机器学习精通 - 从数据到高级分类器
上次更新时间：2024-11-21
课程售价: 2.9 元
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课程内容
2. Course Contents
- 1. Import Data
- 2. 2 visualizing missing data in a dataset
- 3. 3 calculating statistical information
- 4. 4 checking for duplicate rows in the DataFrame
- 5. 5 calculating the number of distinct values in each column
- 6. 6 checking for missing or null values in the DataFrame
- 7. 7 Cleaning the data
- 8. 8 creating a new column called 'Label' in the DataFrame
- 9. 9 creating a histogram plot
- 10. 10 displaying the distribution of the data using a box plot
- 11. 11 displaying the distribution of the data by the different categories
- 12. 12 visualize the relationship between two variables with jointplot
- 13. 13 calculating the correlation matrix of the DataFrame
- 14. 14 creating a mask using NumPy
- 15. 15 creating a color map using seaborn
- 16. 16 creating a heatmap using seaborn
- 17. 17 calculating the number of outliers
- 18. 18 standardizing features
- 19. 19 Hypothesis testing
- 20. 20 Normalization
- 21. 21 split the data into training and testing sets
- 22. 22 Start traning SVC and Learn Hyperparameters
- 23. 23 find the best hyperparameter
- 24. 24 make predictions on the test data and avaluate the model
- 25. 25 Train RandomForestClassifier
- 26. 26 Train XGBClassifier
- 27. 27 Train KNeighborsClassifier
- 28. 28 Train LGBMClassifier
- 29. 29 calculate the (ROC) curve and the (AUC) score
课程内容
2个章节 , 32个讲座
2. Course Contents
- 1. Import Data
- 2. 2 visualizing missing data in a dataset
- 3. 3 calculating statistical information
- 4. 4 checking for duplicate rows in the DataFrame
- 5. 5 calculating the number of distinct values in each column
- 6. 6 checking for missing or null values in the DataFrame
- 7. 7 Cleaning the data
- 8. 8 creating a new column called 'Label' in the DataFrame
- 9. 9 creating a histogram plot
- 10. 10 displaying the distribution of the data using a box plot
- 11. 11 displaying the distribution of the data by the different categories
- 12. 12 visualize the relationship between two variables with jointplot
- 13. 13 calculating the correlation matrix of the DataFrame
- 14. 14 creating a mask using NumPy
- 15. 15 creating a color map using seaborn
- 16. 16 creating a heatmap using seaborn
- 17. 17 calculating the number of outliers
- 18. 18 standardizing features
- 19. 19 Hypothesis testing
- 20. 20 Normalization
- 21. 21 split the data into training and testing sets
- 22. 22 Start traning SVC and Learn Hyperparameters
- 23. 23 find the best hyperparameter
- 24. 24 make predictions on the test data and avaluate the model
- 25. 25 Train RandomForestClassifier
- 26. 26 Train XGBClassifier
- 27. 27 Train KNeighborsClassifier
- 28. 28 Train LGBMClassifier
- 29. 29 calculate the (ROC) curve and the (AUC) score