Shifting gears away from the Space Shuttle, let's work through how to set up, train, and evaluate a deep learning model. You see these used quite a bit for image classification, NLP, and so on. However, let's look at using it for regression. You don't find too many examples of that in my opinion. As such, let's go with our Ames housing price data we used back in Chapter 2, Linear Regression. Before that, let's briefly discuss what Tensor, TensorFlow, and Keras are.
Mastering Machine Learning with R
By :
Mastering Machine Learning with R
By:
Overview of this book
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.
Table of Contents (16 chapters)
Preface
Free Chapter
Preparing and Understanding Data
Linear Regression
Logistic Regression
Advanced Feature Selection in Linear Models
K-Nearest Neighbors and Support Vector Machines
Tree-Based Classification
Neural Networks and Deep Learning
Creating Ensembles and Multiclass Methods
Cluster Analysis
Principal Component Analysis
Association Analysis
Time Series and Causality
Text Mining
Creating a Package
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