Skip to main content
main-content

Über dieses Buch

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.

Who This Book is For:

Data scientists, data science professionals and researchers in academia who want to understand the nuances of Machine learning approaches/algorithms along with ways to see them in practice using R. The book will also benefit the readers who want to understand the technology behind implementing a scalable machine learning model using Apache Hadoop, Hive, Pig and Spark.

What you will learn:

1. ML model building process flow2. Theoretical aspects of Machine Learning3. Industry based Case-Study4. Example based understanding of ML algorithm using R5. Building ML models using Apache Hadoop and Spark

Inhaltsverzeichnis

Frontmatter

2017 | OriginalPaper | Buchkapitel

Chapter 1. Introduction to Machine Learning and R

Karthik Ramasubramanian, Abhishek Singh

2017 | OriginalPaper | Buchkapitel

Chapter 2. Data Preparation and Exploration

Karthik Ramasubramanian, Abhishek Singh

2017 | OriginalPaper | Buchkapitel

Chapter 3. Sampling and Resampling Techniques

Karthik Ramasubramanian, Abhishek Singh

2017 | OriginalPaper | Buchkapitel

Chapter 4. Data Visualization in R

Karthik Ramasubramanian, Abhishek Singh

2017 | OriginalPaper | Buchkapitel

Chapter 5. Feature Engineering

Karthik Ramasubramanian, Abhishek Singh

2017 | OriginalPaper | Buchkapitel

Chapter 6. Machine Learning Theory and Practices

Karthik Ramasubramanian, Abhishek Singh

2017 | OriginalPaper | Buchkapitel

Chapter 7. Machine Learning Model Evaluation

Karthik Ramasubramanian, Abhishek Singh

2017 | OriginalPaper | Buchkapitel

Chapter 8. Model Performance Improvement

Karthik Ramasubramanian, Abhishek Singh

2017 | OriginalPaper | Buchkapitel

Chapter 9. Scalable Machine Learning and Related Technologies

Karthik Ramasubramanian, Abhishek Singh

Backmatter

Weitere Informationen

Premium Partner

Neuer Inhalt

BranchenIndex Online

Die B2B-Firmensuche für Industrie und Wirtschaft: Kostenfrei in Firmenprofilen nach Lieferanten, Herstellern, Dienstleistern und Händlern recherchieren.

Whitepaper

- ANZEIGE -

Product Lifecycle Management im Konzernumfeld – Herausforderungen, Lösungsansätze und Handlungsempfehlungen

Für produzierende Unternehmen hat sich Product Lifecycle Management in den letzten Jahrzehnten in wachsendem Maße zu einem strategisch wichtigen Ansatz entwickelt. Forciert durch steigende Effektivitäts- und Effizienzanforderungen stellen viele Unternehmen ihre Product Lifecycle Management-Prozesse und -Informationssysteme auf den Prüfstand. Der vorliegende Beitrag beschreibt entlang eines etablierten Analyseframeworks Herausforderungen und Lösungsansätze im Product Lifecycle Management im Konzernumfeld.
Jetzt gratis downloaden!

Bildnachweise