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 Description
Large 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 for
This 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.
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.
