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种子名称： [FreeTutorials.Eu] Udemy - Machine Learning A-Z Become Kaggle Master
文件类型： 视频
文件数目： 257个文件
文件大小： 13.87 GB
收录时间： 2019-2-16 19:07
已经下载： 3次
资源热度： 276
最近下载： 2024-12-2 16:32

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[FreeTutorials.Eu] Udemy - Machine Learning A-Z Become Kaggle Master.torrent

10. Multiple Linear Regression/10. Case Study Part5.mp445.73MB

10. Multiple Linear Regression/11. Case Study Part6 (RFE).mp464.15MB

10. Multiple Linear Regression/1. Introduction.mp416.46MB

10. Multiple Linear Regression/2. Case Study part1.mp483.04MB

10. Multiple Linear Regression/3. Case Study part2.mp498.41MB

10. Multiple Linear Regression/4. Case Study part3.mp468.67MB

10. Multiple Linear Regression/5. Adjusted R Square.mp48.08MB

10. Multiple Linear Regression/6. Case Study Part1.mp468.55MB

10. Multiple Linear Regression/7. Case Study Part2.mp472.9MB

10. Multiple Linear Regression/8. Case Study Part3.mp466.56MB

10. Multiple Linear Regression/9. Case Study Part4.mp4132.2MB

11. HotstarNetflix Real world Case Study for Multiple Linear Regression/1. Introduction to the Problem Statement.mp440.85MB

