本文为Kaggle Learn的Machine Learning课程的中文翻译，原文链接为：https://www.kaggle.com/learn/machine-learning
1.How Models Work
- The first step if you're new to machine learning
2.Explore Your Data
- Load data and set up your environment for your hands-on project
3.Your First Machine Learning Model
- Building your first model!
4.Model Validation
- Measure the performance of your model?so you can test and compare alternatives
5.Underfitting and Overfitting
- Fine-tune your model for better performance
6.Random Forests
- Using a more sophisticated machine learning algorithm
7.Handling Missing Values
- Learn multiple approaches for dealing with missing data fields
8.Using Categorical Data with One Hot Encoding
- Handle this important but challenging data type
9.XGBoost
- The most important technique for building high-performance models on conventional data(the type that fits in tables or data frames)
10.Partial Dependence Plots
- Extract insights from your models,Insights many didn't even realize were possible.
11.Piplines
- Make your machine learning code cleaner and more professional
12.Cross-Validation
- Improve how you compare and choose models and data preprocessing
13.Data Leakage
- Identify and avoid one of the most common and costly mistakes in machine learning