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

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种子名称： [FreeCourseSite.com] Udemy - Machine Learning A-Z™ Hands-On Python & R In Data Science
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1. Welcome to the course!/1. Applications of Machine Learning.mp49.81MB

1. Welcome to the course!/10. Installing R and R Studio (Mac, Linux & Windows).mp423.22MB

1. Welcome to the course!/5. Why Machine Learning is the Future.mp414.49MB

1. Welcome to the course!/9. Presentation of the ML A-Z folder, Colaboratory, Jupyter Notebook and Spyder.mp494.8MB

10. Decision Tree Regression/1. Decision Tree Regression Intuition.mp425.33MB

10. Decision Tree Regression/3. Decision Tree Regression in Python - Step 1.mp442.39MB

10. Decision Tree Regression/4. Decision Tree Regression in Python - Step 2.mp426.26MB

10. Decision Tree Regression/5. Decision Tree Regression in Python - Step 3.mp419.47MB

10. Decision Tree Regression/6. Decision Tree Regression in Python - Step 4.mp454.79MB

10. Decision Tree Regression/7. Decision Tree Regression in R.mp456.24MB

11. Random Forest Regression/1. Random Forest Regression Intuition.mp415.65MB

11. Random Forest Regression/3. Random Forest Regression in Python.mp474.39MB

11. Random Forest Regression/4. Random Forest Regression in R.mp451.87MB

12. Evaluating Regression Models Performance/1. R-Squared Intuition.mp49.81MB

12. Evaluating Regression Models Performance/2. Adjusted R-Squared Intuition.mp421.42MB

13. Regression Model Selection in Python/2. Preparation of the Regression Code Templates.mp4123.59MB

13. Regression Model Selection in Python/3. THE ULTIMATE DEMO OF THE POWERFUL REGRESSION CODE TEMPLATES IN ACTION!.mp456.78MB

