[Tutorialsplanet.NET] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science番号下载_[Tutorialsplanet.NET] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science番号下载_[Tutorialsplanet.NET] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science下载

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

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种子名称： [Tutorialsplanet.NET] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science
文件类型：视频
文件数目： 263个文件
文件大小： 5.7 GB
收录时间： 2020-11-25 16:05
已经下载： 3次
资源热度： 339
最近下载： 2024-11-29 02:51

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

1. Welcome to the course!/1. Applications of Machine Learning.mp47.99MB

1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp417.55MB

1. Welcome to the course!/3. Why Machine Learning is the Future.mp412.81MB

1. Welcome to the course!/7. Updates on Udemy Reviews.mp452.92MB

1. Welcome to the course!/8. Installing Python and Anaconda (Mac, Linux & Windows).mp419.52MB

10. Evaluating Regression Models Performance/1. R-Squared Intuition.mp48.85MB

10. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp419.28MB

10. Evaluating Regression Models Performance/3. Evaluating Regression Models Performance - Homework's Final Part.mp421.89MB

10. Evaluating Regression Models Performance/4. Interpreting Linear Regression Coefficients.mp424.21MB

12. Logistic Regression/1. Logistic Regression Intuition.mp429.17MB

12. Logistic Regression/10. Logistic Regression in R - Step 2.mp47.85MB

12. Logistic Regression/11. Logistic Regression in R - Step 3.mp414.59MB

12. Logistic Regression/12. Logistic Regression in R - Step 4.mp46.91MB

12. Logistic Regression/13. Logistic Regression in R - Step 5.mp451.68MB

12. Logistic Regression/14. R Classification Template.mp412.47MB

12. Logistic Regression/2. How to get the dataset.mp411.71MB

12. Logistic Regression/3. Logistic Regression in Python - Step 1.mp412.93MB

12. Logistic Regression/4. Logistic Regression in Python - Step 2.mp48.24MB

12. Logistic Regression/5. Logistic Regression in Python - Step 3.mp45.98MB

12. Logistic Regression/6. Logistic Regression in Python - Step 4.mp410.38MB

12. Logistic Regression/7. Logistic Regression in Python - Step 5.mp442.55MB

12. Logistic Regression/8. Python Classification Template.mp412.06MB

12. Logistic Regression/9. Logistic Regression in R - Step 1.mp412.59MB

13. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp49.28MB

13. K-Nearest Neighbors (K-NN)/2. How to get the dataset.mp411.71MB

13. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp435.21MB

13. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp441.37MB

14. Support Vector Machine (SVM)/1. SVM Intuition.mp418.01MB

14. Support Vector Machine (SVM)/2. How to get the dataset.mp411.72MB

14. Support Vector Machine (SVM)/3. SVM in Python.mp431.16MB

