R.Unleash.Machine.Learning.Techniques

Find out how to build smarter machine learning systems with R. Follow this three module course to become a more fluent machine learning practitioner. About This Book Build your confidence with R and find out how to solve a huge range of data-related problems Get to grips with some of the most important machine learning techniques being used by data scientists and analysts across industries today Don't just learn – apply your knowledge by following featured practical projects covering everything from financial modeling to social media analysis Who This Book Is For Aimed for intermediate-to-advanced people (especially data scientist) who are already into the field of data science What You Will Learn Get to grips with R techniques to clean and prepare your data for analysis, and visualize your results Implement R machine learning algorithms from scratch and be amazed to see the algorithms in action Solve interesting real-world problems using machine learning and R as the journey unfolds Write reusable code and build complete machine learning systems from the ground up Learn specialized machine learning techniques for text mining, social network data, big data, and more Discover the different types of machine learning models and learn which is best to meet your data needs and solve your analysis problems Evaluate and improve the performance of machine learning models Learn specialized machine learning techniques for text mining, social network data, big data, and more In Detail 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. This Learning Path has been curated from three Packt products: R Machine Learning By Example By Raghav Bali, Dipanjan Sarkar Machine Learning with R Learning - Second Edition By Brett Lantz Mastering Machine Learning with R By Cory Lesmeister Style and approach This is an enticing learning path that starts from the very basics to gradually pick up pace as the story unfolds. Each concept is first defined in the larger context of things succinctly, followed by a detailed explanation of their application. Each topic is explained with the help of a project that solves a real-world problem involving hands-on work thus giving you a deep insight into the world of machine learning. Table of Contents Module 1: R Machine Learning By Example Chapter 1: Getting Started with R and Machine Learning Chapter 2: Let's Help Machines Learn Chapter 3: Predicting Customer Shopping Trends with Market Basket Analysis Chapter 4: Building a Product Recommendation System Chapter 5: Credit Risk Detection and Prediction – Descriptive Analytics Chapter 6: Credit Risk Detection and Prediction – Predictive Analytics Chapter 7: Social Media Analysis – Analyzing Twitter Data Chapter 8: Sentiment Analysis of Twitter Data Module 2: Machine Learning with R Chapter 1: Introducing Machine Learning Chapter 2: Managing and Understanding Data Chapter 3: Lazy Learning – Classification Using Nearest Neighbors Chapter 4: Probabilistic Learning – Classification Using Naive Bayes Chapter 5: Divide and Conquer – Classification Using Decision Trees and Rules Chapter 6: Forecasting Numeric Data – Regression Methods Chapter 7: Black Box Methods – Neural Networks and Support Vector Machines Chapter 8: Finding Patterns – Market Basket Analysis Using Association Rules Chapter 9: Finding Groups of Data – Clustering with k-means Chapter 10: Evaluating Model Performance Chapter 11: Improving Model Performance Chapter 12: Specialized Machine Learning Topics Module 3: Mastering Machine Learning with R Chapter 1: A Process for Success Chapter 2: Linear Regression – The Blocking and Tackling of Machine Learning Chapter 3: Logistic Regression and Discriminant Analysis Chapter 4: Advanced Feature Selection in Linear Models Chapter 5: More Classification Techniques – K-Nearest Neighbors and Support Vector Machines Chapter 6: Classification and Regression Trees Chapter 7: Neural Networks Chapter 8: Cluster Analysis Chapter 9: Principal Components Analysis Chapter 10: Market Basket Analysis and Recommendation Engines Chapter 11: Time Series and Causality Chapter 12: Text Mining Appendix: R Fundamentals