0.Overview----Machine Learning

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

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0.Overview----Machine Learning

1.How Models Work

2.Explore Your Data

3.Your First Machine Learning Model

4.Model Validation

5.Underfitting and Overfitting

6.Random Forests

7.Handling Missing Values

8.Using Categorical Data with One Hot Encoding

9.XGBoost

10.Partial Dependence Plots

11.Piplines

12.Cross-Validation

13.Data Leakage

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