11. HotstarNetflix Real world Case Study for Multiple Linear Regression/2. Playing With Data.mp481.36MB

11. HotstarNetflix Real world Case Study for Multiple Linear Regression/3. Building Model Part1.mp455.07MB

11. HotstarNetflix Real world Case Study for Multiple Linear Regression/4. Building Model Part2.mp487.8MB

11. HotstarNetflix Real world Case Study for Multiple Linear Regression/5. Building Model Part3.mp448.52MB

11. HotstarNetflix Real world Case Study for Multiple Linear Regression/6. Verification of Model.mp439.49MB

12. Gradient Descent/1. Pre-Req For Gradient Descent Part1.mp461.24MB

12. Gradient Descent/2. Pre-Req For Gradient Descent Part2.mp432.9MB

12. Gradient Descent/3. Cost Functions.mp413.16MB

12. Gradient Descent/4. Defining Cost Functions More Formally.mp436.51MB

12. Gradient Descent/5. Gradient Descent.mp437.66MB

12. Gradient Descent/6. Optimisation.mp421.68MB

12. Gradient Descent/7. Closed Form Vs Gradient Descent.mp426.61MB

12. Gradient Descent/8. Gradient Descent case study.mp471.66MB

13. KNN/10. Case Study.mp470.71MB

13. KNN/11. Classification Case1.mp484.22MB

13. KNN/12. Classification Case2.mp452.23MB

13. KNN/13. Classification Case3.mp452.97MB

13. KNN/14. Classification Case4.mp441.1MB

13. KNN/1. Introduction to Classification.mp454.11MB

13. KNN/2. Defining Classification Mathematically.mp439.99MB

13. KNN/3. Introduction to KNN.mp447.13MB

13. KNN/4. Accuracy of KNN.mp457.16MB

13. KNN/5. Effectiveness of KNN.mp448.23MB

13. KNN/6. Distance Metrics.mp447.9MB

13. KNN/7. Distance Metrics Part2.mp428.83MB

13. KNN/8. Finding k.mp433.32MB

13. KNN/9. KNN on Regression.mp49.28MB

14. Model Performance Metrics/1. Performance Metrics Part1.mp4113.83MB

14. Model Performance Metrics/2. Performance Metrics Part2.mp490.48MB

14. Model Performance Metrics/3. Performance Metrics Part3.mp424.02MB

15. Model Selection Part1/1. Model Creation Case1.mp452.09MB

15. Model Selection Part1/2. Model Creation Case2.mp434.67MB

15. Model Selection Part1/3. Gridsearch Case study Part1.mp4124.24MB

15. Model Selection Part1/4. Gridsearch Case study Part2.mp4178.88MB

16. Naive Bayes/10. Case Study 2 Part1.mp474.57MB

16. Naive Bayes/11. Case Study 2 Part2.mp425.35MB

16. Naive Bayes/1. Introduction to Naive Bayes.mp473.37MB

16. Naive Bayes/2. Bayes Theorem.mp463.05MB

16. Naive Bayes/3. Practical Example from NB with One Column.mp480.59MB

16. Naive Bayes/4. Practical Example from NB with Multiple Columns.mp459.83MB

16. Naive Bayes/5. Naive Bayes On Text Data Part1.mp454.74MB

16. Naive Bayes/6. Naive Bayes On Text Data Part2.mp446.06MB

16. Naive Bayes/7. Laplace Smoothing.mp455.26MB

16. Naive Bayes/8. Bernoulli Naive Bayes.mp427.11MB

16. Naive Bayes/9. Case Study 1.mp495.46MB

17. Logistic Regression/1. Introduction.mp426.6MB

17. Logistic Regression/2. Sigmoid Function.mp444.31MB

17. Logistic Regression/3. Log Odds.mp441.83MB

17. Logistic Regression/4. Case Study.mp4198.2MB

18. Support Vector Machine (SVM)/10. Kernel Part2.mp471.13MB

18. Support Vector Machine (SVM)/11. Case Study 2.mp490MB

18. Support Vector Machine (SVM)/12. Case Study 3 Part1.mp456.01MB

18. Support Vector Machine (SVM)/13. Case Study 3 Part2.mp461.28MB

18. Support Vector Machine (SVM)/14. Case Study 4.mp4164.41MB

18. Support Vector Machine (SVM)/1. Introduction.mp458.71MB

18. Support Vector Machine (SVM)/2. Hyperplane Part1.mp427.07MB

18. Support Vector Machine (SVM)/3. Hyperplane Part2.mp465.32MB

18. Support Vector Machine (SVM)/4. Maths Behind SVM.mp424.04MB

18. Support Vector Machine (SVM)/5. Support Vectors.mp411.04MB

