使用 Python 进行机器学习的项目和案例研究
上次更新时间：2024-11-24
课程售价: 2.9 元
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课程内容
- 1. Introduction to Machine Learning Case Studies (免费)
- 2. Environmental SetUp (免费)
- 3. Problem Statement for Linear Regression
- 4. Starting with Normal linear Regression
- 5. Polynomial Regression
- 6. Backward Elimination
- 7. Robust Regression
- 8. Logistic Regression
- 9. Logistic Regression Continue
- 10. Introduction to k-Means Clustering
- 11. Creating Scattered Plots
- 12. Euclidean Distance Calculator
- 13. Printing Centroid Values
- 14. Analysing Face Detection
- 15. Problem Statement
- 16. Creating Model of time Series
- 17. Training and Testing Data
- 18. Analysing Output
- 19. Time Series Bitcoin Data
- 20. Classification
- 21. Fruit type Distribution
- 22. Create Training and Test Sets
- 23. Building Logistic Regression
- 24. Building Decision Tree
- 25. K-Nearest Neighbors
- 26. Linear Discriminant Analysis
- 27. Gaussian Naive Bayes
- 28. Plot the Decision Boundary
- 29. Plot the Decision Boundary Continue
- 30. Defining the Problem Statement
- 31. Data Preparation
- 32. Clean up
- 33. Payment Delays
- 34. Standing Credit
- 35. Payments in the Previous Months
- 36. Explore Defaulting
- 37. Absolute Statistics
- 38. Starting with Feature Engineering
- 39. From Variables to Train
- 40. Visualization-Confusion Matrices and AUC Curves
- 41. Creating SNS Plot
课程内容
41个讲座
- 1. Introduction to Machine Learning Case Studies (免费)
- 2. Environmental SetUp (免费)
- 3. Problem Statement for Linear Regression
- 4. Starting with Normal linear Regression
- 5. Polynomial Regression
- 6. Backward Elimination
- 7. Robust Regression
- 8. Logistic Regression
- 9. Logistic Regression Continue
- 10. Introduction to k-Means Clustering
- 11. Creating Scattered Plots
- 12. Euclidean Distance Calculator
- 13. Printing Centroid Values
- 14. Analysing Face Detection
- 15. Problem Statement
- 16. Creating Model of time Series
- 17. Training and Testing Data
- 18. Analysing Output
- 19. Time Series Bitcoin Data
- 20. Classification
- 21. Fruit type Distribution
- 22. Create Training and Test Sets
- 23. Building Logistic Regression
- 24. Building Decision Tree
- 25. K-Nearest Neighbors
- 26. Linear Discriminant Analysis
- 27. Gaussian Naive Bayes
- 28. Plot the Decision Boundary
- 29. Plot the Decision Boundary Continue
- 30. Defining the Problem Statement
- 31. Data Preparation
- 32. Clean up
- 33. Payment Delays
- 34. Standing Credit
- 35. Payments in the Previous Months
- 36. Explore Defaulting
- 37. Absolute Statistics
- 38. Starting with Feature Engineering
- 39. From Variables to Train
- 40. Visualization-Confusion Matrices and AUC Curves
- 41. Creating SNS Plot