MB
  • 31. Artificial Neural Networks/20. ANN in Python - Step 9.mp4 17.7 MB
  • 31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp4 17.6 MB
  • 22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp4 17.3 MB
  • 31. Artificial Neural Networks/10. Business Problem Description.mp4 17.2 MB
  • 4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp4 16.4 MB
  • 21. K-Means Clustering/2. K-Means Random Initialization Trap.mp4 16.1 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.mp4 15.6 MB
  • 31. Artificial Neural Networks/3. The Activation Function.mp4 15.5 MB
  • 12. Logistic Regression/11. Logistic Regression in R - Step 3.mp4 15.3 MB
  • 4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp4 15.1 MB
  • 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp4 15.0 MB
  • 5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp4 15.0 MB
  • 31. Artificial Neural Networks/23. ANN in R - Step 2.mp4 14.9 MB
  • 32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp4 14.8 MB
  • 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp4 14.8 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.mp4 14.7 MB
  • 9. Random Forest Regression/1. Random Forest Regression Intuition.mp4 14.5 MB
  • 15. Kernel SVM/2. Mapping to a higher dimension.mp4 14.4 MB
  • 19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp4 14.3 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp4 14.2 MB
  • 6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp4 14.2 MB
  • 5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.srt 14.0 MB
  • 16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp4 13.9 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp4 13.9 MB
  • 32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp4 13.6 MB
  • 12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp4 13.6 MB
  • 1. Welcome to the course!/3. Why Machine Learning is the Future.mp4 13.4 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp4 13.3 MB
  • 22. Hierarchical Clustering/6. HC in Python - Step 2.mp4 13.3 MB
  • 12. Logistic Regression/9. Logistic Regression in R - Step 1.mp4 13.2 MB
  • 12. Logistic Regression/14. R Classification Template.mp4 13.1 MB
  • 22. Hierarchical Clustering/7. HC in Python - Step 3.mp4 12.9 MB
  • 15. Kernel SVM/4. Types of Kernel Functions.mp4 12.9 MB
  • 12. Logistic Regression/8. Python Classification Template.mp4 12.7 MB
  • 22. Hierarchical Clustering/8. HC in Python - Step 4.mp4 12.6 MB
  • 12. Logistic Regression/2. How to get the dataset.mp4 12.3 MB
  • 17. Decision Tree Classification/2. How to get the dataset.mp4 12.3 MB
  • 32. Convolutional Neural Networks/10. How to get the dataset.mp4 12.3 MB
  • 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp4 12.3 MB
  • 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp4 12.3 MB
  • 14. Support Vector Machine (SVM)/2. How to get the dataset.mp4 12.3 MB
  • 15. Kernel SVM/5. How to get the dataset.mp4 12.3 MB
  • 16. Naive Bayes/5. How to get the dataset.mp4 12.3 MB
  • 18. Random Forest Classification/2. How to get the dataset.mp4 12.3 MB
  • 21. K-Means Clustering/4. How to get the dataset.mp4 12.3 MB
  • 22. Hierarchical Clustering/4. How to get the dataset.mp4 12.3 MB
  • 24. Apriori/2. How to get the dataset.mp4 12.3 MB
  • 25. Eclat/2. How to get the dataset.mp4 12.3 MB
  • 27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp4 12.3 MB
  • 28. Thompson Sampling/3. How to get the dataset.mp4 12.3 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.mp4 12.3 MB
  • 31. Artificial Neural Networks/9. How to get the dataset.mp4 12.3 MB
  • 34. Principal Component Analysis (PCA)/2. How to get the dataset.mp4 12.3 MB
  • 36. Kernel PCA/1. How to get the dataset.mp4 12.3 MB
  • 38. Model Selection/1. How to get the dataset.mp4 12.3 MB
  • 39. XGBoost/1. How to get the dataset.mp4 12.3 MB
  • 4. Simple Linear Regression/1. How to get the dataset.mp4 12.3 MB
  • 5. Multiple Linear Regression/1. How to get the dataset.mp4 12.3 MB
  • 6. Polynomial Regression/2. How to get the dataset.mp4 12.3 MB
  • 7. Support Vector Regression (SVR)/1. How to get the dataset.mp4 12.3 MB
  • 8. Decision Tree Regression/2. How to get the dataset.mp4 12.3 MB