18. Support Vector Machine (SVM)/6. Slack Variable.mp433.27MB

18. Support Vector Machine (SVM)/7. SVM Case Study Part1.mp474.15MB

18. Support Vector Machine (SVM)/8. SVM Case Study Part2.mp466.16MB

18. Support Vector Machine (SVM)/9. Kernel Part1.mp449.24MB

19. Decision Tree/10. DT Case Study Part2.mp495.71MB

19. Decision Tree/1. Introduction.mp429.78MB

19. Decision Tree/2. Example of DT.mp440.59MB

19. Decision Tree/3. Homogenity.mp420.61MB

19. Decision Tree/4. Gini Index.mp444.19MB

19. Decision Tree/5. Information Gain Part1.mp429.29MB

19. Decision Tree/6. Information Gain Part2.mp427.37MB

19. Decision Tree/7. Advantages and Disadvantages of DT.mp415.45MB

19. Decision Tree/8. Preventing Overfitting Issues in DT.mp440.29MB

19. Decision Tree/9. DT Case Study Part1.mp4125.45MB

1. Python Fundamentals/10. Functions.mp485.62MB

1. Python Fundamentals/11. String Part1.mp4106.01MB

1. Python Fundamentals/12. String Part2.mp427.38MB

1. Python Fundamentals/13. List Part1.mp410.04MB

1. Python Fundamentals/14. List Part2.mp487.32MB

1. Python Fundamentals/15. List Part3.mp473.56MB

1. Python Fundamentals/16. List Part4.mp463.85MB

1. Python Fundamentals/17. Tuples.mp467.33MB

1. Python Fundamentals/18. Sets.mp458.16MB

1. Python Fundamentals/19. Dictionaries.mp461.6MB

1. Python Fundamentals/1. Introduction to the course.mp493.85MB

1. Python Fundamentals/20. Comprehentions.mp470.54MB

1. Python Fundamentals/2. Introduction to Kaggle.mp490.07MB

1. Python Fundamentals/3. Installation of Python and Anaconda.mp482.29MB

1. Python Fundamentals/4. Python Introduction.mp410.25MB

1. Python Fundamentals/5. Variables in Python.mp4110.46MB

1. Python Fundamentals/6. Numeric Operations in Python.mp436.92MB

1. Python Fundamentals/7. Logical Operations.mp417.32MB

1. Python Fundamentals/8. If else Loop.mp464.01MB

1. Python Fundamentals/9. for while Loop.mp477.78MB

20. Ensembling/10. Adaboost Part2.mp438.46MB

20. Ensembling/11. Adaboost Case Study.mp453.65MB

20. Ensembling/12. XGBoost.mp423.11MB

20. Ensembling/13. Boosting Part1.mp413.69MB

20. Ensembling/14. Boosting Part2.mp435.51MB

20. Ensembling/15. XGboost Algorithm.mp438.76MB

20. Ensembling/16. Case Study Part1.mp4141.54MB

20. Ensembling/17. Case Study Part2.mp4136.7MB

20. Ensembling/18. Case Study Part3.mp475.43MB

20. Ensembling/1. Introduction to Ensembles.mp439.28MB

20. Ensembling/2. Bagging.mp471.21MB

20. Ensembling/3. Advantages.mp414.87MB

20. Ensembling/4. Runtime.mp416.38MB

20. Ensembling/5. Case study.mp473.09MB

20. Ensembling/6. Introduction to Boosting.mp433.05MB

20. Ensembling/7. Weak Learners.mp417.9MB

20. Ensembling/8. Shallow Decision Tree.mp414.96MB

20. Ensembling/9. Adaboost Part1.mp441.53MB

21. Model Selection Part2/1. Model Selection Part1.mp4104.3MB

21. Model Selection Part2/2. Model Selection Part2.mp441.33MB

21. Model Selection Part2/3. Model Selection Part3.mp435.66MB

22. Unsupervised Learning/10. Case Study Part2.mp461.33MB

22. Unsupervised Learning/11. More on Segmentation.mp418.06MB

22. Unsupervised Learning/12. Hierarchial Clustering.mp438.02MB

22. Unsupervised Learning/13. Case Study.mp434.4MB

22. Unsupervised Learning/1. Introduction to Clustering.mp459.13MB

22. Unsupervised Learning/2. Segmentation.mp428.65MB

22. Unsupervised Learning/3. Kmeans.mp457.71MB

22. Unsupervised Learning/4. Maths Behind Kmeans.mp453.75MB

22. Unsupervised Learning/5. More Maths.mp49.43MB

22. Unsupervised Learning/6. Kmeans plus.mp451.78MB

22. Unsupervised Learning/7. Value of K.mp435.82MB

22. Unsupervised Learning/8. Hopkins test.mp412.27MB

