Machine Learning

Jason Bell 지음

Wiley 2020년 02월 14일 출간

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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

목차

Chapter 1: What is Machine Learning? Chapter 2: Planning for Machine LearningChapter 3: Data Acquisition Techniques Chapter 4: Revisiting Basic Statistics and Linear Regression Chapter 5: Working with Decision Trees Chapter 6: Clustering Chapter 7: Association Rules LearningChapter 8: Support Vector Machines Chapter 9: Artifical Neural Networks and Deep Learning Chapter 10: Machine Learning from Text Documents Chapter 11: Machine Learning from Image Information Chapter 12: Machine Learning from Streaming Data with Apache Kafka Chapter 13: Apache Spark and MLLib Chapter 14: Maching Learing with R Appx A: A Kafka Quick StartAppx B: Spark 1.x Quick StartAppx C: Useful Unix Commands Appx D: Further Reading

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저자(글) Jason Bell

JASON BELL has worked in software development for over thirty years, now he focuses on large volume data solutions and helping retail and finance customers gain insight from that data with machine learning. He is also an active committee member for several international technology conferences.