Mathematical Approaches To Neural NetWorks
pdf | 6.18 MB | English | Isbn:B0114P1K1C |
Author: Taylor, J. G. | PAge: 391 | Year: 2015
Build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R
- Harness the power of R for statistical computing and data science
- Explore, forecast, and classify data with R
- Use R to apply common machine learning algorithms to real-world scenarios
Machine learning, at its core, is concerned with transforming data into actionable knowledge. This makes machine learning well suited to the present-day era of big data. Given the growing prominence of R’s cross-platform, zero-cost statistical programming environment, there has never been a better time to start applying machine learning to your data. Machine learning with R offers a powerful set of methods to quickly and easily gain insight from your data to both, veterans and beginners in data analytics.
Want to turn your data into actionable knowledge, predict outcomes that make real impact, and have constantly developing insights? R gives you access to all the power you need to master exceptional machine learning techniques.
The second edition of Machine Learning with R provides you with an introduction to the essential skills required in 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 to 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. Transform the way you think about data; discover machine learning with R.
What you will learn
- Harness the power of R to build common machine learning algorithms with real-world 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 For
Perhaps 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.
Table of Contents
- Introducing Machine Learning
- Managing and Understanding Data
- Lazy Learning – Classification Using Nearest Neighbors
- Probabilistic Learning – Classification Using Naive Bayes
- Divide and Conquer – Classification Using Decision 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
Category:Machine Theory, Mathematical & Statistical, Algorithm Programming