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种子名称： [DesireCourse.Net] Udemy - Machine Learning Basics Classification models in Python
文件类型： 视频
文件数目： 52个文件
文件大小： 2.15 GB
收录时间： 2021-10-8 07:08
已经下载： 3次
资源热度： 201
最近下载： 2024-5-3 08:17

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[DesireCourse.Net] Udemy - Machine Learning Basics Classification models in Python.torrent

1. Introduction/1. Welcome to the course!.mp417.6MB

2. Introduction to Machine Learning/1. Introduction to Machine Learning.mp4123.83MB

2. Introduction to Machine Learning/2. Building a Machine Learning model.mp445.27MB

3. Basics of Statistics/1. Types of Data.mp425.86MB

3. Basics of Statistics/2. Types of Statistics.mp413.24MB

3. Basics of Statistics/3. Describing data Graphically.mp482.16MB

3. Basics of Statistics/4. Measures of Centers.mp445.69MB

3. Basics of Statistics/6. Measures of Dispersion.mp428.38MB

4. Setting up Python and Jupyter Notebook/1. Installing Python and Anaconda.mp418.64MB

4. Setting up Python and Jupyter Notebook/2. Opening Jupyter Notebook.mp473.06MB

4. Setting up Python and Jupyter Notebook/3. Introduction to Jupyter.mp451.28MB

4. Setting up Python and Jupyter Notebook/4. Arithmetic operators in Python Python Basics.mp415.97MB

4. Setting up Python and Jupyter Notebook/5. Strings in Python Python Basics.mp480.59MB

4. Setting up Python and Jupyter Notebook/6. Lists, Tuples and Directories Python Basics.mp473.66MB

4. Setting up Python and Jupyter Notebook/7. Working with Numpy Library of Python.mp454.1MB

4. Setting up Python and Jupyter Notebook/8. Working with Pandas Library of Python.mp456.4MB

4. Setting up Python and Jupyter Notebook/9. Working with Seaborn Library of Python.mp448.86MB

5. Data Preprocessing/1. Gathering Business Knowledge.mp425.13MB

5. Data Preprocessing/10. Outlier treatment in Python.mp458.44MB

5. Data Preprocessing/12. Missing Value Imputation.mp427.56MB

5. Data Preprocessing/13. Missing Value Imputation in Python.mp427.65MB

5. Data Preprocessing/15. Seasonality in Data.mp420.88MB

5. Data Preprocessing/16. Variable Transformation.mp415.29MB

5. Data Preprocessing/17. Variable transformation and Deletion in Python.mp435.58MB

5. Data Preprocessing/19. Dummy variable creation Handling qualitative data.mp440.6MB

5. Data Preprocessing/2. Data Exploration.mp423.4MB

5. Data Preprocessing/20. Dummy variable creation in Python.mp433.87MB

5. Data Preprocessing/3. The Dataset and the Data Dictionary.mp487.62MB

5. Data Preprocessing/4. Data Import in Python.mp425.49MB

5. Data Preprocessing/6. Univariate analysis and EDD.mp427.31MB

5. Data Preprocessing/7. EDD in Python.mp497.09MB

5. Data Preprocessing/9. Outlier Treatment.mp427.77MB

6. Classification Models/1. Three Classifiers and the problem statement.mp422.93MB

6. Classification Models/10. Confusion Matrix.mp426.65MB

6. Classification Models/11. Making Confusion Matrix in Python.mp464.71MB

6. Classification Models/12. Evaluating performance of model.mp442.79MB

6. Classification Models/13. Evaluating model performance in Python.mp411.77MB

6. Classification Models/15. Linear Discriminant Analysis.mp448.68MB

6. Classification Models/16. LDA in Python.mp414.38MB

6. Classification Models/18. Test-Train Split.mp445.69MB

6. Classification Models/19. Test-Train Split in Python.mp443.1MB

6. Classification Models/2. Why can't we use Linear Regression.mp420.4MB

6. Classification Models/21. K-Nearest Neighbors classifier.mp483.57MB

6. Classification Models/22. K-Nearest Neighbors in Python Part 1.mp445.85MB

6. Classification Models/23. K-Nearest Neighbors in Python Part 2.mp451.98MB

6. Classification Models/25. Understanding the results of classification models.mp445.98MB

6. Classification Models/26. Summary of the three models.mp425.25MB

6. Classification Models/3. Logistic Regression.mp439.11MB

6. Classification Models/4. Training a Simple Logistic Model in Python.mp461.19MB

6. Classification Models/6. Result of Simple Logistic Regression.mp431.15MB

6. Classification Models/7. Logistic with multiple predictors.mp49.99MB

6. Classification Models/8. Training multiple predictor Logistic model in Python.mp434.02MB