Resolving and offering solutions to your machine learning problems with R

About This Book

• Implement a wide range of algorithms and techniques for tackling complex data
• Improve predictions and recommendations to have better levels of accuracy
• Optimize performance of your machine-learning systems

Who This Book Is For

This book is for analysts, statisticians, and data scientists with knowledge of fundamentals of machine learning and statistics, who need help in dealing with challenging scenarios faced every day of working in the field of machine learning and improving system performance and accuracy. It is assumed that as a reader you have a good understanding of mathematics. Working knowledge of R is expected.

What You Will Learn

• Get equipped with a deeper understanding of how to apply machine-learning techniques
• Implement each of the advanced machine-learning techniques
• Solve real-life problems that are encountered in order to make your applications produce improved results
• Gain hands-on experience in problem solving for your machine-learning systems
• Understand the methods of collecting data, preparing data for usage, training the model, evaluating the model's performance, and improving the model's performance

In Detail

Machine learning has become the new black. The challenge in today's world is the explosion of data from existing legacy data and incoming new structured and unstructured data. The complexity of discovering, understanding, performing analysis, and predicting outcomes on the data using machine learning algorithms is a challenge. This cookbook will help solve everyday challenges you face as a data scientist. The application of various data science techniques and on multiple data sets based on real-world challenges you face will help you appreciate a variety of techniques used in various situations.

The first half of the book provides recipes on fairly complex machine-learning systems, where you'll learn to explore new areas of applications of machine learning and improve its efficiency. That includes recipes on classifications, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and more.

The second half of the book focuses on three different machine learning case studies, all based on real-world data, and offers solutions and solves specific machine-learning issues in each one.

Style and approach

Following a cookbook approach, we'll teach you how to solve everyday difficulties and struggles you encounter.

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Spis treści

Practical Machine Learning Cookbook. Supervised and unsupervised machine learning simplified eBook -- spis treści

• 1. Introduction
• 2. Classification
• 3. Clustering
• 4. Model Selection and Regularization
• 5. Non-linearity
• 6. Supervised Learning
• 7. Unsupervised Learning
• 8. Reinforcement learning
• 9. Structured prediction
• 10. Neural Networks
• 11. Deep Learning
• 12. Case Study 1
• 13. Case Study 2
• 14. Case Study 3