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Real-World Machine Learning: Practical Applications & Use Cases for AI & Data Science | Business, Healthcare & Finance Solutions
Real-World Machine Learning: Practical Applications & Use Cases for AI & Data Science | Business, Healthcare & Finance Solutions

Real-World Machine Learning: Practical Applications & Use Cases for AI & Data Science | Business, Healthcare & Finance Solutions

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Product Description

Summary Real-World Machine Learning is a practical guide designed to teach working developers the art of ML project execution. Without overdosing you on academic theory and complex mathematics, it introduces the day-to-day practice of machine learning, preparing you to successfully build and deploy powerful ML systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand. About the Book Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems. What's Inside Predicting future behaviorPerformance evaluation and optimizationAnalyzing sentiment and making recommendations About the Reader No prior machine learning experience assumed. Readers should know Python. About the Authors Henrik Brink, Joseph Richards and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning. Table of Contents PART 1: THE MACHINE-LEARNING WORKFLOW What is machine learning?Real-world dataModeling and predictionModel evaluation and optimizationBasic feature engineering PART 2: PRACTICAL APPLICATION Example: NYC taxi dataAdvanced feature engineeringAdvanced NLP example: movie review sentimentScaling machine-learning workflowsExample: digital display advertising

Customer Reviews

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While this is a practitioner oriented book, it will be useful to anyone who is learning about machine learning. This book does a very good job of illustrating the "how" of machine learning--- the practical steps of organizing data sets, and the various steps involved in building up and evaluating models.The book is very well organized, and well presented. The authors crafted a good systematic approach for explaining and illustrating through example the various steps of the process of using ML methods to create models for classification and prediction. They book has a number of good examples.No mathematical background is required for reading this book. Obviously it helps if the reader has some familiarity with the various types of statistical models used in ML. Even if that is not the case, the book is a good starting point for bridging between the "what" of ML and the "how" of ML. For those who want to try things in a hands-on fashion, they give a number of code examples, with sufficient brief annotations so you know what the blocks are code are being used for.