  • 9. Random Forest Regression/2. How to get the dataset.mp4 12.3 MB
  • 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp4 12.1 MB
  • 22. Hierarchical Clustering/11. HC in R - Step 2.mp4 11.7 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.mp4 11.6 MB
  • 31. Artificial Neural Networks/8. Backpropagation.mp4 11.5 MB
  • 22. Hierarchical Clustering/5. HC in Python - Step 1.mp4 11.2 MB
  • 25. Eclat/1. Eclat Intuition.mp4 11.2 MB
  • 5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp4 10.9 MB
  • 12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp4 10.9 MB
  • 5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp4 10.5 MB
  • 32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp4 10.4 MB
  • 32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp4 10.2 MB
  • 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp4 10.0 MB
  • 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp4 9.9 MB
  • 6. Polynomial Regression/1. Polynomial Regression Intuition.mp4 9.9 MB
  • 13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp4 9.7 MB
  • 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp4 9.6 MB
  • 31. Artificial Neural Networks/18. ANN in Python - Step 7.mp4 9.4 MB
  • 10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp4 9.3 MB
  • 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp4 9.1 MB
  • 28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp4 8.8 MB
  • 22. Hierarchical Clustering/9. HC in Python - Step 5.mp4 8.8 MB
  • 31. Artificial Neural Networks/14. ANN in Python - Step 3.mp4 8.8 MB
  • 12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp4 8.6 MB
  • 19. Evaluating Classification Models Performance/2. Confusion Matrix.mp4 8.6 MB
  • 1. Welcome to the course!/1. Applications of Machine Learning.mp4 8.4 MB
  • 32. Convolutional Neural Networks/8. Summary.mp4 8.3 MB
  • 12. Logistic Regression/10. Logistic Regression in R - Step 2.mp4 8.2 MB
  • 22. Hierarchical Clustering/12. HC in R - Step 3.mp4 8.2 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp4 7.9 MB
  • 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp4 7.8 MB
  • 22. Hierarchical Clustering/13. HC in R - Step 4.mp4 7.8 MB
  • 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp4 7.8 MB
  • 22. Hierarchical Clustering/10. HC in R - Step 1.mp4 7.8 MB
  • 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp4 7.6 MB
  • 31. Artificial Neural Networks/17. ANN in Python - Step 6.mp4 7.4 MB
  • 12. Logistic Regression/12. Logistic Regression in R - Step 4.mp4 7.2 MB
  • 22. Hierarchical Clustering/14. HC in R - Step 5.mp4 7.2 MB
  • 32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp4 7.1 MB
  • 4. Simple Linear Regression/2. Dataset + Business Problem Description.mp4 7.0 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.mp4 6.8 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp4 6.8 MB
  • 36. Kernel PCA/2. Kernel PCA in Python.srt 6.3 MB
  • 12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp4 6.3 MB
  • 32. Convolutional Neural Networks/1. Plan of attack.mp4 6.2 MB
  • 31. Artificial Neural Networks/15. ANN in Python - Step 4.mp4 6.2 MB
  • 32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp4 6.1 MB
  • 15. Kernel SVM/1. Kernel SVM Intuition.mp4 6.1 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.srt 5.9 MB
  • 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp4 5.6 MB
  • 31. Artificial Neural Networks/1. Plan of attack.mp4 5.0 MB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp4 4.8 MB
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  • 19. Evaluating Classification Models Performance/3. Accuracy Paradox.srt 4.0 MB
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  • 29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.mp4 3.5 MB
  • 32. Convolutional Neural Networks/6. Step 3 - Flattening.mp4 3.4 MB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.mp4 3.1 MB
  • 1. Welcome to the course!/5.1 Machine_Learning_A_Z_Q_A.pdf.pdf 2.4 MB
  • 32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp4 2.3 MB
  • 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp4 1.9 MB
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  • 16. Naive Bayes/1. Bayes Theorem.srt 35.3 kB