14. Support Vector Machine (SVM)/4. SVM in R.mp432.26MB

15. Kernel SVM/1. Kernel SVM Intuition.mp45.79MB

15. Kernel SVM/2. Mapping to a higher dimension.mp413.74MB

15. Kernel SVM/3. The Kernel Trick.mp429.28MB

15. Kernel SVM/4. Types of Kernel Functions.mp412.31MB

15. Kernel SVM/5. How to get the dataset.mp411.71MB

15. Kernel SVM/6. Kernel SVM in Python.mp441.62MB

15. Kernel SVM/7. Kernel SVM in R.mp440.45MB

16. Naive Bayes/1. Bayes Theorem.mp443.9MB

16. Naive Bayes/2. Naive Bayes Intuition.mp427.79MB

16. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp413.28MB

16. Naive Bayes/4. Naive Bayes Intuition (Extras).mp418.94MB

16. Naive Bayes/5. How to get the dataset.mp411.71MB

16. Naive Bayes/6. Naive Bayes in Python.mp423.38MB

16. Naive Bayes/7. Naive Bayes in R.mp437.31MB

17. Decision Tree Classification/1. Decision Tree Classification Intuition.mp418.79MB

17. Decision Tree Classification/2. How to get the dataset.mp411.71MB

17. Decision Tree Classification/3. Decision Tree Classification in Python.mp429.8MB

17. Decision Tree Classification/4. Decision Tree Classification in R.mp451.18MB

18. Random Forest Classification/1. Random Forest Classification Intuition.mp419.43MB

18. Random Forest Classification/2. How to get the dataset.mp411.71MB

18. Random Forest Classification/3. Random Forest Classification in Python.mp447.15MB

18. Random Forest Classification/4. Random Forest Classification in R.mp449.39MB

19. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp413.65MB

19. Evaluating Classification Models Performance/2. Confusion Matrix.mp48.22MB

19. Evaluating Classification Models Performance/3. Accuracy Paradox.mp43.8MB

19. Evaluating Classification Models Performance/4. CAP Curve.mp418.68MB

19. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp411.52MB

2. -------------------- Part 1 Data Preprocessing --------------------/1. Welcome to Part 1 - Data Preprocessing.mp42.99MB

2. -------------------- Part 1 Data Preprocessing --------------------/10. Feature Scaling.mp434.62MB

2. -------------------- Part 1 Data Preprocessing --------------------/11. And here is our Data Preprocessing Template!.mp419.67MB

2. -------------------- Part 1 Data Preprocessing --------------------/2. Get the dataset.mp421.15MB

2. -------------------- Part 1 Data Preprocessing --------------------/3. Importing the Libraries.mp411.08MB

2. -------------------- Part 1 Data Preprocessing --------------------/4. Importing the Dataset.mp423.31MB

2. -------------------- Part 1 Data Preprocessing --------------------/6. Missing Data.mp432.16MB

2. -------------------- Part 1 Data Preprocessing --------------------/7. Categorical Data.mp440.79MB

2. -------------------- Part 1 Data Preprocessing --------------------/9. Splitting the Dataset into the Training set and Test set.mp439.03MB

