Book Image

Practical Machine Learning

By : Sunila Gollapudi
Book Image

Practical Machine Learning

By: Sunila Gollapudi

Overview of this book

This book explores an extensive range of machine learning techniques uncovering hidden tricks and tips for several types of data using practical and real-world examples. While machine learning can be highly theoretical, this book offers a refreshing hands-on approach without losing sight of the underlying principles. Inside, a full exploration of the various algorithms gives you high-quality guidance so you can begin to see just how effective machine learning is at tackling contemporary challenges of big data This is the only book you need to implement a whole suite of open source tools, frameworks, and languages in machine learning. We will cover the leading data science languages, Python and R, and the underrated but powerful Julia, as well as a range of other big data platforms including Spark, Hadoop, and Mahout. Practical Machine Learning is an essential resource for the modern data scientists who want to get to grips with its real-world application. With this book, you will not only learn the fundamentals of machine learning but dive deep into the complexities of real world data before moving on to using Hadoop and its wider ecosystem of tools to process and manage your structured and unstructured data. You will explore different machine learning techniques for both supervised and unsupervised learning; from decision trees to Naïve Bayes classifiers and linear and clustering methods, you will learn strategies for a truly advanced approach to the statistical analysis of data. The book also explores the cutting-edge advancements in machine learning, with worked examples and guidance on deep learning and reinforcement learning, providing you with practical demonstrations and samples that help take the theory–and mystery–out of even the most advanced machine learning methodologies.
Table of Contents (23 chapters)
Practical Machine Learning
About the Author
About the Reviewers

About the Reviewers

Rahul Agrawal is a Principal Research Manager at Bing Sponsored Search in Microsoft India, where he heads a team of applied scientists solving problems in the domain of query understanding, ad matching, and large-scale data mining in real time. His research interests include large-scale text mining, recommender systems, deep neural networks, and social network analysis. Prior to Microsoft, he worked with Yahoo! Research, where he worked in building click prediction models for display advertising. He is a post graduate from Indian Institute of Science and has 13 years of experience in Machine learning and massive scale data mining.

Rahul Jain is a big data / search consultant from Hyderabad, India, where he helps organizations in scaling their big data / search applications. He has 8 years of experience in the development of Java- and J2EE-based distributed systems with 3 years of experience in working with big data technologies (Apache Hadoop / Spark), NoSQL(MongoDB, HBase, and Cassandra), and Search / IR systems (Lucene, Solr, or Elasticsearch). In his previous assignments, he was associated with IVY Comptech as an architect where he worked on implementation of big data solutions using Kafka, Spark, and Solr. Prior to that, he worked with Aricent Technologies and Wipro Technologies Ltd, Bangalore, on the development of multiple products.

He runs one of the top technology meet-ups in Hyderabad—Big Data Hyderabad Meetup—that focuses on big data and its ecosystem. He is a frequent speaker and had given several talks on multiple topics in big data/search domain at various meet-ups/conferences in India and abroad. In his free time, he enjoys meeting new people and learning new skills.

Ryota Kamoshida is the maintainer of Python library MALSS ( and now works as a researcher in computer science at a Japanese company.

Ravi Teja Kankanala is a Machine learning expert and loves making sense of large amount of data and predicts trends through advanced algorithms. At Xlabs, he leads all research and data product development efforts, addressing HealthCare and Market Research Domain. Prior to that, he developed data science product for various use cases in telecom sector at Ericsson R&D. Ravi did his BTech in computer science from IIT Madras.

Dr. Jinfeng Yi is a research staff Member at IBM's Thomas J. Watson Research Center, concentrating on data analytics for complex real-world applications. His research interests lie in Machine learning and its application to various domains, including recommender system, crowdsourcing, social computing, and spatio-temporal analysis. Jinfeng is particularly interested in developing theoretically principled and practically efficient algorithms for learning from massive datasets. He has published over 15 papers in top Machine learning and data mining venues, such as ICML, NIPS, KDD, AAAI, and ICDM. He also holds multiple US and international patents related to large-scale data management, electronic discovery, spatial-temporal analysis, and privacy preserved data sharing.