Mastering Machine Learning with R: Advanced machine learning techniques for building smart applications with R 3.5, 3rd Edition
By 作者: Cory Lesmeister
ISBN-10 书号: 1789618002
ISBN-13 书号: 9781789618006
Release Finelybook 出版日期: 2019-01-31
pages 页数: (354 )
Book Description to Finelybook sorting
Given the growing popularity of R-zero-cost statistical programming environment, there has never been a better time to start applying ML to your data. This book will teach you advanced techniques in ML with the latest code in R 3.5. You will delve into various complex features of supervised learning, unsupervised learning and reinforcement learning algorithms to design efficient and powerful ML models.
This newly updated edition is packed with fresh examples covering a range of tasks from different domains. Mastering Machine Learning with R starts by showing you how to quickly manipulate data and prepare it for analysis. You will explore simple and complex models and understand how to compare them. You’ll also learn to use the latest library support such as TensorFlow and Keras-R for performing advanced computations. Additionally, you’ll explore complex topics such as natural language processing (NLP), time series analysis, and clustering, which will further refine your skills in developing applications. Each chapter will help you implement advanced ML algorithms using real-world examples. You’ll even be introduced to reinforcement learning along with its various use cases and models. Towards the concluding chapters, you’ll get a glimpse into how some of these black-box models can be diagnosed and understood.
By the end of this book, you’ll be equipped with the skills to deploy ML techniques in your own projects or at work.
1: PREPARING AND UNDERSTANDING DATA
2: LINEAR REGRESSION
3: LOGISTIC REGRESSION
4: ADVANCED FEATURE SELECTION IN LINEAR MODELS
5: K-NEAREST NEIGHBORS AND SUPPORT VECTOR MACHINES
6: TREE-BASED CLASSIFICATION
7: NEURAL NETWORKS AND DEEP LEARNING
8: CREATING ENSEMBLES AND MULTICLASS METHODS
9: CLUSTER ANALYSIS
10: PRINCIPAL COMPONENT ANALYSIS
11: ASSOCIATION ANALYSIS
12: TIME SERIES AND CAUSALITY
13: TEXT MINING
What You Will Learn
Prepare data for machine learning methods with ease
Learn to write production-ready code and package it for use
Produce simple and effective data visualizations for improved insights
Master advanced methods such as Boosted Trees and deep neural networks
Use natural language processing to extract insights for text
Implement tree-based classifiers including Random Forest and Boosted Tree
Cory Lesmeister has over fourteen years of quantitative experience and is currently a senior data scientist for the Advanced Analytics team at Cummins, Inc. in Columbus, Indiana. Cory spent 16 years at Eli Lilly and Company in sales, market research, Lean Six Sigma, marketing analytics, and new product forecasting. He also has several years of experience in the insurance and banking industries, both as a consultant and as a manager of marketing analytics. A former US Army active duty and reserve officer, Cory was stationed in Baghdad, Iraq, in 2009 serving as the strategic advisor to the 29,000-person Iraqi Oil Police, succeeding where others failed by acquiring and delivering promised equipment to help the country secure and protect its oil infrastructure. Cory has a BBA in Aviation Administration from the University of North Dakota and a commercial helicopter license.