22. Unsupervised Learning/9. Case Study Part1.mp495.82MB

23. Dimension Reduction/1. Introduction.mp4156.68MB

23. Dimension Reduction/2. PCA.mp498.39MB

23. Dimension Reduction/3. Maths Behind PCA.mp496.82MB

23. Dimension Reduction/4. Case Study Part1.mp445.47MB

23. Dimension Reduction/5. Case Study Part2.mp4123.06MB

24. Advanced Machine Learning Algorithms/10. Adjusted R Square.mp420.13MB

24. Advanced Machine Learning Algorithms/1. Introduction.mp430.94MB

24. Advanced Machine Learning Algorithms/2. Example Part1.mp427.48MB

24. Advanced Machine Learning Algorithms/3. Example Part2.mp445.11MB

24. Advanced Machine Learning Algorithms/4. Optimal Solution.mp465.23MB

24. Advanced Machine Learning Algorithms/5. Case study.mp439.97MB

24. Advanced Machine Learning Algorithms/6. Regularization.mp448.6MB

24. Advanced Machine Learning Algorithms/7. Ridge and Lasso.mp439.95MB

24. Advanced Machine Learning Algorithms/8. Case Study.mp4106.22MB

24. Advanced Machine Learning Algorithms/9. Model Selection.mp431.3MB

25. Deep Learning/1. Expectations.mp49.36MB

25. Deep Learning/2. Introduction.mp448.76MB

25. Deep Learning/3. History.mp461.86MB

25. Deep Learning/4. Perceptron.mp429.78MB

25. Deep Learning/5. Multi Layered Perceptron.mp463.83MB

25. Deep Learning/6. Neural Network Playground.mp4103.7MB

26. Project Kaggle/10. Response encoding and one hot encoder.mp454.68MB

26. Project Kaggle/11. Laplace Smoothing and Calibrated classifier.mp448.25MB

26. Project Kaggle/12. Significance of first categorical column.mp471.74MB

26. Project Kaggle/13. Second Categorical column.mp445.7MB

26. Project Kaggle/14. Third Categorical column.mp466.72MB

26. Project Kaggle/15. Data pre-processing before building machine learning model.mp450.59MB

26. Project Kaggle/16. Building Machine Learning model part1.mp4124.01MB

26. Project Kaggle/17. Building Machine Learning model part2.mp4135.18MB

26. Project Kaggle/18. Building Machine Learning model part3.mp438.41MB

26. Project Kaggle/19. Building Machine Learning model part4.mp433.07MB

26. Project Kaggle/1. Introduction to the Problem Statement.mp493.36MB

26. Project Kaggle/20. Building Machine Learning model part5.mp441.94MB

26. Project Kaggle/21. Building Machine Learning model part6.mp450.82MB

26. Project Kaggle/2. Playing With The Data.mp4137.05MB

26. Project Kaggle/3. Translating the Problem In Machine Learning World.mp4113.02MB

26. Project Kaggle/4. Dealing with Text Data.mp498.05MB

26. Project Kaggle/5. Train, Test And Cross Validation Split.mp4116.21MB

26. Project Kaggle/6. Understanding Evaluation Matrix Log Loss.mp485.5MB

26. Project Kaggle/7. Building A Worst Model.mp468.49MB

26. Project Kaggle/8. Evaluating Worst ML Model.mp458.87MB

26. Project Kaggle/9. First Categorical column analysis.mp471.13MB

2. Numpy/1. Introduction.mp424.74MB

2. Numpy/2. Numpy Operations Part1.mp4128.75MB

2. Numpy/3. Numpy Operations Part2.mp4169.97MB

3. Pandas/10. groupby.mp446.92MB

3. Pandas/11. Merging Part2.mp433.9MB

3. Pandas/12. Pivot Table.mp427.7MB

3. Pandas/1. Introduction.mp439.1MB

3. Pandas/2. Series.mp461.49MB

3. Pandas/3. DataFrame.mp466.19MB

3. Pandas/4. Operations Part1.mp412.02MB

3. Pandas/5. Operations Part2.mp444.1MB

3. Pandas/6. Indexes.mp450.11MB

3. Pandas/7. loc and iloc.mp459.37MB

3. Pandas/8. Reading CSV.mp442.47MB

3. Pandas/9. Merging Part1.mp430.01MB

4. Some Fun With Maths/1. Linear Algebra Vectors.mp4162.41MB

4. Some Fun With Maths/2. Linear Algebra Matrix Part1.mp495.26MB

4. Some Fun With Maths/3. Linear Algebra Matrix Part2.mp477.99MB