  • 18. Random Forest Classification/4. Random Forest Classification in R.srt 33.2 kB
  • 8. Decision Tree Regression/4. Decision Tree Regression in R.srt 32.9 kB
  • 6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.srt 32.2 kB
  • 24. Apriori/5. Apriori in R - Step 3.srt 31.9 kB
  • 24. Apriori/3. Apriori in R - Step 1.srt 31.8 kB
  • 7. Support Vector Regression (SVR)/3. SVR in Python.srt 31.6 kB
  • 6. Polynomial Regression/10. Polynomial Regression in R - Step 3.srt 31.6 kB
  • 36. Kernel PCA/3. Kernel PCA in R.srt 31.5 kB
  • 18. Random Forest Classification/3. Random Forest Classification in Python.srt 31.5 kB
  • 12. Logistic Regression/7. Logistic Regression in Python - Step 5.srt 30.4 kB
  • 35. Linear Discriminant Analysis (LDA)/4. LDA in R.srt 30.4 kB
  • 32. Convolutional Neural Networks/20. CNN in Python - Step 9.srt 30.1 kB
  • 17. Decision Tree Classification/4. Decision Tree Classification in R.srt 29.8 kB
  • 12. Logistic Regression/13. Logistic Regression in R - Step 5.srt 29.8 kB
  • 31. Artificial Neural Networks/13. ANN in Python - Step 2.srt 29.6 kB
  • 28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.srt 29.6 kB
  • 15. Kernel SVM/6. Kernel SVM in Python.srt 28.9 kB
  • 21. K-Means Clustering/5. K-Means Clustering in Python.srt 28.9 kB
  • 9. Random Forest Regression/4. Random Forest Regression in R.srt 28.8 kB
  • 24. Apriori/6. Apriori in Python - Step 1.srt 28.6 kB
  • 38. Model Selection/3. k-Fold Cross Validation in R.srt 28.6 kB
  • 28. Thompson Sampling/6. Thompson Sampling in R - Step 1.srt 28.5 kB
  • 9. Random Forest Regression/3. Random Forest Regression in Python.srt 28.2 kB
  • 28. Thompson Sampling/1. Thompson Sampling Intuition.srt 28.2 kB
  • 5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.srt 28.1 kB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.srt 27.7 kB
  • 27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.srt 27.6 kB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.srt 27.6 kB
  • 31. Artificial Neural Networks/22. ANN in R - Step 1.srt 27.4 kB
  • 35. Linear Discriminant Analysis (LDA)/3. LDA in Python.srt 27.1 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.srt 26.9 kB
  • 39. XGBoost/4. XGBoost in R.srt 26.6 kB
  • 24. Apriori/1. Apriori Intuition.srt 26.5 kB
  • 22. Hierarchical Clustering/16.1 Clustering-Pros-Cons.pdf.pdf 26.4 kB
  • 15. Kernel SVM/7. Kernel SVM in R.srt 26.1 kB
  • 32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.srt 25.9 kB
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  • 31. Artificial Neural Networks/2. The Neuron.srt 25.6 kB
  • 5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.srt 25.0 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.srt 24.6 kB
  • 12. Logistic Regression/1. Logistic Regression Intuition.srt 24.5 kB
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  • 29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.srt 24.4 kB
  • 8. Decision Tree Regression/3. Decision Tree Regression in Python.srt 24.3 kB
  • 5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.srt 24.1 kB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.srt 24.0 kB
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  • 21. K-Means Clustering/1. K-Means Clustering Intuition.srt 23.9 kB
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  • 2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.srt 23.2 kB
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  • 4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.srt 23.0 kB
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  • 32. Convolutional Neural Networks/2. What are convolutional neural networks.srt 22.6 kB
  • 38. Model Selection/4. Grid Search in Python - Step 1.srt 22.6 kB
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  • 16. Naive Bayes/7. Naive Bayes in R.srt 22.4 kB
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  • 13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.srt 21.7 kB
  • 32. Convolutional Neural Networks/5. Step 2 - Pooling.srt 21.5 kB
  • 38. Model Selection/6. Grid Search in R.srt 21.4 kB
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  • 27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.srt 21.0 kB