14. Regression Model Selection in R/1. Evaluating Regression Models Performance - Homework's Final Part.mp428.36MB

14. Regression Model Selection in R/2. Interpreting Linear Regression Coefficients.mp427.38MB

16. Logistic Regression/1. Logistic Regression Intuition.mp429.18MB

16. Logistic Regression/10. Logistic Regression in R - Step 1.mp415.73MB

16. Logistic Regression/11. Logistic Regression in R - Step 2.mp414.85MB

16. Logistic Regression/12. Logistic Regression in R - Step 3.mp427.45MB

16. Logistic Regression/13. Logistic Regression in R - Step 4.mp411.73MB

16. Logistic Regression/15. Logistic Regression in R - Step 5.mp493.77MB

16. Logistic Regression/16. R Classification Template.mp417.51MB

16. Logistic Regression/3. Logistic Regression in Python - Step 1.mp444.6MB

16. Logistic Regression/4. Logistic Regression in Python - Step 2.mp484.66MB

16. Logistic Regression/5. Logistic Regression in Python - Step 3.mp443.05MB

16. Logistic Regression/6. Logistic Regression in Python - Step 4.mp445.2MB

16. Logistic Regression/7. Logistic Regression in Python - Step 5.mp430.59MB

16. Logistic Regression/8. Logistic Regression in Python - Step 6.mp452.96MB

16. Logistic Regression/9. Logistic Regression in Python - Step 7.mp4118.63MB

17. K-Nearest Neighbors (K-NN)/1. K-Nearest Neighbor Intuition.mp410.48MB

17. K-Nearest Neighbors (K-NN)/3. K-NN in Python.mp4146.61MB

17. K-Nearest Neighbors (K-NN)/4. K-NN in R.mp455.78MB

18. Support Vector Machine (SVM)/2. SVM Intuition.mp419.92MB

18. Support Vector Machine (SVM)/4. SVM in Python.mp4104.75MB

18. Support Vector Machine (SVM)/5. SVM in R.mp465.32MB

19. Kernel SVM/1. Kernel SVM Intuition.mp46.42MB

19. Kernel SVM/2. Mapping to a higher dimension.mp415.4MB

19. Kernel SVM/3. The Kernel Trick.mp434.73MB

19. Kernel SVM/4. Types of Kernel Functions.mp415.71MB

19. Kernel SVM/5. Non-Linear Kernel SVR (Advanced).mp465.64MB

19. Kernel SVM/7. Kernel SVM in Python.mp488.37MB

19. Kernel SVM/8. Kernel SVM in R.mp452.82MB

20. Naive Bayes/1. Bayes Theorem.mp450.44MB

20. Naive Bayes/2. Naive Bayes Intuition.mp431.11MB

20. Naive Bayes/3. Naive Bayes Intuition (Challenge Reveal).mp413.27MB

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

20. Naive Bayes/6. Naive Bayes in Python.mp4100.47MB

20. Naive Bayes/7. Naive Bayes in R.mp449.8MB

21. Decision Tree Classification/1. Decision Tree Classification Intuition.mp421.63MB

21. Decision Tree Classification/3. Decision Tree Classification in Python.mp4108.06MB

21. Decision Tree Classification/4. Decision Tree Classification in R.mp468.19MB

22. Random Forest Classification/1. Random Forest Classification Intuition.mp425.66MB

22. Random Forest Classification/3. Random Forest Classification in Python.mp496.69MB

22. Random Forest Classification/4. Random Forest Classification in R.mp464.11MB

23. Classification Model Selection in Python/2. THE ULTIMATE DEMO OF THE POWERFUL CLASSIFICATION CODE TEMPLATES IN ACTION!.mp4135.99MB

