coursera introduction to machine learning

-schonberger, and Kenneth CukierA Short but insightful manuscript that'll motivate you to rethink how we should face the explosive growth of data in the New century.Statistical Pattern Recognition (2nd/3rd Edition)Andrew R. Webb, and Keith D. CopseyA well written book on the pattern Recognition for beginners. It covers basic topics in this field, including discriminant analysis, decision Trees, feature selection, and clustering- -All is basic knowledge that researchers in

also covers some recent developments in the field of pattern recognition and machine learning, which is not only suitable for beginners, but also has great reference value for professional researchers.A total of 738 pages, divided into 14 chapters, gradual, forward and backward echo, express clearly, understand deeply. Each chapter has corresponding exercises and answers, which is helpful for

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology, application interested students to join.Recently, while reviewing the classical machine learning algorithms, we also looked at some typical algorithms of deep learning. Deep

: Network Disk DownloadContent Introduction······How far has the algorithm affected our lives?Shopping site using algorithms to recommend products for you, review website using algorithms to help you choose restaurants, GPS system with algorithms to help you choose the best route, the company used algorithms to select candidates ...What happens when the machine finally learns how to learn?Unlike traditional

An introductory tutorial on machine learning with a higher degree of identity, by Andrew Ng of Stanford. NetEase public class with Chinese and English subtitles teaching video resources (http://open.163.com/special/opencourse/ machinelearning.html), handout stamp here: http://cs229.stanford.edu/materials.htmlThere are a variety of similar course learning notes on

I. Introduction of supervised learningThe supervised machine learning problem is nothing more than "Minimizeyour error while regularizing your parameters", which is to minimize errors while the parameters are being parameterized. The minimization error is to let our model fit our training data, and the rule parameter is to prevent our model from overfitting our t

Series of articles: Learning Notes for machine learningThis is the first chapter of machine learning, this chapter briefly describes what is machine learning, the main task of machine

Summary of advantages and disadvantages of machine learning common algorithmsk Nearest Neighbor : The algorithm uses the method of measuring the distance between different eigenvalues to classify.Advantages:1. Easy to use, easy to understand, high precision, mature theory, can be used to do classification can also be used to do regression;2. Can be used for numerical data and discrete data;3. The training t

Introduction to Python machine learning The first chapter is to let the computer learn from the data Turn data into knowledge Three kinds of machine learning algorithms Chapter II Training machine