机器学习-hands on meachine learning

No Free Lunch (NFL) theorem

David Wolpert demonstrated that if you make absolutely no assumption about the data, then there is no reason to prefer one model over any other.

Instance-Based Versus Model-Based Learning
Supervised learning
需要人工标注数据
Unsupervised learning
不需要人工标注数据，能获取数据之间的潜在关系

semi-Supervised learning
一部分数据是需要人工处理的，一部分不需要

Active learning

Batch learning
训练数据分批
reinforce learning
Online learning

feature engineering
• Feature selection: selecting the useful features to train on existing features.
• Feature extraction: combining existing features to produce a more useful one.
• Creating new features by gathering new data.

machine learning challenge
1.不够充分的训练数据
2.没有表征的训练数据
3.质量差的训练数据
4.数据中不相关的特征对数据造成干扰。
5.模型训练的过拟合现象.