24. Evaluating Classification Models Performance/1. False Positives & False Negatives.mp415.13MB

24. Evaluating Classification Models Performance/2. Confusion Matrix.mp48.91MB

24. Evaluating Classification Models Performance/3. Accuracy Paradox.mp44.22MB

24. Evaluating Classification Models Performance/4. CAP Curve.mp420.32MB

24. Evaluating Classification Models Performance/5. CAP Curve Analysis.mp412.95MB

26. K-Means Clustering/1. K-Means Clustering Intuition.mp429.97MB

26. K-Means Clustering/10. K-Means Clustering in R.mp436.91MB

26. K-Means Clustering/2. K-Means Random Initialization Trap.mp415.37MB

26. K-Means Clustering/3. K-Means Selecting The Number Of Clusters.mp425.69MB

26. K-Means Clustering/5. K-Means Clustering in Python - Step 1.mp438.09MB

26. K-Means Clustering/6. K-Means Clustering in Python - Step 2.mp454.08MB

26. K-Means Clustering/7. K-Means Clustering in Python - Step 3.mp481.33MB

26. K-Means Clustering/8. K-Means Clustering in Python - Step 4.mp435.1MB

26. K-Means Clustering/9. K-Means Clustering in Python - Step 5.mp4120.5MB

27. Hierarchical Clustering/10. Hierarchical Clustering in R - Step 2.mp413.87MB

27. Hierarchical Clustering/11. Hierarchical Clustering in R - Step 3.mp49.96MB

27. Hierarchical Clustering/12. Hierarchical Clustering in R - Step 4.mp410.18MB

27. Hierarchical Clustering/13. Hierarchical Clustering in R - Step 5.mp413.68MB

27. Hierarchical Clustering/2. Hierarchical Clustering Intuition.mp416.52MB

27. Hierarchical Clustering/3. Hierarchical Clustering How Dendrograms Work.mp417.47MB

27. Hierarchical Clustering/4. Hierarchical Clustering Using Dendrograms.mp422.82MB

27. Hierarchical Clustering/6. Hierarchical Clustering in Python - Step 1.mp440.23MB

27. Hierarchical Clustering/7. Hierarchical Clustering in Python - Step 2.mp4135.92MB

27. Hierarchical Clustering/8. Hierarchical Clustering in Python - Step 3.mp475.29MB

27. Hierarchical Clustering/9. Hierarchical Clustering in R - Step 1.mp48.59MB

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

29. Apriori/3. Apriori in Python - Step 1.mp469.84MB

29. Apriori/4. Apriori in Python - Step 2.mp4107.7MB

29. Apriori/5. Apriori in Python - Step 3.mp469.2MB

29. Apriori/6. Apriori in Python - Step 4.mp4164.33MB

29. Apriori/7. Apriori in R - Step 1.mp452.84MB

29. Apriori/8. Apriori in R - Step 2.mp438.82MB

29. Apriori/9. Apriori in R - Step 3.mp456.51MB

3. Data Preprocessing in Python/2. Getting Started.mp454.34MB

3. Data Preprocessing in Python/3. Importing the Libraries.mp415.98MB

3. Data Preprocessing in Python/4. Importing the Dataset.mp471.79MB

3. Data Preprocessing in Python/6. Taking care of Missing Data.mp469.02MB

3. Data Preprocessing in Python/7. Encoding Categorical Data.mp488.63MB

3. Data Preprocessing in Python/8. Splitting the dataset into the Training set and Test set.mp467.63MB

3. Data Preprocessing in Python/9. Feature Scaling.mp4101.72MB

30. Eclat/1. Eclat Intuition.mp410.66MB

30. Eclat/3. Eclat in Python.mp475.55MB

30. Eclat/4. Eclat in R.mp425.26MB

32. Upper Confidence Bound (UCB)/1. The Multi-Armed Bandit Problem.mp430.2MB

32. Upper Confidence Bound (UCB)/10. Upper Confidence Bound in Python - Step 7.mp443.34MB

32. Upper Confidence Bound (UCB)/11. Upper Confidence Bound in R - Step 1.mp434.01MB

32. Upper Confidence Bound (UCB)/12. Upper Confidence Bound in R - Step 2.mp434.1MB

32. Upper Confidence Bound (UCB)/13. Upper Confidence Bound in R - Step 3.mp457.84MB

32. Upper Confidence Bound (UCB)/14. Upper Confidence Bound in R - Step 4.mp49.55MB

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

32. Upper Confidence Bound (UCB)/4. Upper Confidence Bound in Python - Step 1.mp458.74MB

32. Upper Confidence Bound (UCB)/5. Upper Confidence Bound in Python - Step 2.mp417.75MB

32. Upper Confidence Bound (UCB)/6. Upper Confidence Bound in Python - Step 3.mp438.47MB

32. Upper Confidence Bound (UCB)/7. Upper Confidence Bound in Python - Step 4.mp485.38MB

32. Upper Confidence Bound (UCB)/8. Upper Confidence Bound in Python - Step 5.mp432.43MB

32. Upper Confidence Bound (UCB)/9. Upper Confidence Bound in Python - Step 6.mp444.9MB

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

33. Thompson Sampling/10. Thompson Sampling in R - Step 2.mp49.56MB

33. Thompson Sampling/2. Algorithm Comparison UCB vs Thompson Sampling.mp414.08MB

33. Thompson Sampling/4. Thompson Sampling in Python - Step 1.mp430.59MB

33. Thompson Sampling/5. Thompson Sampling in Python - Step 2.mp470.01MB

33. Thompson Sampling/6. Thompson Sampling in Python - Step 3.mp478.66MB

33. Thompson Sampling/7. Thompson Sampling in Python - Step 4.mp444.64MB

33. Thompson Sampling/9. Thompson Sampling in R - Step 1.mp451.04MB

34. -------------------- Part 7 Natural Language Processing --------------------/10. Natural Language Processing in Python - Step 4.mp460.11MB

34. -------------------- Part 7 Natural Language Processing --------------------/11. Natural Language Processing in Python - Step 5.mp489.63MB

34. -------------------- Part 7 Natural Language Processing --------------------/12. Natural Language Processing in Python - Step 6.mp452.9MB

34. -------------------- Part 7 Natural Language Processing --------------------/15. Natural Language Processing in R - Step 1.mp451.2MB

34. -------------------- Part 7 Natural Language Processing --------------------/16. Natural Language Processing in R - Step 2.mp421.66MB

34. -------------------- Part 7 Natural Language Processing --------------------/17. Natural Language Processing in R - Step 3.mp416.89MB

34. -------------------- Part 7 Natural Language Processing --------------------/18. Natural Language Processing in R - Step 4.mp48.25MB

34. -------------------- Part 7 Natural Language Processing --------------------/19. Natural Language Processing in R - Step 5.mp45.78MB

34. -------------------- Part 7 Natural Language Processing --------------------/2. NLP Intuition.mp412.71MB

34. -------------------- Part 7 Natural Language Processing --------------------/20. Natural Language Processing in R - Step 6.mp416.1MB

34. -------------------- Part 7 Natural Language Processing --------------------/21. Natural Language Processing in R - Step 7.mp49.59MB

34. -------------------- Part 7 Natural Language Processing --------------------/22. Natural Language Processing in R - Step 8.mp417.23MB

34. -------------------- Part 7 Natural Language Processing --------------------/23. Natural Language Processing in R - Step 9.mp437.7MB

34. -------------------- Part 7 Natural Language Processing --------------------/24. Natural Language Processing in R - Step 10.mp454.15MB

34. -------------------- Part 7 Natural Language Processing --------------------/3. Types of Natural Language Processing.mp422.5MB

34. -------------------- Part 7 Natural Language Processing --------------------/4. Classical vs Deep Learning Models.mp483.95MB

34. -------------------- Part 7 Natural Language Processing --------------------/5. Bag-Of-Words Model.mp4103.5MB

