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Your Starter Guide for Data Management, Model Training, Neural Networks, Machine Learning Algorithms

Machine Learning: For Beginners, Book 1

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Your Starter Guide for Data Management, Model Training, Neural Networks, Machine Learning Algorithms

Auteur(s): Ken Richards
Narrateur(s): Jacob Ford
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À propos de cet audio

Discover and learn all about machine learning. This audiobook covers the basic and fundamental subjects on machine learning and its application.

Are you clueless about what machine learning is about?

Have you ever wonder how a machine (Deep Blue) can beat Garry Kasparov, a reigning world champion in chess? Do you know that a handful of Fortune 500 companies are using machine learning in new creative ways?

Machine learning has been around for more than a decade and yet its usage is still in its infancy and innovating in an interesting way. Companies are investing heavily in machine learning and are reaping generous rewards. Some of their products have become an essential part of our daily activities such as the navigation systems in our vehicles.

In this audiobook, you will learn about the basics of machine learning and its applications, which is the fundamental block of artificial intelligence.

  • Learn about the three types of machine learning
  • Learn about the machine learning algorithms such as KNN
  • Discover the machine learning tools that you can use
  • Learn about data acquisition and data management
  • Learn about model training, validation, and application
  • Learn how neural networks work and usage
  • And much more

Even if you feel that you may know some of the subjects discussed here, be open and give this book a chance. You may find a new perspective, learn something new, and pick up some valuable tools. It's written in an informative way for easy listening.

©2017 Ken Richards (P)2017 Ken Richards
Ingénierie Apprentissage automatique Science des données Informatique Intelligence artificielle Programmation Mathématiques
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don't buy this book, listen to this free podcast instead https://pca.st/AYA3 it goes into more detail and avoids the formulas and stuff

not worth it

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