Your Definitive Guide for Machine Learning Framework, Machine Learning Model, Bayes Theorem, Decision Trees: Machine Learning: For Beginners, Book 2 (Unabridged) - Ken Richards

Your Definitive Guide for Machine Learning Framework, Machine Learning Model, Bayes Theorem, Decision Trees: Machine Learning: For Beginners, Book 2 (Unabridged)

Status: AVAILABLE

  • Release Date: 2018-01-31
  • Genre: Technologie
  • Format: PdF, ePub, Audiobook & Magazine

Preview

"Artificial Intelligence, deep learning, machine learning - whatever you're doing if you don't understand it - learn it. Because otherwise you're going to be a dinosaur within three years." (Mark Cuban)  

“The development of full artificial intelligence could spell the end of the human race.... It would take off on its own, and re-design itself at an ever increasing rate. Humans, who are limited by slow biological evolution, couldn't compete, and would be superseded.” (Stephen Hawking)

Discover and learn all about machine learning in this book. You will understand and learn more about machine learning with the proper frameworks to solve problems.

Have you seen or read about the automated driverless cars cruising the road and highways?

Don’t you want to learn more about the framework that institutions and companies are using for machine learning?

Wouldn’t you want to find out the types of real-life problems that are solved by various machine learning models?

Machine learning usage has been looming around us yet most of us are still uninformed about its applications and how it can improve daily life. 

"Machine learning is about taking the data that anyone might have - whether it's a sports franchise or an industrial manufacturer - and using algorithms to actually reason over the data and to predict outcomes that a businessperson can use to make better decisions.” (Christopher Matthews)

In this book, Machine Learning: For Beginners - Your Definitive Guide for Machine Learning Framework, Machine Learning Model, Bayes Theorem, Decision Trees:
Learn how to choose a machine-learning framework in terms of speed, scalability, and ease of use Learn how to use the right machine-learning model for different types of problems  More in-depth discussion on anomalies and anomaly detection techniques Understand association analysis concepts and rules  Explore probability theory, Bayes theorem, decision trees  Learn how to optimize your machine learning model  And much more....
Final words:

Perhaps 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 that dropped off your radar. It's written in an informative way for easy listening.