# 应用机器学习：算法 In the first installment of the Applied Machine Learning series, instructor Derek Jedamski covered foundational concepts, providing you with a general recipe to follow to attack any machine learning problem in a pragmatic, thorough manner. In this course—the second and final installment in the series—Derek builds on top of that architecture by exploring a variety of algorithms, from logistic regression to gradient boosting, and showing how to set a structure that guides you through picking the best one for the problem at hand. Each algorithm has its pros and cons, making each one the preferred choice for certain types of problems. Understanding what actually drives each algorithm, as well as their benefits and drawbacks, can give you a significant competitive advantage as a data scientist.

Topics include:

• Models vs. algorithms
• Cleaning continuous and categorical variables
• Tuning hyperparameters
• Pros and cons of logistic regression
• Fitting a support vector machines model
• When to consider using a multilayer perceptron model
• Using the random forest algorithm
• Fitting a basic boosting model

## 课程信息

• 英文名称：Applied Machine Learning: Algorithms
• 时长：2小时24分
• 字幕：英语

## 课程目录

1. The power of algorithms in machine learning
2. What you should know
3. What tools you need
4. Using the exercise files
5. Defining model vs. algorithm
6. Process overview
7. Clean continuous variables
8. Clean categorical variables
9. Split into train, validation, and test set
10. What is logistic regression?
11. When should you consider using logistic regression?
12. What are the key hyperparameters to consider?
13. Fit a basic logistic regression model
14. What is Support Vector Machine?
15. When should you consider using SVM?
16. What are the key hyperparameters to consider?
17. Fit a basic SVM model
18. What is a multi-layer perceptron?
19. When should you consider using a multi-layer perceptron?
20. What are the key hyperparameters to consider?
21. Fit a basic multi-layer perceptron model
22. What is Random Forest?
23. When should you consider using Random Forest?
24. What are the key hyperparameters to consider?
25. Fit a basic Random Forest model
26. What is boosting?
27. When should you consider using boosting?
28. What are the key hyperparameters to consider boosting?
29. Fit a basic boosting model
30. Why do you need to consider so many different models?
31. Conceptual comparison of algorithms
32. Final model selection and evaluation
33. Next steps