Key Features
Book Description
What you will learn
- Build dynamic workflows for scientific computing
- Leverage open source libraries to extract patterns from time series
- Write your own classification, clustering, or evolutionary algorithm
- Perform relative performance tuning and evaluation of Spark
- Master probabilistic models for sequential data
- Experiment with advanced techniques such as regularization and kernelization
- Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters
- Apply key learning strategies to a technical analysis of financial markets
Who this book is for
Table of Contents
- Getting Started
- Hello World
- Data preprocessing
- Unsupervised Learning
- Naïve Bayes Classifiers
- Regression and Regularization
- Sequential Data Models HMM and CRF
- Kernel models and Support Vector Machines
- Artificial Neural Networks
- Genetic Algorithms
- Reinforcement learning
- Scalable Frameworks
- Appendix: Appendix
- Appendix B
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