Learn to build powerful machine learning models quickly and deploy large-scale predictive applications
About This Book
- 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
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.
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
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.
Style and Approach
This efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.
Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production.
This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.
Table of Contents
Chapter 1: First Steps to Scalability
Chapter 2: Scalable Learning in Scikit-learn
Chapter 3: Fast SVM Implementations
Chapter 4: Neural Networks and Deep Learning
Chapter 5: Deep Learning with TensorFlow
Chapter 6: Classification and Regression Trees at Scale
Chapter 7: Unsupervised Learning at Scale
Chapter 8: Distributed Environments – Hadoop and Spark
Chapter 9: Practical Machine Learning with Spark
Appendix: Introduction to GPUs and Theano