21. K-Means Clustering/1. K-Means Clustering Intuition.mp426.86MB

21. K-Means Clustering/2. K-Means Random Initialization Trap.mp415.36MB

21. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp423.13MB

21. K-Means Clustering/4. How to get the dataset.mp411.71MB

21. K-Means Clustering/5. K-Means Clustering in Python.mp439.77MB

21. K-Means Clustering/6. K-Means Clustering in R.mp428.99MB

22. Hierarchical Clustering/1. Hierarchical Clustering Intuition.mp416.53MB

22. Hierarchical Clustering/10. HC in R - Step 1.mp47.38MB

22. Hierarchical Clustering/11. HC in R - Step 2.mp411.15MB

22. Hierarchical Clustering/12. HC in R - Step 3.mp47.81MB

22. Hierarchical Clustering/13. HC in R - Step 4.mp47.44MB

22. Hierarchical Clustering/14. HC in R - Step 5.mp46.89MB

22. Hierarchical Clustering/2. Hierarchical Clustering How Dendrograms Work.mp417.47MB

22. Hierarchical Clustering/3. Hierarchical Clustering Using Dendrograms.mp422.81MB

22. Hierarchical Clustering/4. How to get the dataset.mp411.72MB

22. Hierarchical Clustering/5. HC in Python - Step 1.mp410.72MB

22. Hierarchical Clustering/6. HC in Python - Step 2.mp412.64MB

22. Hierarchical Clustering/7. HC in Python - Step 3.mp412.3MB

22. Hierarchical Clustering/8. HC in Python - Step 4.mp412.02MB

22. Hierarchical Clustering/9. HC in Python - Step 5.mp48.39MB

24. Apriori/1. Apriori Intuition.mp435.02MB

24. Apriori/2. How to get the dataset.mp411.72MB

24. Apriori/3. Apriori in R - Step 1.mp442.87MB

24. Apriori/4. Apriori in R - Step 2.mp430.5MB

24. Apriori/5. Apriori in R - Step 3.mp443.84MB

24. Apriori/6. Apriori in Python - Step 1.mp437.97MB

24. Apriori/7. Apriori in Python - Step 2.mp429.52MB

24. Apriori/8. Apriori in Python - Step 3.mp426.96MB

25. Eclat/1. Eclat Intuition.mp410.65MB

25. Eclat/2. How to get the dataset.mp411.72MB

25. Eclat/3. Eclat in R.mp420.68MB

27. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp430.19MB

27. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in R - Step 3.mp447.2MB

27. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 4.mp47.41MB

27. Upper Confidence Bound (UCB)/2. Upper Confidence Bound (UCB) Intuition.mp429.33MB

27. Upper Confidence Bound (UCB)/3. How to get the dataset.mp411.71MB

27. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp431.53MB

27. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp435.44MB

27. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp441.11MB

27. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp49.13MB

27. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in R - Step 1.mp428.05MB

27. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in R - Step 2.mp429.02MB

28. Thompson Sampling/1. Thompson Sampling Intuition.mp437.27MB

28. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp414.09MB

28. Thompson Sampling/3. How to get the dataset.mp411.72MB

28. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp443.13MB

28. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp48.42MB

28. Thompson Sampling/6. Thompson Sampling in R - Step 1.mp440.93MB

28. Thompson Sampling/7. Thompson Sampling in R - Step 2.mp47.47MB

29. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 7.mp417.1MB

29. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 8.mp439.48MB

29. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 9.mp414.01MB

29. -------------------- Part 7 Natural Language Processing --------------------/13. Natural Language Processing in Python - Step 10.mp424.13MB

29. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp440.38MB

29. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp417.48MB

29. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp413.52MB

29. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp46.51MB

29. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp44.57MB

29. -------------------- Part 7 Natural Language Processing --------------------/2. Natural Language Processing Intuition.mp429.69MB

29. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp412.74MB

29. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp47.52MB

29. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp413.27MB

29. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp428.99MB

29. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp441.19MB

29. -------------------- Part 7 Natural Language Processing --------------------/3. How to get the dataset.mp411.71MB

29. -------------------- Part 7 Natural Language Processing --------------------/4. Natural Language Processing in Python - Step 1.mp435.2MB

29. -------------------- Part 7 Natural Language Processing --------------------/5. Natural Language Processing in Python - Step 2.mp421.96MB

29. -------------------- Part 7 Natural Language Processing --------------------/6. Natural Language Processing in Python - Step 3.mp43.39MB

29. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 4.mp424.01MB

29. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 5.mp414.91MB

29. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 6.mp46.49MB

30. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp431.31MB

31. Artificial Neural Networks/1. Plan of attack.mp44.74MB

31. Artificial Neural Networks/10. Business Problem Description.mp416.38MB

31. Artificial Neural Networks/12. ANN in Python - Step 1.mp429.31MB

31. Artificial Neural Networks/13. ANN in Python - Step 2.mp448.09MB

31. Artificial Neural Networks/14. ANN in Python - Step 3.mp48.38MB

31. Artificial Neural Networks/15. ANN in Python - Step 4.mp45.88MB

31. Artificial Neural Networks/16. ANN in Python - Step 5.mp429.58MB

31. Artificial Neural Networks/17. ANN in Python - Step 6.mp47.06MB

31. Artificial Neural Networks/18. ANN in Python - Step 7.mp48.99MB

31. Artificial Neural Networks/19. ANN in Python - Step 8.mp418.17MB

31. Artificial Neural Networks/2. The Neuron.mp429.87MB

31. Artificial Neural Networks/20. ANN in Python - Step 9.mp416.9MB

31. Artificial Neural Networks/21. ANN in Python - Step 10.mp417.09MB

31. Artificial Neural Networks/22. ANN in R - Step 1.mp438.55MB

31. Artificial Neural Networks/23. ANN in R - Step 2.mp414.17MB

31. Artificial Neural Networks/24. ANN in R - Step 3.mp428.94MB

31. Artificial Neural Networks/25. ANN in R - Step 4 (Last step).mp433.44MB

31. Artificial Neural Networks/3. The Activation Function.mp414.76MB

31. Artificial Neural Networks/4. How do Neural Networks work.mp423.53MB

31. Artificial Neural Networks/5. How do Neural Networks learn.mp426.55MB

31. Artificial Neural Networks/6. Gradient Descent.mp418.54MB

31. Artificial Neural Networks/7. Stochastic Gradient Descent.mp416.83MB

31. Artificial Neural Networks/8. Backpropagation.mp410.92MB

31. Artificial Neural Networks/9. How to get the dataset.mp411.71MB

32. Convolutional Neural Networks/1. Plan of attack.mp45.9MB

32. Convolutional Neural Networks/10. How to get the dataset.mp411.71MB

32. Convolutional Neural Networks/12. CNN in Python - Step 1.mp424.93MB

32. Convolutional Neural Networks/13. CNN in Python - Step 2.mp45.86MB

32. Convolutional Neural Networks/14. CNN in Python - Step 3.mp42.22MB

32. Convolutional Neural Networks/15. CNN in Python - Step 4.mp427.18MB

32. Convolutional Neural Networks/16. CNN in Python - Step 5.mp49.91MB

32. Convolutional Neural Networks/17. CNN in Python - Step 6.mp49.71MB

32. Convolutional Neural Networks/18. CNN in Python - Step 7.mp412.93MB

32. Convolutional Neural Networks/19. CNN in Python - Step 8.mp46.8MB

32. Convolutional Neural Networks/2. What are convolutional neural networks.mp429.5MB

32. Convolutional Neural Networks/20. CNN in Python - Step 9.mp446.85MB

32. Convolutional Neural Networks/21. CNN in Python - Step 10.mp420.6MB

32. Convolutional Neural Networks/3. Step 1 - Convolution Operation.mp431.02MB

32. Convolutional Neural Networks/4. Step 1(b) - ReLU Layer.mp414.09MB

32. Convolutional Neural Networks/5. Step 2 - Pooling.mp440.24MB

32. Convolutional Neural Networks/6. Step 3 - Flattening.mp43.28MB

32. Convolutional Neural Networks/7. Step 4 - Full Connection.mp442.75MB

32. Convolutional Neural Networks/8. Summary.mp47.92MB

32. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp433.23MB

34. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp432.11MB

34. Principal Component Analysis (PCA)/2. How to get the dataset.mp411.71MB

34. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp431.96MB

34. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp422.07MB

34. Principal Component Analysis (PCA)/5. PCA in Python - Step 3.mp425.51MB

34. Principal Component Analysis (PCA)/6. PCA in R - Step 1.mp430.65MB

34. Principal Component Analysis (PCA)/7. PCA in R - Step 2.mp429.02MB

34. Principal Component Analysis (PCA)/8. PCA in R - Step 3.mp436.73MB

35. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp426.98MB

35. Linear Discriminant Analysis (LDA)/2. How to get the dataset.mp411.71MB

35. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp445.42MB

35. Linear Discriminant Analysis (LDA)/4. LDA in R.mp451.29MB

36. Kernel PCA/1. How to get the dataset.mp411.71MB

36. Kernel PCA/2. Kernel PCA in Python.mp433.38MB

36. Kernel PCA/3. Kernel PCA in R.mp456.57MB

38. Model Selection/1. How to get the dataset.mp411.72MB

38. Model Selection/2. k-Fold Cross Validation in Python.mp432.83MB

38. Model Selection/3. k-Fold Cross Validation in R.mp443.63MB

