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Machine Learning: A Hands-On, Project-Based Introduction to Machine Learning for Absolute Beginners: Mastering Engineering ML Systems using Scikit-Learn and TensorFlo

Machine Learning: A Hands-On, Project-Based Introduction to Machine Learning for Absolute Beginners: Mastering Engineering ML Systems using Scikit-Learn and TensorFlo

          
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About the Book

Can Machines Really Learn? Machine learning (ML) is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning has become an essential pillar of IT in all aspects, even though it has been hidden in the recent past. We are increasingly being surrounded by several machine learning-based apps across a broad spectrum of industries. From search engines to anti-spam filters to credit card fraud detection systems, list of machine learning applications is ever-expanding in scope and applications. The goal of this book is to provide you with a hands-on, project-based overview of machine learning systems and how they are applied over a vast spectrum of applications that underpins AI technology from Absolute Beginners to Experts. This book is a fast-paced, thorough introduction to Machine Learning that will have you writing programs, solving problems, and making things that work in no time. This book presents algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

  • Supervised and Unsupervised learning methods
  • Artificial Neural Networks
  • Hands-on projects based on Real-world applications
  • Bayesian learning method
  • Reinforcement learning
  • And much more
By the end of this book, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning. Learning Outcomes: By the end of this book, you will be able to:
  • Identify potential applications of machine learning in practice
  • Describe the core differences in analyses enabled by regression, classification, and clustering
  • Select the appropriate machine learning task for a potential application
  • Apply regression, classification, and clustering
  • Represent your data as features to serve as input to machine learning models
  • Utilize a dataset to fit a model to analyze new data
  • Build an end-to-end application that uses machine learning at its core
  • Implement these techniques in Python
If you've been thinking seriously about digging into ML, this book will get you up to speed. Why wait any longer?


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Product Details
  • ISBN-13: 9781978373884
  • Publisher: Createspace Independent Publishing Platform
  • Publisher Imprint: Createspace Independent Publishing Platform
  • Height: 229 mm
  • No of Pages: 178
  • Spine Width: 10 mm
  • Width: 152 mm
  • ISBN-10: 1978373880
  • Publisher Date: 18 Oct 2017
  • Binding: Paperback
  • Language: English
  • Returnable: N
  • Weight: 245 gr


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