4. Some Fun With Maths/4. Linear Algebra Going From 2D to nD Part1.mp427.71MB

4. Some Fun With Maths/5. Linear Algebra 2D to nD Part2.mp425.78MB

5. Inferential Statistics/10. Normal Distribution.mp419.02MB

5. Inferential Statistics/11. z Score.mp423.8MB

5. Inferential Statistics/12. Sampling.mp438.73MB

5. Inferential Statistics/13. Sampling Distribution.mp425.51MB

5. Inferential Statistics/14. Central Limit Theorem.mp413.1MB

5. Inferential Statistics/15. Confidence Interval Part1.mp434.55MB

5. Inferential Statistics/16. Confidence Interval Part2.mp413.39MB

5. Inferential Statistics/1. Inferential Statistics.mp410.31MB

5. Inferential Statistics/2. Probability Theory.mp454.79MB

5. Inferential Statistics/3. Probability Distribution.mp424.24MB

5. Inferential Statistics/4. Expected Values Part1.mp424.25MB

5. Inferential Statistics/5. Expected Values Part2.mp414.49MB

5. Inferential Statistics/6. Without Experiment.mp428.68MB

5. Inferential Statistics/7. Binomial Distribution.mp417.58MB

5. Inferential Statistics/8. Commulative Distribution.mp48.37MB

5. Inferential Statistics/9. PDF.mp421MB

6. Hypothesis Testing/10. Types of Error.mp415.3MB

6. Hypothesis Testing/11. t- distribution Part1.mp421.31MB

6. Hypothesis Testing/12. t- distribution Part2.mp429.32MB

6. Hypothesis Testing/1. Introduction.mp431.09MB

6. Hypothesis Testing/2. NULL And Alternate Hypothesis.mp428.79MB

6. Hypothesis Testing/3. Examples.mp427.75MB

6. Hypothesis Testing/4. OneTwo Tailed Tests.mp438MB

6. Hypothesis Testing/5. Critical Value Method.mp424.71MB

6. Hypothesis Testing/6. z Table.mp458.63MB

6. Hypothesis Testing/7. Examples.mp426.42MB

6. Hypothesis Testing/8. More Examples.mp416.47MB

6. Hypothesis Testing/9. p Value.mp433.48MB

7. Data Visualisation/1. Matplotlib.mp4172.76MB

7. Data Visualisation/2. Seaborn.mp4184.74MB

7. Data Visualisation/3. Case Study.mp4113.2MB

7. Data Visualisation/4. Seaborn On Time Series Data.mp454.06MB

8. Exploratory Data Analysis/10. Univariate Analysis Part1.mp482.78MB

8. Exploratory Data Analysis/11. Univariate Analysis Part2.mp460.85MB

8. Exploratory Data Analysis/12. Segmented Analysis.mp424.47MB

8. Exploratory Data Analysis/13. Bivariate Analysis.mp460.6MB

8. Exploratory Data Analysis/14. Derived Columns.mp441.89MB

8. Exploratory Data Analysis/1. Introduction.mp43.79MB

8. Exploratory Data Analysis/2. Data Sourcing and Cleaning part1.mp415.56MB

8. Exploratory Data Analysis/3. Data Sourcing and Cleaning part2.mp415.62MB

8. Exploratory Data Analysis/4. Data Sourcing and Cleaning part3.mp410.03MB

8. Exploratory Data Analysis/5. Data Sourcing and Cleaning part4.mp410.37MB

8. Exploratory Data Analysis/6. Data Sourcing and Cleaning part5.mp412.41MB

8. Exploratory Data Analysis/7. Data Sourcing and Cleaning part6.mp453.7MB

8. Exploratory Data Analysis/8. Data Cleaning part1.mp476.24MB

8. Exploratory Data Analysis/9. Data Cleaning part2.mp429.7MB

9. Simple Linear Regression/10. Residual Square Error (RSE).mp44.55MB

9. Simple Linear Regression/1. Introduction to Machine Learning.mp411.16MB

9. Simple Linear Regression/2. Types of Machine Learning.mp435.38MB

9. Simple Linear Regression/3. Introduction to Linear Regression (LR).mp417.88MB

9. Simple Linear Regression/4. How LR Works.mp458.68MB

9. Simple Linear Regression/5. Some Fun With Maths Behind LR.mp452.75MB

9. Simple Linear Regression/6. R Square.mp452.47MB

9. Simple Linear Regression/7. LR Case Study Part1.mp4137.5MB

9. Simple Linear Regression/8. LR Case Study Part2.mp453.38MB

9. Simple Linear Regression/9. LR Case Study Part3.mp446.44MB