Lantz Machine Learning with R

Expert techniques for predictive modeling to solve all your data analysis problems
2. Auflage 2024
ISBN: 978-1-78439-452-3
Verlag: De Gruyter
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 452 Seiten

Key FeaturesBook DescriptionUpdated and upgraded to the latest libraries and most modern thinking, Machine Learning with R, Second Edition provides you with a rigorous introduction to this essential skill of professional data science. Without shying away from technical theory, it is written to provide focused and practical knowledge to get you building algorithms and crunching your data, with minimal previous experience. With this book, you'll discover all the analytical tools you need to gain insights from complex data and learn how to choose the correct algorithm for your specific needs. Through full engagement with the sort of real-world problems data-wranglers face, you'll learn to apply machine learning methods to deal with common tasks, including classification, prediction, forecasting, market analysis, and clustering.What you will learn - Harness the power of R to build common machine learning algorithms with realworld data science applications
- Get to grips with techniques in R to clean and prepare your data for analysis and visualize your results
- Discover the different types of machine learning models and learn what is best to meet your data needs and solve data analysis problems
- Classify your data with Bayesian and nearest neighbour methods
- Predict values using R to build decision trees, rules, and support vector machines
- Forecast numeric values with linear regression and model your data with neural networks
- Evaluate and improve the performance of machine learning models
- Learn specialized machine learning techniques for text mining, social network data, and big data
Who this book is forPerhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.

Autoren/Hrsg.

Lantz, Brett

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software

Inhaltsverzeichnis

Table of Contents - Introducing Machine Learning
- Managing and Understanding Data
- Lazy Learning: Classification using Nearest Neighbors
- Probabilistic Learning: Classification using Na
- Divide and Conquer: Classification using Trees and Rules
- Forecasting Numeric Data: Regression Methods
- Black Box Methods: Neural Networks and Support Vector Machines
- Finding Patterns: Market Basket Analysis Using Association Rules
- Finding Groups of Data: Clustering with k-means
- Evaluating Model Performance
- Improving Model Performance
- Specialized Machine Learning Topics

Über Autor(innen)

Lantz Brett :

Brett Lantz (DataSpelunking) has spent more than 10 years using innovative data methods to understand human behavior. A sociologist by training, Brett was first captivated by machine learning during research on a large database of teenagers' social network profiles. Brett is a DataCamp instructor and a frequent speaker at machine learning conferences and workshops around the world. He is known to geek out about data science applications for sports, autonomous vehicles, foreign language learning, and fashion, among many other subjects, and hopes to one day blog about these subjects at Data Spelunking, a website dedicated to sharing knowledge about the search for insight in data.