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Introduction to Machine Learning with R: Rigorous Mathematical Analysis

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Machine learning is an intimidating subject until you know the fundamentals. If you understand basic coding concepts, this introductory guide will help you gain a solid foundation in machine learning principles. Using the R programming language, you’ll first start to learn with regression modelling and then move into more advanced topics such as neural networks and tree-based methods.

Finally, you’ll delve into the frontier of machine learning, using the caret package in R. Once you develop a familiarity with topics such as the difference between regression and classification models, you’ll be able to solve an array of machine learning problems. Author Scott V. Burger provides several examples to help you build a working knowledge of machine learning.

  • Explore machine learning models, algorithms, and data training
  • Understand machine learning algorithms for supervised and unsupervised cases
  • Examine statistical concepts for designing data for use in models
  • Dive into linear regression models used in business and science
  • Use single-layer and multilayer neural networks for calculating outcomes
  • Look at how tree-based models work, including popular decision trees
  • Get a comprehensive view of the machine learning ecosystem in R
  • Explore the powerhouse of tools available in R’s caret package
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Scientific Computing with Python 3
Scientific Computing with Python 3

Key Features

  • Your ultimate resource for getting up and running with Python numerical computations
  • Explore numerical computing and mathematical libraries using Python 3.x code with SciPy and NumPy modules
  • A hands-on guide to implementing mathematics with Python, with complete...
SQL Primer: An Accelerated Introduction to SQL Basics
SQL Primer: An Accelerated Introduction to SQL Basics
Modern society is driven by data. Whether it is at a personal level, like a notebook containing scribbled notes; or at a countrywide level like Census data, it has permeated all our workflows. There is always a growing need to efficiently store and organize it so that meaningful information can be extracted out of raw data. ...
Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master
Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master

Whether you have some experience with Tableau software or are just getting started, this manual goes beyond the basics to help you build compelling, interactive data visualization applications. Author Ryan Sleeper, one of the world’s most qualified Tableau consultants, complements his web posts and instructional videos with...


Practical SQL: A Beginner's Guide to Storytelling with Data
Practical SQL: A Beginner's Guide to Storytelling with Data

Practical SQL is an approachable and fast-paced guide to SQL (Structured Query Language), the standard programming language for defining, organizing, and exploring data in relational databases. The book focuses on using SQL to find the story your data tells, with the popular open-source database PostgreSQL and the pgAdmin interface as...

Adopting Elixir: From Concept to Production
Adopting Elixir: From Concept to Production

Adoption is more than programming. Elixir is an exciting new language, but to successfully get your application from start to finish, you're going to need to know more than just the language. The case studies and strategies in this book will get you there. Learn the best practices for the whole life of your application, from...

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

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