R: Unleash Machine Learning Techniques (2016)

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R: Unleash Machine Learning Techniques

Raghav Bali et al.



Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner.

R is the established language of data analysts and statisticians around the world. And you shouldn’t be afraid to use it…

This Learning Path will take you through the fundamentals of R and demonstrate how to use the language to solve a diverse range of challenges through machine learning. Accessible yet comprehensive, it provides you with everything you need to become more a more fluent data professional, and more confident with R.

In the first module you’ll get to grips with the fundamentals of R. This means you’ll be taking a look at some of the details of how the language works, before seeing how to put your knowledge into practice to build some simple machine learning projects that could prove useful for a range of real world problems.

For the following two modules we’ll begin to investigate machine learning algorithms in more detail. To build upon the basics, you’ll get to work on three different projects that will test your skills. Covering some of the most important algorithms and featuring some of the most popular R packages, they’re all focused on solving real problems in different areas, ranging from finance to social media.

Table of Contents

1: GETTING STARTED WITH R AND MACHINE LEARNING
2: LET'S HELP MACHINES LEARN
3: PREDICTING CUSTOMER SHOPPING TRENDS WITH MARKET BASKET ANALYSIS
4: BUILDING A PRODUCT RECOMMENDATION SYSTEM
5: CREDIT RISK DETECTION AND PREDICTION – DESCRIPTIVE ANALYTICS
6: CREDIT RISK DETECTION AND PREDICTION – PREDICTIVE ANALYTICS
7: SOCIAL MEDIA ANALYSIS – ANALYZING TWITTER DATA
8: SENTIMENT ANALYSIS OF TWITTER DATA
9: INTRODUCING MACHINE LEARNING
10: MANAGING AND UNDERSTANDING DATA
11: LAZY LEARNING – CLASSIFICATION USING NEAREST NEIGHBORS
12: PROBABILISTIC LEARNING – CLASSIFICATION USING NAIVE BAYES
13: DIVIDE AND CONQUER – CLASSIFICATION USING DECISION TREES AND RULES
14: FORECASTING NUMERIC DATA – REGRESSION METHODS
15: BLACK BOX METHODS – NEURAL NETWORKS AND SUPPORT VECTOR MACHINES
16: FINDING PATTERNS – MARKET BASKET ANALYSIS USING ASSOCIATION RULES
17: FINDING GROUPS OF DATA – CLUSTERING WITH K-MEANS
18: EVALUATING MODEL PERFORMANCE
19: IMPROVING MODEL PERFORMANCE
20: SPECIALIZED MACHINE LEARNING TOPICS
21: A PROCESS FOR SUCCESS
22: LINEAR REGRESSION – THE BLOCKING AND TACKLING OF MACHINE LEARNING
23: LOGISTIC REGRESSION AND DISCRIMINANT ANALYSIS
24: ADVANCED FEATURE SELECTION IN LINEAR MODELS
25: MORE CLASSIFICATION TECHNIQUES – K-NEAREST NEIGHBORS AND SUPPORT VECTOR MACHINES
26: CLASSIFICATION AND REGRESSION TREES
27: NEURAL NETWORKS
28: CLUSTER ANALYSIS
29: PRINCIPAL COMPONENTS ANALYSIS
30: MARKET BASKET ANALYSIS AND RECOMMENDATION ENGINES
31: TIME SERIES AND CAUSALITY
32: TEXT MINING

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关键词：Techniques Technique Learning earning machine learning machine systems through course

谢谢分享。。

R书籍发到R软件版块来

太过包罗万象，实又是啥子没有！！似此出版社的通病！！个人观感，未读过的旁人别信！！

cmwei333 发表于 2016-10-28 00:23
R: Unleash Machine Learning Techniques

Raghav Bali et al.

好的收到

谢谢分享

and you will know you are so young to the field

XIE XIE, A VERY THICK BOOK.

cmwei333 发表于 2016-10-28 00:23
R: Unleash Machine Learning Techniques

Raghav Bali et al.

thank you share