Machine learning Practical Notes (Python3 implementation) 01--overview

written in front: These one months are learning python, from the Python3 Foundation, Python crawlers, Python data mining and data analysis have contact, recently saw a machine learning book (mainly learning related algorithms)

So I intend to do this machine learning notes, the main source of the note is "machine learning combat" and some online blog information and their own understanding, mainly to do my personal study, beginner level is limited, the text will inevitably have

Mistakes and omissions in the place, if there is a mistake, but also please enlighten me, thank you!

Machine learning algorithms are divided into supervised learning and unsupervised learning .

The first two sections of this book are about supervised learning, and the third part is about unsupervised learning (also known as clustering).

There are two functions of supervised learning, one is classification (the first part of this book is introduced), and one is regression prediction (the second part of this book is introduced).

This has a general grasp of the idea of the book.

This book deals with algorithms including:K-Nearest neighbor algorithm (KNN), decision tree, naive Bayesian, logistic regression, support vector machine (SVM), AdaBoost algorithm, K-means clustering algorithm (K-means), Apriori algorithm, PCA , etc.

In addition, the book is through the " principle of a brief + problem instances + actual code + running effect " to introduce each algorithm separately,

The original book code is implemented in Python2, which is partially modified to allow it to run in Python3, and the code focuses on helping to deepen the understanding of the algorithm principle.

Machine learning Practical Notes (Python3 implementation) 01--overview