English | 2019 | ISBN: 1789618006 | 354 Pages | EPUB | 37 MB
Stay updated with expert techniques for solving data analytics and machine learning challenges and gain insights from complex projects and power up your applications
Given the growing popularity of the R-zerocost 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 ,using? 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. In the concluding chapters, you’ll get a glimpse into how some of these blackbox 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.
What you will learn
- Prepare data for machine learning methods with ease
- Understand how 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 in relation to text
- Implement tree-based classifiers, including Random Forest and Boosted Tree