Machine Learning 机器学习笔记目录

目录

• 前言

第一周：Welcome

• 1.1 What is Machine Learning?
• 1.2 Linear Regression with One Variable

第二周：Linear Regression with Multiple Variables

• 2.1 Multivariate Linear Regression
• 2.2 Computing Parameters Analytically
• 2.3 Octave/Matlab Tutorial

第三周：Logistic Regression

• 3.1 Logistic Regression
• 3.2 Regularization

第四周：Neural Networks: Representation

• 4.1 Neural Networks Representation

第五周：Neural Networks: Learning

• 5.1 Neural Networks Learning
• 5.2 Backpropagation in Practice

第六周：Advice for Applying Machine Learning

• 6.1 Advice for Applying Machine Learning
• 6.2 Machine Learning System Design

第七周：Support Vector Machines

• 7.1 Support Vector Machines

第八周：Unsupervised Learning

• 8.1 Unsupervised Learning
• 8.2 Dimensionality Reduction

第九周：Anomaly Detection

• 9.1 Anomaly Detection
• 9.2 Recommender Systems

第十周：Large Scale Machine Learning

• 10.1 Large Scale Machine Learning

第十一周：Application Example: Photo OCR

• 11.1 Application Example: Photo OCR

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GitHub Repo：Halfrost-Field

Follow: halfrost · GitHub

Source: https://github.com/halfrost/Halfrost-Field/blob/master/contents/Machine_Learning/contents.md