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种子名称： [FreeCourseSite.com] Udemy - Machine Learning using Python
文件类型： 视频
文件数目： 154个文件
文件大小： 7.06 GB
收录时间： 2022-11-14 02:01
已经下载： 3次
资源热度： 89
最近下载： 2024-6-29 18:12

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[FreeCourseSite.com] Udemy - Machine Learning using Python.torrent

1. Setting up Python and Jupyter notebook/1. Installing Python and Anaconda.mp418.06MB

1. Setting up Python and Jupyter notebook/10. Working with Seaborn Library of Python.mp439.57MB

1. Setting up Python and Jupyter notebook/2. This is a Milestone!.mp420.66MB

1. Setting up Python and Jupyter notebook/3. Opening Jupyter Notebook.mp468.44MB

1. Setting up Python and Jupyter notebook/4. Introduction to Jupyter.mp444.06MB

1. Setting up Python and Jupyter notebook/5. Arithmetic operators in Python Python Basics.mp413.53MB

1. Setting up Python and Jupyter notebook/6. Strings in Python Python Basics.mp468.18MB

1. Setting up Python and Jupyter notebook/7. Lists, Tuples and Directories Python Basics.mp463.21MB

1. Setting up Python and Jupyter notebook/8. Working with Numpy Library of Python.mp446.45MB

1. Setting up Python and Jupyter notebook/9. Working with Pandas Library of Python.mp450.69MB

10. Comparing results from 3 models/1. Understanding the results of classification models.mp441.65MB

10. Comparing results from 3 models/2. Summary of the three models.mp422.23MB

11. Simple Decision Trees/1. Introduction to Decision trees.mp444.91MB

11. Simple Decision Trees/10. Creating Decision tree in Python.mp421.33MB

11. Simple Decision Trees/11. Evaluating model performance in Python.mp418.28MB

11. Simple Decision Trees/12. Plotting decision tree in Python.mp427.05MB

11. Simple Decision Trees/13. Pruning a tree.mp425.05MB

11. Simple Decision Trees/14. Pruning a tree in Python.mp425.06MB

11. Simple Decision Trees/2. Basics of Decision Trees.mp458.65MB

11. Simple Decision Trees/3. Understanding a Regression Tree.mp461.1MB

11. Simple Decision Trees/4. The stopping criteria for controlling tree growth.mp419.39MB

11. Simple Decision Trees/5. Importing the Data set into Python.mp415.86MB

11. Simple Decision Trees/6. Missing value treatment in Python.mp412.94MB

11. Simple Decision Trees/7. Dummy Variable Creation in Python.mp424.58MB

11. Simple Decision Trees/8. Dependent- Independent Data split in Python.mp416.87MB

11. Simple Decision Trees/9. Test-Train split in Python.mp425.63MB

12. Simple Classification Tree/1. Classification tree.mp440.23MB

12. Simple Classification Tree/2. The Data set for Classification problem.mp420.89MB

12. Simple Classification Tree/3. Classification tree in Python Preprocessing.mp453.82MB

12. Simple Classification Tree/4. Classification tree in Python Training.mp499.55MB

12. Simple Classification Tree/5. Advantages and Disadvantages of Decision Trees.mp410.05MB

13. Ensemble technique 1 - Bagging/1. Ensemble technique 1 - Bagging.mp439.32MB

13. Ensemble technique 1 - Bagging/2. Ensemble technique 1 - Bagging in Python.mp497.09MB

14. Ensemble technique 2 - Random Forests/1. Ensemble technique 2 - Random Forests.mp426.03MB

14. Ensemble technique 2 - Random Forests/2. Ensemble technique 2 - Random Forests in Python.mp454.86MB

14. Ensemble technique 2 - Random Forests/3. Using Grid Search in Python.mp491.74MB

15. Ensemble technique 3 - Boosting/1. Boosting.mp440.9MB

15. Ensemble technique 3 - Boosting/2. Ensemble technique 3a - Boosting in Python.mp439.86MB

15. Ensemble technique 3 - Boosting/3. Ensemble technique 3b - AdaBoost in Python.mp430.54MB

15. Ensemble technique 3 - Boosting/4. Ensemble technique 3c - XGBoost in Python.mp474.98MB

16. Support Vector Machines/1. Introduction to SVM's.mp421.64MB

16. Support Vector Machines/2. The Concept of a Hyperplane.mp440.55MB

16. Support Vector Machines/3. Maximum Margin Classifier.mp430.64MB

16. Support Vector Machines/4. Limitations of Maximum Margin Classifier.mp414.52MB

17. Support Vector classifiers/1. Support Vector classifiers.mp473.69MB

17. Support Vector classifiers/2. Limitations of Support Vector Classifiers.mp415.63MB

18. Support Vector Machines/1. Kernel Based Support Vector Machines.mp453.27MB