  • 38. Model Selection/2. k-Fold Cross Validation in Python.srt 20.7 kB
  • 31. Artificial Neural Networks/12. ANN in Python - Step 1.srt 20.5 kB
  • 34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.srt 20.2 kB
  • 5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.srt 20.2 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.srt 20.1 kB
  • 24. Apriori/8. Apriori in Python - Step 3.srt 20.1 kB
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  • 21. K-Means Clustering/6. K-Means Clustering in R.srt 19.9 kB
  • 17. Decision Tree Classification/3. Decision Tree Classification in Python.srt 19.9 kB
  • 32. Convolutional Neural Networks/15. CNN in Python - Step 4.srt 19.7 kB
  • 14. Support Vector Machine (SVM)/3. SVM in Python.srt 19.6 kB
  • 31. Artificial Neural Networks/4. How do Neural Networks work.srt 19.6 kB
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  • 39. XGBoost/3. XGBoost in Python - Step 2.srt 19.3 kB
  • 31. Artificial Neural Networks/24. ANN in R - Step 3.srt 19.3 kB
  • 34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.srt 19.1 kB
  • 7. Support Vector Regression (SVR)/4. SVR in R.srt 19.1 kB
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  • 30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.srt 18.6 kB
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  • 8. Decision Tree Regression/1. Decision Tree Regression Intuition.srt 17.5 kB
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  • 15. Kernel SVM/3. The Kernel Trick.srt 16.9 kB
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  • 6. Polynomial Regression/11. Polynomial Regression in R - Step 4.srt 15.8 kB
  • 5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.srt 15.8 kB
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  • 29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.srt 14.7 kB
  • 22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.srt 14.7 kB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.srt 14.5 kB
  • 6. Polynomial Regression/8. Polynomial Regression in R - Step 1.srt 14.5 kB
  • 31. Artificial Neural Networks/6. Gradient Descent.srt 14.4 kB
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  • 29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.srt 13.2 kB
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  • 4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.srt 12.6 kB
  • 1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).srt 12.6 kB
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  • 28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.srt 11.4 kB
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  • 19. Evaluating Classification Models Performance/5. CAP Curve Analysis.srt 9.5 kB
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  • 12. Logistic Regression/9. Logistic Regression in R - Step 1.srt 9.1 kB
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  • 6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.srt 8.9 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.srt 8.6 kB
  • 4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.srt 8.5 kB
  • 5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.srt 8.5 kB
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  • 25. Eclat/1. Eclat Intuition.srt 8.3 kB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.srt 8.3 kB
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  • 6. Polynomial Regression/1. Polynomial Regression Intuition.srt 8.0 kB
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  • 4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.srt 7.9 kB
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  • 12. Logistic Regression/11. Logistic Regression in R - Step 3.srt 7.6 kB
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  • 12. Logistic Regression/6. Logistic Regression in Python - Step 4.srt 7.3 kB
  • 31. Artificial Neural Networks/8. Backpropagation.srt 7.3 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.srt 7.2 kB
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  • 18. Random Forest Classification/1. Random Forest Classification Intuition.srt 7.2 kB
