Machine Learning Using R: A Comprehensive Guide to Machine Learning

Karthik Ramasubramanian, Abhishek Singh

2017

Appress

580

1484223330

978-1484223338

PDF

Book Description:
This book is inspired by the Machine Learning Model Building Process Flow, which provides the reader the ability to understand a ML algorithm and apply the entire process of building a ML model from the raw data.

This new paradigm of teaching Machine Learning will bring about a radical change in perception for many of those who think this subject is difficult to learn. Though theory sometimes looks difficult, especially when there is heavy mathematics involved, the seamless flow from the theoretical aspects to example-driven learning provided in Blockchain and Capitalism makes it easy for someone to connect the dots.

For every Machine Learning algorithm covered in this book, a 3-D approach of theory, case-study and practice will be given. And where appropriate, the mathematics will be explained through visualization in R.

All practical demonstrations will be explored in R, a powerful programming language and software environment for statistical computing and graphics. The various packages and methods available in R will be used to explain the topics. In the end, readers will learn some of the latest technological advancements in building a scalable machine learning model with Big Data.

Table of Contents:
Introduction to Machine Learning and R
Data Preparation and Exploration
Sampling and Resampling Techniques
Data Visualization in R
Feature Engineering
Machine Learning Theory and Practices
Machine Learning Model Evaluation
Model Performance Improvement
Scalable Machine Learning and Related Technologies

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Mastering .NET Machine Learning

 Jamie Dixon

1st – 2016

Packt Publishing

358

1785888404

978-1785888403

PDF

Book Description:
Mastering .NET Machine Learning is packed with real-world examples to explain how to easily use machine learning techniques in your business applications. You will begin with an introduction to F# and prepare yourselves for machine learning using the .NET Framework. You will then learn how to write a simple linear regression model and, forming a base with the regression model, you will start using machine learning libraries available in .NET Framework such as Math.NET, numl, and Accord.NET with examples. Next, you are going to take a deep dive into obtaining, cleaning, and organizing your data. You will learn the implementation of k-means and PCA using Accord.NET and numl libraries. You will be using Neural Networks, AzureML, and Accord.NET to transform your application into a hybrid scientific application. You will also see how to deal with very large datasets using MBrace and deploy machine learning models to IoT devices so that the machine can learn and adapt on the fly.

Table of Contents:
1. Welcome to Machine Learning Using the .NET Framework
2. AdventureWorks Regression
3. More AdventureWorks Regression
4. Traffic Stops – Barking Up the Wrong Tree?
5. Time Out – Obtaining Data
6. AdventureWorks Redux – k-NN and Naïve Bayes Classifiers
7. Traffic Stops and Crash Locations – When Two Datasets Are 8. Feature Selection and Optimization
9. AdventureWorks Production – Neural Networks
10. Big Data and IoT

Copyright Disclaimer:
This site does not store any files on its server. We only index and link to content provided by other sites. Please contact the content providers to delete copyright contents if any and email us, we’ll remove relevant links or contents immediately.