38. Model Selection/4. Grid Search in Python - Step 1.mp438.21MB

38. Model Selection/5. Grid Search in Python - Step 2.mp429.52MB

38. Model Selection/6. Grid Search in R.mp435.54MB

39. XGBoost/1. How to get the dataset.mp411.71MB

39. XGBoost/2. XGBoost in Python - Step 1.mp421.39MB

39. XGBoost/3. XGBoost in Python - Step 2.mp431.98MB

39. XGBoost/4. XGBoost in R.mp447.26MB

39. XGBoost/5. THANK YOU bonus video.mp452.24MB

4. Simple Linear Regression/1. How to get the dataset.mp411.71MB

4. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp414.36MB

4. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp48.64MB

4. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp437.37MB

4. Simple Linear Regression/2. Dataset + Business Problem Description.mp46.63MB

4. Simple Linear Regression/3. Simple Linear Regression Intuition - Step 1.mp49.48MB

4. Simple Linear Regression/4. Simple Linear Regression Intuition - Step 2.mp45.37MB

4. Simple Linear Regression/5. Simple Linear Regression in Python - Step 1.mp421.72MB

4. Simple Linear Regression/6. Simple Linear Regression in Python - Step 2.mp418.75MB

4. Simple Linear Regression/7. Simple Linear Regression in Python - Step 3.mp415.61MB

4. Simple Linear Regression/8. Simple Linear Regression in Python - Step 4.mp430.82MB

4. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp49.54MB

5. Multiple Linear Regression/1. How to get the dataset.mp411.71MB

5. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp47.23MB

5. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp414.29MB

5. Multiple Linear Regression/12. Multiple Linear Regression in Python - Backward Elimination - Preparation.mp423.82MB

5. Multiple Linear Regression/13. Multiple Linear Regression in Python - Backward Elimination - HOMEWORK !.mp432.59MB

5. Multiple Linear Regression/14. Multiple Linear Regression in Python - Backward Elimination - Homework Solution.mp427.17MB

5. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 1.mp417.94MB

5. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 2.mp425.93MB

5. Multiple Linear Regression/18. Multiple Linear Regression in R - Step 3.mp410.41MB

5. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp439.73MB

5. Multiple Linear Regression/2. Dataset + Business Problem Description.mp49.98MB

5. Multiple Linear Regression/20. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp417.24MB

5. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 1.mp41.82MB

5. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 2.mp41.78MB

5. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 3.mp414.28MB

5. Multiple Linear Regression/6. Multiple Linear Regression Intuition - Step 4.mp44.51MB

5. Multiple Linear Regression/8. Multiple Linear Regression Intuition - Step 5.mp428.83MB

5. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp439.56MB

6. Polynomial Regression/1. Polynomial Regression Intuition.mp49.44MB

6. Polynomial Regression/10. Polynomial Regression in R - Step 3.mp443.31MB

6. Polynomial Regression/11. Polynomial Regression in R - Step 4.mp422.34MB

6. Polynomial Regression/12. R Regression Template.mp425.41MB

6. Polynomial Regression/2. How to get the dataset.mp411.71MB

6. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp424.89MB

6. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp427.1MB

6. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp442.98MB

6. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp413.5MB

6. Polynomial Regression/7. Python Regression Template.mp427.43MB

6. Polynomial Regression/8. Polynomial Regression in R - Step 1.mp417.78MB

6. Polynomial Regression/9. Polynomial Regression in R - Step 2.mp423.87MB

7. Support Vector Regression (SVR)/1. How to get the dataset.mp411.71MB

7. Support Vector Regression (SVR)/2. SVR Intuition.mp446.59MB

7. Support Vector Regression (SVR)/3. SVR in Python.mp446.18MB

7. Support Vector Regression (SVR)/4. SVR in R.mp425.87MB

8. Decision Tree Regression/1. Decision Tree Regression Intuition.mp422.69MB

8. Decision Tree Regression/2. How to get the dataset.mp411.71MB

8. Decision Tree Regression/3. Decision Tree Regression in Python.mp433.54MB

8. Decision Tree Regression/4. Decision Tree Regression in R.mp444.37MB

9. Random Forest Regression/1. Random Forest Regression Intuition.mp413.82MB

9. Random Forest Regression/2. How to get the dataset.mp411.72MB

9. Random Forest Regression/3. Random Forest Regression in Python.mp439.47MB

9. Random Forest Regression/4. Random Forest Regression in R.mp440.34MB