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  • 32. Convolutional Neural Networks/8. Summary.srt 6.2 kB
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  • 31. Artificial Neural Networks/18. ANN in Python - Step 7.srt 5.8 kB
  • 5. Multiple Linear Regression/2. Dataset + Business Problem Description.srt 5.8 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.srt 5.7 kB
  • 4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.srt 5.6 kB
  • 1. Welcome to the course!/1. Applications of Machine Learning.srt 5.4 kB
  • 28. Thompson Sampling/7. Thompson Sampling in R - Step 2.srt 5.4 kB
  • 32. Convolutional Neural Networks/1. Plan of attack.srt 5.4 kB
  • 31. Artificial Neural Networks/14. ANN in Python - Step 3.srt 5.3 kB
  • 35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.srt 5.2 kB
  • 34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.srt 5.2 kB
  • 27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.srt 5.1 kB
  • 15. Kernel SVM/4. Types of Kernel Functions.srt 5.1 kB
  • 12. Logistic Regression/4. Logistic Regression in Python - Step 2.srt 5.0 kB
  • 12. Logistic Regression/2. How to get the dataset.srt 4.9 kB
  • 13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.srt 4.9 kB
  • 14. Support Vector Machine (SVM)/2. How to get the dataset.srt 4.9 kB
  • 15. Kernel SVM/5. How to get the dataset.srt 4.9 kB
  • 16. Naive Bayes/5. How to get the dataset.srt 4.9 kB
  • 17. Decision Tree Classification/2. How to get the dataset.srt 4.9 kB
  • 18. Random Forest Classification/2. How to get the dataset.srt 4.9 kB
  • 21. K-Means Clustering/4. How to get the dataset.srt 4.9 kB
  • 22. Hierarchical Clustering/4. How to get the dataset.srt 4.9 kB
  • 24. Apriori/2. How to get the dataset.srt 4.9 kB
  • 25. Eclat/2. How to get the dataset.srt 4.9 kB
  • 27. Upper Confidence Bound (UCB)/3. How to get the dataset.srt 4.9 kB
  • 28. Thompson Sampling/3. How to get the dataset.srt 4.9 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.srt 4.9 kB
  • 31. Artificial Neural Networks/9. How to get the dataset.srt 4.9 kB
  • 32. Convolutional Neural Networks/10. How to get the dataset.srt 4.9 kB
  • 34. Principal Component Analysis (PCA)/2. How to get the dataset.srt 4.9 kB
  • 35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.srt 4.9 kB
  • 36. Kernel PCA/1. How to get the dataset.srt 4.9 kB
  • 38. Model Selection/1. How to get the dataset.srt 4.9 kB
  • 39. XGBoost/1. How to get the dataset.srt 4.9 kB
  • 4. Simple Linear Regression/1. How to get the dataset.srt 4.9 kB
  • 5. Multiple Linear Regression/1. How to get the dataset.srt 4.9 kB
  • 6. Polynomial Regression/2. How to get the dataset.srt 4.9 kB
  • 7. Support Vector Regression (SVR)/1. How to get the dataset.srt 4.9 kB
  • 8. Decision Tree Regression/2. How to get the dataset.srt 4.9 kB
  • 9. Random Forest Regression/2. How to get the dataset.srt 4.9 kB
  • 22. Hierarchical Clustering/12. HC in R - Step 3.srt 4.8 kB
  • 40. Bonus Lectures/1. YOUR SPECIAL BONUS.html 4.8 kB
  • 32. Convolutional Neural Networks/19. CNN in Python - Step 8.srt 4.7 kB
  • 31. Artificial Neural Networks/17. ANN in Python - Step 6.srt 4.6 kB
  • 32. Convolutional Neural Networks/13. CNN in Python - Step 2.srt 4.6 kB
  • 27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.srt 4.5 kB
  • 15. Kernel SVM/1. Kernel SVM Intuition.srt 4.5 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.srt 4.5 kB
  • 12. Logistic Regression/10. Logistic Regression in R - Step 2.srt 4.5 kB
  • 4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.srt 4.4 kB
  • 12. Logistic Regression/5. Logistic Regression in Python - Step 3.srt 4.2 kB
  • 4. Simple Linear Regression/2. Dataset + Business Problem Description.srt 4.2 kB
  • 5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.srt 4.2 kB
  • 1. Welcome to the course!/7. Updates on Udemy Reviews.srt 4.1 kB
  • 22. Hierarchical Clustering/14. HC in R - Step 5.srt 4.1 kB
  • 31. Artificial Neural Networks/1. Plan of attack.srt 4.1 kB
  • 12. Logistic Regression/12. Logistic Regression in R - Step 4.srt 4.1 kB
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  • 5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.srt 3.6 kB
