Large Scale Machine Learning with Python: Learn to build powerful machine learning models quickly and deploy large-scale predictive applications

About the Book

Learn to build powerful machine learning models quickly and deploy large-scale predictive applications Key Features [*]Design, engineer and deploy scalable machine learning solutions with the power of Python [*]Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework [*]Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Book DescriptionLarge Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and 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. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn 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 [*]Improve your work by combining the MapReduce framework with Spark [*]Build powerful ensembles at scale [*] Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machine Who this book is forThis book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.

Table of Contents:
Table of Contents

1. First Steps to Scalability
2. Scalable Learning in Scikit Learn
3. Fast learning SVM
4. Neural Networks & Deep Learning
5. Deep learning with Tensorflow
6. CART at scale
7. Unsupervised Learning at Scale
8. Distributed environments: Hadoop and Spark
9. Practical Machine Learning with Spark and Python

About the Author :
Luca Massaron is a data scientist with over a decade of experience in transforming data into high-impact, innovative artifacts, solving real-world problems, and generating value for businesses and stakeholders. He is the author of numerous bestselling books on AI, machine learning, and algorithms. Luca is also a 3x Kaggle Grandmaster who reached number 7 in the worldwide user rankings for his performance in data science competitions. Additionally, he is recognized as a Google Developer Expert (GDE) in AI, Kaggle, and the cloud. Alberto Boschetti is a data scientist with 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 challenges ranging from natural language processing (NLP) and behavioral analysis to machine learning and 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. Bastiaan Sjardin is a data scientist and founder with a background in artificial intelligence and mathematics. He has a MSc degree in cognitive science obtained at the University of Leiden together with on campus courses at Massachusetts Institute of Technology (MIT). In the past 5 years, he has worked on a wide range of data science and artificial intelligence 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 (http://www.quandbee.com/), a company providing machine learning and artificial intelligence applications at scale.