19. Creating Support Vector Machine Model in Python/1. Regression and Classification Models.mp44.69MB

19. Creating Support Vector Machine Model in Python/10. Radial Kernel with Hyperparameter Tuning.mp444.5MB

19. Creating Support Vector Machine Model in Python/2. Importing and preprocessing data in Python.mp426.45MB

19. Creating Support Vector Machine Model in Python/3. Standardizing the data.mp442.09MB

19. Creating Support Vector Machine Model in Python/4. SVM based Regression Model in Python.mp473.72MB

19. Creating Support Vector Machine Model in Python/5. Classification model - Preprocessing.mp453.92MB

19. Creating Support Vector Machine Model in Python/6. Classification model - Standardizing the data.mp410.61MB

19. Creating Support Vector Machine Model in Python/7. SVM Based classification model.mp472.9MB

19. Creating Support Vector Machine Model in Python/8. Hyper Parameter Tuning.mp467.53MB

19. Creating Support Vector Machine Model in Python/9. Polynomial Kernel with Hyperparameter Tuning.mp426.45MB

2. Basics of statistics/1. Types of Data.mp423.31MB

2. Basics of statistics/2. Types of Statistics.mp411.99MB

2. Basics of statistics/3. Describing data Graphically.mp476.04MB

2. Basics of statistics/4. Measures of Centers.mp443.32MB

2. Basics of statistics/5. Measures of Dispersion.mp426.31MB

20. Time Series Analysis and Forecasting/1. Introduction.mp418.68MB

20. Time Series Analysis and Forecasting/2. Time Series Forecasting - Use cases.mp431.35MB

20. Time Series Analysis and Forecasting/3. Forecasting model creation - Steps.mp412.13MB

20. Time Series Analysis and Forecasting/4. Forecasting model creation - Steps 1 (Goal).mp445.94MB

20. Time Series Analysis and Forecasting/5. Time Series - Basic Notations.mp478.9MB

21. Time Series - Preprocessing in Pyhton/1. Data Loading in Python.mp4134.61MB

21. Time Series - Preprocessing in Pyhton/10. Exponential Smoothing.mp410.86MB

21. Time Series - Preprocessing in Pyhton/2. Time Series - Visualization Basics.mp480.35MB

21. Time Series - Preprocessing in Pyhton/3. Time Series - Visualization in Python.mp4208.24MB

21. Time Series - Preprocessing in Pyhton/4. Time Series - Feature Engineering Basics.mp476.92MB

21. Time Series - Preprocessing in Pyhton/5. Time Series - Feature Engineering in Python.mp4142.5MB

21. Time Series - Preprocessing in Pyhton/6. Time Series - Upsampling and Downsampling.mp423.34MB

21. Time Series - Preprocessing in Pyhton/7. Time Series - Upsampling and Downsampling in Python.mp4124.28MB

21. Time Series - Preprocessing in Pyhton/8. Time Series - Power Transformation.mp418.7MB

21. Time Series - Preprocessing in Pyhton/9. Moving Average.mp450.04MB

22. Time Series - Important Concepts/1. White Noise.mp414.71MB

22. Time Series - Important Concepts/2. Random Walk.mp428.05MB

22. Time Series - Important Concepts/3. Decomposing Time Series in Python.mp478.61MB

22. Time Series - Important Concepts/4. Differencing.mp444.01MB

22. Time Series - Important Concepts/5. Differencing in Python.mp4141.15MB

23. Time Series - Implementation in Python/1. Test Train Split in Python.mp477.12MB

23. Time Series - Implementation in Python/2. Naive (Persistence) model in Python.mp456.85MB

23. Time Series - Implementation in Python/3. Auto Regression Model - Basics.mp420.93MB

23. Time Series - Implementation in Python/4. Auto Regression Model creation in Python.mp467.57MB

23. Time Series - Implementation in Python/5. Auto Regression with Walk Forward validation in Python.mp461.79MB

23. Time Series - Implementation in Python/6. Moving Average model -Basics.mp431.74MB

23. Time Series - Implementation in Python/7. Moving Average model in Python.mp464.3MB

24. Time Series - ARIMA model/1. ACF and PACF.mp452.76MB

24. Time Series - ARIMA model/2. ARIMA model - Basics.mp426.47MB

24. Time Series - ARIMA model/3. ARIMA model in Python.mp484.87MB

24. Time Series - ARIMA model/4. ARIMA model with Walk Forward Validation in Python.mp436.14MB

25. Time Series - SARIMA model/1. SARIMA model.mp440.23MB

25. Time Series - SARIMA model/2. SARIMA model in Python.mp475.11MB

25. Time Series - SARIMA model/3. Stationary time Series.mp45.66MB

