Machine Learning Using R

Autor:	Karthik Ramasubramanian, Abhishek Singh
EAN:	9781484223345
eBook Format:	PDF
Sprache:	Englisch
Produktart:	eBook
Veröffentlichungsdatum:	22.12.2016
Kategorie:	Computer
Schlagworte:	Data Exploration Data Visualization Feature Engineering Machine Learning Machine Learning Models Sampling Techniques Scalable Machine Learning

42,79 €*

inkl. MwSt.

zzgl. Versandkosten

(ab 25 Euro versandkostenfrei) *außer auf ausgewälte Artikel

Versandkostenfrei

Die Verfügbarkeit wird nach ihrer Bestellung bei uns geprüft.
Bücher sind in der Regel innerhalb von 1-2 Werktagen abholbereit.

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 flow

2.Theoretical aspects of Machine Learning

3.Industry based Case-Study

4.Example based understanding of ML algorithm using R

5.Building ML models using Apache Hadoop and Spark

Karthik Ramasubramanian, works for one of the largest and fastest growing technology unicorn in India, Hike Messenger. He brings the best of Business Analytics and Data Science experience to his role at Hike Messenger. In his 7 years of research and industry experience, he has worked on cross-industry data science problems in retail, e-commerce, and technology, developing and prototyping data driven solutions. In his previous role at Snapdeal, one of the largest e-commerce retailer in India, he was leading core statistical modelling initiatives for customer growth and pricing analytics. Prior to Snapdeal, he was part of central database team, managing the data warehouses for global business applications of Reckitt Benckiser (RB). He has rich experience working with scalable machine learning solutions for industry, including sophisticated graph network and self-learning neural networks. He has a Masters in Theoretical Computer Science from PSG College of Technology, Anna University and certified big data professional. He is passionate about teaching and mentoring future data scientist through different online and public forums. He enjoys writing poems in his leisure time and an avid traveler.

Abhishek Singh, is a Data Scientist in Advanced Data Science team of Prudential Financial Inc., second largest Life Insurance Provider in US, and is based out of Ireland. He have 5 years of professional and academic experiene in Data Science, spanning across consulting,teaching and financial services. At Deloitte Advisory, he was leading Risk Analytics initiatives for top US banks in their regulatory risk, credit risk, and balance sheet modelling requirements. In his current role, he is working on scalable machine learning algorithms for Indiavidual Life Insurance business of Prudential. He have working experience in time series models and has worked with cross functional teams to implement data science solutions in enterprise infrastructure. He has been active trainer at Deloitte Professional University and had led training and development initiatives for professionals in the area of statistics, economics, financial risk and data science tools (SAS and R). He is a B.Tech. in Mathematics and Computing from Indian Institute of Technology, Guwahati and an MBA from Indian Institute of Management, Bangalore. He speaks in public events on Data Science and working with leading universities towards bringing data science skills to graduates. He have keen interest in Law and holds a Post Graduate Diploma in Cyber Law from NALSAR University. He enjoys cooking and photography during his free hours.