Large Scale Machine Learning with Python

Key Features

• This practical hands-on tutorial focuses on design, engineering, and scalable solutions in machine learning, using cutting edge techniques, tools, and solutions
• The book uses popular languages and tools such as Python, Hadoop, and Spark
• Through this book, you can learn to develop high-value applications with personalized recommendations to perform machine learning engineering at scale, and build state-of-the-art models

Book Description

Data scientists have to manage and maintain complex data projects. Finding algorithms and designing and building platforms that deal with large sets of data is a growing need.

With the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy.

First, we start with the family of machine learning algorithms that are considered scalable. With this family of algorithms, we lead you through three levels of scalability. The first level is all about speeding up algorithms that can be used on a desktop computer. We will provide tips on parallelization and memory allocation. The second level is the newer algorithms that are specifically designed for scalability and can handle bigger files. The third level is about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

What you will learn

• Apply the most scalable machine learning algorithms
• Work with modern state-of-the-art large-scale machine learning techniques
• Increase predictive accuracy with deep learning and scalable data-handling techniques
• Work with a map reduce framework in Spark
• Apply effective machine learning algorithms with Spark and Hadoop
• Build powerful ensembles at scale
• Perform powerful data-manipulation operations in the command line

Bastiaan Sjardin is a data scientist and entrepreneur with a background in artificial intelligence and mathematics. He has an MSc degree in cognitive science and mathematical statistics from the University of Leiden. In the past 5 years, he has worked on a wide range of data science projects. He is a frequent community TA at Coursera in the social network analysis course from the University of Michigan and the practical machine learning course from Johns Hopkins University. His programming languages of choice are Python and R. Currently, he is the cofounder of Quandbee, a company providing machine learning and artificial intelligence applications.

Luca Massaron Luca Massaron is a data scientist and a marketing research director who is specialized in multivariate statistical analysis, machine learning, and customer insight with over a decade of experience in solving real-world problems and in generating value for stakeholders by applying reasoning, statistics, data mining, and algorithms. From being a pioneer of Web audience analysis in Italy to achieving the rank of a top ten Kaggler, he has always been very passionate about everything regarding data and its analysis and also about demonstrating the potential of data-driven knowledge discovery to both experts and non-experts. Favoring simplicity over unnecessary sophistication, he believes that a lot can be achieved in data science just by doing the essentials.

Alberto Boschetti Alberto Boschetti is a data scientist, with an expertise in signal processing and statistics. He holds a Ph.D. in telecommunication engineering and currently lives and works in London. In his work projects, he faces daily challenges that span from natural language processing (NLP) and machine learning to distributed processing. He is very passionate about his job and always tries to stay updated about the latest developments in data science technologies, attending meet-ups, conferences, and other events.