25. Time Series - SARIMA model/4. The final milestone!.mp411.85MB

3. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4113.34MB

3. Introduction to Machine Learning/2. Building a Machine Learning Model.mp440.97MB

4. Data Preprocessing/1. Gathering Business Knowledge.mp417.27MB

4. Data Preprocessing/10. Missing Value Imputation in Python.mp433.32MB

4. Data Preprocessing/11. Seasonality in Data.mp417.02MB

4. Data Preprocessing/12. Bi-variate analysis and Variable transformation.mp4100.45MB

4. Data Preprocessing/13. Variable transformation and deletion in Python.mp467.33MB

4. Data Preprocessing/14. Non-usable variables.mp420.24MB

4. Data Preprocessing/15. Dummy variable creation Handling qualitative data.mp440.47MB

4. Data Preprocessing/16. Dummy variable creation in Python.mp440.8MB

4. Data Preprocessing/17. Correlation Analysis.mp474.68MB

4. Data Preprocessing/18. Correlation Analysis in Python.mp465.61MB

4. Data Preprocessing/2. Data Exploration.mp428.38MB

4. Data Preprocessing/3. The Dataset and the Data Dictionary.mp476.34MB

4. Data Preprocessing/4. Importing Data in Python.mp433.93MB

4. Data Preprocessing/5. Univariate analysis and EDD.mp429.25MB

4. Data Preprocessing/6. EDD in Python.mp478.5MB

4. Data Preprocessing/7. Outlier Treatment.mp426.61MB

4. Data Preprocessing/8. Outlier Treatment in Python.mp498.28MB

4. Data Preprocessing/9. Missing Value Imputation.mp424.46MB

5. Linear Regression/1. The Problem Statement.mp410.14MB

5. Linear Regression/10. Test-train split.mp441.83MB

5. Linear Regression/11. Bias Variance trade-off.mp425.1MB

5. Linear Regression/12. Test train split in Python.mp464.03MB

5. Linear Regression/13. Regression models other than OLS.mp416.53MB

5. Linear Regression/14. Subset selection techniques.mp479.05MB

5. Linear Regression/15. Shrinkage methods Ridge and Lasso.mp433.29MB

5. Linear Regression/16. Ridge regression and Lasso in Python.mp4174.92MB

5. Linear Regression/17. Heteroscedasticity.mp414.49MB

5. Linear Regression/2. Basic Equations and Ordinary Least Squares (OLS) method.mp442.52MB

5. Linear Regression/3. Assessing accuracy of predicted coefficients.mp4103.21MB

5. Linear Regression/4. Assessing Model Accuracy RSE and R squared.mp444.97MB

5. Linear Regression/5. Simple Linear Regression in Python.mp484.86MB

5. Linear Regression/6. Multiple Linear Regression.mp438.17MB

5. Linear Regression/7. The F - statistic.mp453.78MB

5. Linear Regression/8. Interpreting results of Categorical variables.mp421.42MB

5. Linear Regression/9. Multiple Linear Regression in Python.mp485.12MB

6. Introduction to the classification Models/1. Three classification models and Data set.mp452.25MB

6. Introduction to the classification Models/2. Importing the data into Python.mp46.88MB

6. Introduction to the classification Models/3. The problem statements.mp417.05MB

6. Introduction to the classification Models/4. Why can't we use Linear Regression.mp416.91MB

7. Logistic Regression/1. Logistic Regression.mp432.91MB

7. Logistic Regression/2. Training a Simple Logistic Model in Python.mp469.52MB

7. Logistic Regression/3. Result of Simple Logistic Regression.mp426.9MB

7. Logistic Regression/4. Logistic with multiple predictors.mp48.51MB

7. Logistic Regression/5. Training multiple predictor Logistic model in Python.mp434.25MB

7. Logistic Regression/6. Confusion Matrix.mp421.1MB

7. Logistic Regression/7. Creating Confusion Matrix in Python.mp460.79MB

7. Logistic Regression/8. Evaluating performance of model.mp435.17MB

7. Logistic Regression/9. Evaluating model performance in Python.mp413.39MB

8. Linear Discriminant Analysis (LDA)/1. Linear Discriminant Analysis.mp440.92MB

8. Linear Discriminant Analysis (LDA)/2. LDA in Python.mp417.65MB

9. K Nearest neighbors classifier/1. Test-Train Split.mp439.26MB

9. K Nearest neighbors classifier/2. Test-Train Split in Python.mp459MB

9. K Nearest neighbors classifier/3. K-Nearest Neighbors classifier.mp475.36MB

9. K Nearest neighbors classifier/4. K-Nearest Neighbors in Python Part 1.mp446.15MB

9. K Nearest neighbors classifier/5. K-Nearest Neighbors in Python Part 2.mp453.16MB