34. -------------------- Part 7 Natural Language Processing --------------------/7. Natural Language Processing in Python - Step 1.mp434.07MB

34. -------------------- Part 7 Natural Language Processing --------------------/8. Natural Language Processing in Python - Step 2.mp440.48MB

34. -------------------- Part 7 Natural Language Processing --------------------/9. Natural Language Processing in Python - Step 3.mp460.61MB

35. -------------------- Part 8 Deep Learning --------------------/2. What is Deep Learning.mp431.32MB

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

36. Artificial Neural Networks/11. ANN in Python - Step 1.mp466.47MB

36. Artificial Neural Networks/13. ANN in Python - Step 2.mp4111.03MB

36. Artificial Neural Networks/14. ANN in Python - Step 3.mp475.07MB

36. Artificial Neural Networks/15. ANN in Python - Step 4.mp465.38MB

36. Artificial Neural Networks/16. ANN in Python - Step 5.mp4101.34MB

36. Artificial Neural Networks/17. ANN in R - Step 1.mp449.9MB

36. Artificial Neural Networks/18. ANN in R - Step 2.mp418.24MB

36. Artificial Neural Networks/19. ANN in R - Step 3.mp437.86MB

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

36. Artificial Neural Networks/20. ANN in R - Step 4 (Last step).mp443.76MB

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

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

36. Artificial Neural Networks/5. How do Neural Networks learn.mp426.56MB

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

36. Artificial Neural Networks/7. Stochastic Gradient Descent.mp416.82MB

36. Artificial Neural Networks/8. Backpropagation.mp410.93MB

36. Artificial Neural Networks/9. Business Problem Description.mp429.24MB

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

37. Convolutional Neural Networks/11. CNN in Python - Step 1.mp470.8MB

37. Convolutional Neural Networks/12. CNN in Python - Step 2.mp4106.88MB

37. Convolutional Neural Networks/13. CNN in Python - Step 3.mp4118.59MB

37. Convolutional Neural Networks/14. CNN in Python - Step 4.mp440.02MB

37. Convolutional Neural Networks/15. CNN in Python - Step 5.mp497.68MB

37. Convolutional Neural Networks/16. CNN in Python - FINAL DEMO!.mp4152.77MB

37. Convolutional Neural Networks/2. What are convolutional neural networks.mp429.51MB

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

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

37. Convolutional Neural Networks/5. Step 2 - Pooling.mp440.25MB

37. Convolutional Neural Networks/6. Step 3 - Flattening.mp43.27MB

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

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

37. Convolutional Neural Networks/9. Softmax & Cross-Entropy.mp433.24MB

39. Principal Component Analysis (PCA)/1. Principal Component Analysis (PCA) Intuition.mp432.12MB

39. Principal Component Analysis (PCA)/3. PCA in Python - Step 1.mp4112.91MB

39. Principal Component Analysis (PCA)/4. PCA in Python - Step 2.mp440.79MB

39. Principal Component Analysis (PCA)/5. PCA in R - Step 1.mp430.66MB

39. Principal Component Analysis (PCA)/6. PCA in R - Step 2.mp429.03MB

39. Principal Component Analysis (PCA)/7. PCA in R - Step 3.mp436.74MB

4. Data Preprocessing in R/10. Data Preprocessing Template.mp450.74MB

4. Data Preprocessing in R/2. Getting Started.mp49.81MB

4. Data Preprocessing in R/4. Dataset Description.mp411.84MB

4. Data Preprocessing in R/5. Importing the Dataset.mp416.42MB

4. Data Preprocessing in R/6. Taking care of Missing Data.mp439.79MB

4. Data Preprocessing in R/7. Encoding Categorical Data.mp457.32MB

4. Data Preprocessing in R/8. Splitting the dataset into the Training set and Test set.mp486.5MB

4. Data Preprocessing in R/9. Feature Scaling.mp478.89MB

40. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis (LDA) Intuition.mp426.99MB