  • 19. Evaluating Classification Models Performance/6. Conclusion of Part 3 - Classification.html 3.6 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.srt 3.3 kB
  • 1. Welcome to the course!/4. Important notes, tips & tricks for this course.html 3.3 kB
  • 10. Evaluating Regression Models Performance/5. Conclusion of Part 2 - Regression.html 3.0 kB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/8. WARNING - Update.html 2.9 kB
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  • 32. Convolutional Neural Networks/6. Step 3 - Flattening.srt 2.6 kB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.srt 2.6 kB
  • 32. Convolutional Neural Networks/22. CNN in R.html 2.4 kB
  • 1. Welcome to the course!/2. BONUS Learning Paths.html 2.4 kB
  • 39. XGBoost/5. THANK YOU bonus video.srt 2.4 kB
  • 5. Multiple Linear Regression/15. Multiple Linear Regression in Python - Automatic Backward Elimination.html 2.2 kB
  • 1. Welcome to the course!/13. FAQBot!.html 1.8 kB
  • 32. Convolutional Neural Networks/14. CNN in Python - Step 3.srt 1.8 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/1. Welcome to Part 7 - Natural Language Processing.html 1.7 kB
  • 2. -------------------- Part 1 Data Preprocessing --------------------/5. For Python learners, summary of Object-oriented programming classes & objects.html 1.6 kB
  • 5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.srt 1.6 kB
  • 1. Welcome to the course!/5. This PDF resource will help you a lot.html 1.5 kB
  • 5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.srt 1.5 kB
  • 31. Artificial Neural Networks/11. Installing Keras.html 1.4 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/25. Homework Challenge.html 1.4 kB
  • 29. -------------------- Part 7 Natural Language Processing --------------------/14. Homework Challenge.html 1.4 kB
  • 1. Welcome to the course!/9. Update Recommended Anaconda Version.html 1.4 kB
  • 33. -------------------- Part 9 Dimensionality Reduction --------------------/1. Welcome to Part 9 - Dimensionality Reduction.html 1.3 kB
  • 26. -------------------- Part 6 Reinforcement Learning --------------------/1. Welcome to Part 6 - Reinforcement Learning.html 1.2 kB
  • 1. Welcome to the course!/11. BONUS Meet your instructors.html 1.1 kB
  • 1. Welcome to the course!/6. The whole code folder of the course.html 1.0 kB
  • 32. Convolutional Neural Networks/11. Installing Keras.html 927 Bytes
  • 37. -------------------- Part 10 Model Selection & Boosting --------------------/1. Welcome to Part 10 - Model Selection & Boosting.html 899 Bytes
  • 3. -------------------- Part 2 Regression --------------------/1. Welcome to Part 2 - Regression.html 875 Bytes
  • 30. -------------------- Part 8 Deep Learning --------------------/1. Welcome to Part 8 - Deep Learning.html 870 Bytes
  • 11. -------------------- Part 3 Classification --------------------/1. Welcome to Part 3 - Classification.html 831 Bytes
  • 20. -------------------- Part 4 Clustering --------------------/1. Welcome to Part 4 - Clustering.html 734 Bytes
  • 5. Multiple Linear Regression/21. Multiple Linear Regression in R - Automatic Backward Elimination.html 726 Bytes
  • 5. Multiple Linear Regression/7. Prerequisites What is the P-Value.html 676 Bytes
  • 1. Welcome to the course!/12. Some Additional Resources.html 551 Bytes
  • 22. Hierarchical Clustering/16. Conclusion of Part 4 - Clustering.html 516 Bytes
  • 23. -------------------- Part 5 Association Rule Learning --------------------/1. Welcome to Part 5 - Association Rule Learning.html 425 Bytes
  • 12. Logistic Regression/15. Logistic Regression.html 125 Bytes
  • 13. K-Nearest Neighbors (K-NN)/5. K-Nearest Neighbor.html 125 Bytes
  • 2. -------------------- Part 1 Data Preprocessing --------------------/12. Data Preprocessing.html 125 Bytes
  • 21. K-Means Clustering/7. K-Means Clustering.html 125 Bytes
  • 22. Hierarchical Clustering/15. Hierarchical Clustering.html 125 Bytes
  • 4. Simple Linear Regression/13. Simple Linear Regression.html 125 Bytes
  • 5. Multiple Linear Regression/22. Multiple Linear Regression.html 125 Bytes

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