Machine Learning: A Hands-On, Project-Based Introduction to Machine Learning for Absolute Beginners: Mastering Engineering ML Systems using Scikit-Learn and TensorFlow – Gabriel Rhys, Tech Time Media Publications [ePub & Kindle] [English]


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

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