40. Linear Discriminant Analysis (LDA)/3. LDA in Python.mp4102MB

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

41. Kernel PCA/2. Kernel PCA in Python.mp477.5MB

41. Kernel PCA/3. Kernel PCA in R.mp456.58MB

43. Model Selection/2. k-Fold Cross Validation in Python.mp4112.37MB

43. Model Selection/3. Grid Search in Python.mp4151.79MB

43. Model Selection/4. k-Fold Cross Validation in R.mp443.64MB

43. Model Selection/5. Grid Search in R.mp435.55MB

44. XGBoost/2. XGBoost in Python.mp489.99MB

44. XGBoost/4. XGBoost in R.mp447.27MB

44. XGBoost/5. THANK YOU Bonus Video.mp452.25MB

6. Simple Linear Regression/1. Simple Linear Regression Intuition - Step 1.mp410.53MB

6. Simple Linear Regression/10. Simple Linear Regression in R - Step 2.mp424.87MB

6. Simple Linear Regression/11. Simple Linear Regression in R - Step 3.mp411.43MB

6. Simple Linear Regression/12. Simple Linear Regression in R - Step 4.mp449.16MB

6. Simple Linear Regression/2. Simple Linear Regression Intuition - Step 2.mp45.99MB

6. Simple Linear Regression/4. Simple Linear Regression in Python - Step 1.mp448.61MB

6. Simple Linear Regression/5. Simple Linear Regression in Python - Step 2.mp439.85MB

6. Simple Linear Regression/6. Simple Linear Regression in Python - Step 3.mp428.22MB

6. Simple Linear Regression/7. Simple Linear Regression in Python - Step 4.mp474.57MB

6. Simple Linear Regression/9. Simple Linear Regression in R - Step 1.mp411.53MB

7. Multiple Linear Regression/1. Dataset + Business Problem Description.mp412.56MB

7. Multiple Linear Regression/10. Multiple Linear Regression in Python - Step 2.mp462.33MB

7. Multiple Linear Regression/11. Multiple Linear Regression in Python - Step 3.mp458.21MB

7. Multiple Linear Regression/12. Multiple Linear Regression in Python - Step 4.mp472.52MB

7. Multiple Linear Regression/15. Multiple Linear Regression in R - Step 1.mp423.44MB

7. Multiple Linear Regression/16. Multiple Linear Regression in R - Step 2.mp445.22MB

7. Multiple Linear Regression/17. Multiple Linear Regression in R - Step 3.mp413.85MB

7. Multiple Linear Regression/18. Multiple Linear Regression in R - Backward Elimination - HOMEWORK !.mp450.79MB

7. Multiple Linear Regression/19. Multiple Linear Regression in R - Backward Elimination - Homework Solution.mp421.95MB

7. Multiple Linear Regression/2. Multiple Linear Regression Intuition - Step 1.mp42MB

7. Multiple Linear Regression/3. Multiple Linear Regression Intuition - Step 2.mp42.04MB

7. Multiple Linear Regression/4. Multiple Linear Regression Intuition - Step 3.mp416.59MB

7. Multiple Linear Regression/5. Multiple Linear Regression Intuition - Step 4.mp45.34MB

7. Multiple Linear Regression/6. Understanding the P-Value.mp456.48MB

7. Multiple Linear Regression/7. Multiple Linear Regression Intuition - Step 5.mp432.81MB

7. Multiple Linear Regression/9. Multiple Linear Regression in Python - Step 1.mp450.92MB

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

8. Polynomial Regression/10. Polynomial Regression in R - Step 4.mp428.52MB

8. Polynomial Regression/11. R Regression Template.mp431.34MB

8. Polynomial Regression/3. Polynomial Regression in Python - Step 1.mp458.25MB

8. Polynomial Regression/4. Polynomial Regression in Python - Step 2.mp469.31MB

8. Polynomial Regression/5. Polynomial Regression in Python - Step 3.mp477.86MB

8. Polynomial Regression/6. Polynomial Regression in Python - Step 4.mp438.79MB

8. Polynomial Regression/7. Polynomial Regression in R - Step 1.mp421.21MB

8. Polynomial Regression/8. Polynomial Regression in R - Step 2.mp432.28MB

8. Polynomial Regression/9. Polynomial Regression in R - Step 3.mp454.81MB

9. Support Vector Regression (SVR)/1. SVR Intuition (Updated!).mp436.85MB

9. Support Vector Regression (SVR)/2. Heads-up on non-linear SVR.mp419.78MB

9. Support Vector Regression (SVR)/4. SVR in Python - Step 1.mp442.56MB

9. Support Vector Regression (SVR)/5. SVR in Python - Step 2.mp486.92MB

9. Support Vector Regression (SVR)/6. SVR in Python - Step 3.mp434.8MB

9. Support Vector Regression (SVR)/7. SVR in Python - Step 4.mp446.3MB

9. Support Vector Regression (SVR)/8. SVR in Python - Step 5.mp493.64MB

9. Support Vector Regression (SVR)/9. SVR in R.mp433.73MB