Book description
Dig deep into the data with a hands-on guide to machine learning with updated examples and more!Machine Learning: Hands-On for Developers and Technical Professionals provides hands-on instruction and fully-coded working examples for the most common machine learning techniques used by developers and technical professionals. The book contains a breakdown of each ML variant, explaining how it works and how it is used within certain industries, allowing readers to incorporate the presented techniques into their own work as they follow along. A core tenant of machine learning is a strong focus on data preparation, and a full exploration of the various types of learning algorithms illustrates how the proper tools can help any developer extract information and insights from existing data. The book includes a full complement of Instructor's Materials to facilitate use in the classroom, making this resource useful for students and as a professional reference.
At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. Scientific analysis of big data requires a working knowledge of machine learning, which forms predictions based on known properties learned from training data. Machine Learning is an accessible, comprehensive guide for the non-mathematician, providing clear guidance that allows readers to:
- Learn the languages of machine learning including Hadoop, Mahout, and Weka
- Understand decision trees, Bayesian networks, and artificial neural networks
- Implement Association Rule, Real Time, and Batch learning
- Develop a strategic plan for safe, effective, and efficient machine learning
By learning to construct a system that can learn from data, readers can increase their utility across industries. Machine learning sits at the core of deep dive data analysis and visualization, which is increasingly in demand as companies discover the goldmine hiding in their existing data. For the tech professional involved in data science, Machine Learning: Hands-On for Developers and Technical Professionals provides the skills and techniques required to dig deeper.
Table of contents
- Cover
- Introduction
- CHAPTER 1: What Is Machine Learning?
- CHAPTER 2: Planning for Machine Learning
- CHAPTER 3: Data Acquisition Techniques
- CHAPTER 4: Statistics, Linear Regression, and Randomness
- CHAPTER 5: Working with Decision Trees
- CHAPTER 6: Clustering
- CHAPTER 7: Association Rules Learning
- CHAPTER 8: Support Vector Machines
-
CHAPTER 9: Artificial Neural Networks
- What Is a Neural Network?
- Artificial Neural Network Uses
- Trusting the Black Box
- Breaking Down the Artificial Neural Network
- Data Preparation for Artificial Neural Networks
- Artificial Neural Networks with Weka
- Implementing a Neural Network in Java
- Developing Neural Networks with DeepLearning4J
- Summary
- CHAPTER 10: Machine Learning with Text Documents
- CHAPTER 11: Machine Learning with Images
-
CHAPTER 12: Machine Learning Streaming with Kafka
- What You Will Learn in This Chapter
- From Machine Learning to Machine Learning Engineer
- From Batch Processing to Streaming Data Processing
- What Is Kafka?
- Installing Kafka
- Topics Management
- Kafka Tool UI
- Writing Your Own Producers and Consumers
- Building a Streaming Machine Learning System
- Kafka Topics
- Kafka Connect
- The REST API Microservice
- Processing Commands and Events
- Making Predictions
- Running the Project
- Summary
- CHAPTER 13: Apache Spark
- CHAPTER 14: Machine Learning with R
- APPENDIX A: Kafka Quick Start
- APPENDIX B: The Twitter API Developer Application Configuration
- APPENDIX C: Useful Unix Commands
- APPENDIX D: Further Reading
- Index
- End User License Agreement
Product information
- Title: Machine Learning, 2nd Edition
- Author(s):
- Release date: March 2020
- Publisher(s): Wiley
- ISBN: 9781119642145
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