Machine Learning is defined as the ability of algorithms to work efficiently without explicit programming.

This book dives deep in analyzing the types, models, and applications of Machine Learning algorithms, using a tremendous comparative analysis approach. It sets the tone on pointing out the close relationship between the subsects of Artificial Intelligence and Machine Learning. The book also bears in mind the importance of data in Machine Learning by explaining the terminologies used about and the process of Data Mining. There is no Machine Learning without Data Mining.

But that's not all, because "Machine Learning for Beginners" takes a trip on the current upsurge of intelligent machines and their application in various sectors of the economy.

The book covers the field extensively offering recommendations on the suitable road to take for organizations and the general public. It outlines a different model for predictions in a step-by-step slant of the model, of the algorithmic process, of decision making, and solution.

Inside this book you will find:

• The importance of Artificial intelligence
• Types of ML
• Neural & Bayesian networks
• Machine Learning Models
• Support Vector Machine
• Components of Soft Computing
• Decision Trees classifiers
• ML Datasets
• Applications of Data Mining

...and many more amazing and interesting topics!

In general terms, this guide is an artifact for people looking to understand the basics of ML, not only from the beginners' viewpoint. If you have not joined the Machine Learning world yet, this is the best moment to do that.

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