R Machine Learning Projects

Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5

Dr. Sunil Kumar Chinnamgari

• Editorial: Packt Publishing
• Año: 2019
• ISBN:
  9781789806090
• Edición: 1
• Categoría: Computer Science
• Categoría: Computer Science General

Master a range of machine learning domains with real-world projects using TensorFlow for R, H2O, MXNet, and more

Key Features

• Master machine learning, deep learning, and predictive modeling concepts in R 3.5
• Build intelligent end-to-end projects for finance, retail, social media, and a variety of domains
• Implement smart cognitive models with helpful tips and best practices

Book Description

R is one of the most popular languages when it comes to performing computational statistics (statistical computing) easily and exploring the mathematical side of machine learning. With this book, you will leverage the R ecosystem to build efficient machine learning applications that carry out intelligent tasks within your organization.

This book will help you test your knowledge and skills, guiding you on how to build easily through to complex machine learning projects. You will first learn how to build powerful machine learning models with ensembles to predict employee attrition. Next, you'll implement a joke recommendation engine and learn how to perform sentiment analysis on Amazon reviews. You'll also explore different clustering techniques to segment customers using wholesale data. In addition to this, the book will get you acquainted with credit card fraud detection using autoencoders, and reinforcement learning to make predictions and win on a casino slot machine.

By the end of the book, you will be equipped to confidently perform complex tasks to build research and commercial projects for automated operations.

What you will learn

• Explore deep neural networks and various frameworks that can be used in R
• Develop a joke recommendation engine to recommend jokes that match users' tastes
• Create powerful ML models with ensembles to predict employee attrition
• Build autoencoders for credit card fraud detection
• Work with image recognition and convolutional neural networks
• Make predictions for casino slot machine using reinforcement learning
• Implement NLP techniques for sentiment analysis and customer segmentation

Who this book is for

If you're a data analyst, data scientist, or machine learning developer who wants to master machine learning concepts using R by building real-world projects, this is the book for you. Each project will help you test your skills in implementing machine learning algorithms and techniques. A basic understanding of machine learning and working knowledge of R programming is necessary to get the most out of this book.

Image Recognition Using Deep Neural Networks

In 1966, Professor Seymour Papert at MIT conceptualized an ambitious summer project titled The Summer Vision Project. The task for the graduate student was to plug a camera into a computer and enable it to understand what it sees! I am sure it would have been super-difficult for the graduate student to have finished this project, as even today the task remains half complete.

A human being, when they look outside, is able to recognize the objects that they see. Without thinking, they are able to classify a cat as a cat, a dog as a dog, a plant as a plant, an animal as an animal—this is happening because the human brain draws knowledge from its extensive prelearned database. After all, as human beings, we have millions of years' worth of evolutionary context that enables us draw inferences from the thing that we see. Computer vision deals with replicating the human vision processes so as to pass them on to machines and automate them.

This chapter is all about learning the theory and implementation of computer vision through machine learning (ML). We will build a feedforward deep learning network and LeNet to enable handwritten digit recognition. We will also build a project that uses a pretrained Inception-BatchNorm network to identify objects in an image. We will cover the following topics as we progress in this chapter:

• Understanding computer vision
• Achieving computer vision with deep learning
• Introduction to the MNIST dataset
• Implementing a deep learning network for handwritten digit recognition
• Implementing computer vision with pretrained models

Technical requirements

For the projects covered in this chapter, we'll make use of a very popular open dataset called MNIST. We'll use Apache MXNet, a modern open source deep learning software framework to train and deploy the required deep neural networks.

Understanding computer vision

In today's world, we have advanced cameras that are very successful at mimicking how a human eye captures light and color; but image-capturing in the right way is just stage one in the whole image-comprehension aspect. Post image-capturing, we will need to enable technology that interprets what has been captured and build context around it. This is what the human brain does when the eyes see something. Here comes the huge challenge: we all know that computers see images as huge piles of integer values that represent intensities across a spectrum of colors, and of course, computer have no context associated with the image itself. This is where ML comes into play. ML allows us to train a context for a dataset such that it enables computers to understand what objects certain sequences of numbers actually represent.

Computer vision is one of the emerging areas where ML is applied. It can be used for several purposes in various domains, including healthcare, agriculture, insurance, and the automotive industry. The following are some of its most popular applications:

• Detecting diseases from medical images, such as CT scan/MRI scan images
• Identifying crop diseases and soil quality to support a better crop yield
• Identifying oil reserves from satellite images
• Self-driving cars
• Monitoring and managing skin condition for psoriasis patients
• Classifying and distinguishing weeds from crops
• Facial recognition
• Extracting information from personal documents, such as passports and ID cards
• Detecting terrain for drones and airplanes
• Biometrics
• Public surveillance
• Organizing personal photos
• Answering visual questions

This is just the tip of the iceberg. It's not an overstatement to say that there is no domain where we cannot find an application for computer vision. Therefore, computer vision is a key area for ML p...