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Introduction to Machine Learning with ENCOG 3

This course is focused on implementation and applications of various machine learning methods.

Intermediate
2h 19m
(484)

Created by Abhishek Kumar

Last Updated Jul 31, 2021

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  • Course

Introduction to Machine Learning with ENCOG 3

This course is focused on implementation and applications of various machine learning methods.

Intermediate
2h 19m
(484)

Created by Abhishek Kumar

Last Updated Jul 31, 2021

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What you'll learn

This course is focused on implementation and applications of various machine learning methods. As machine learning is a very vast area, this course will be targeted more towards one of the machine learning methods which is neural networks. The course will try to make a base foundation first by explaining machine learning through some real world applications and various associated components. In this course, we'll take one of the open source machine learning framework for .NET, which is ENCOG. The course will explain how ENCOG fits into the picture for machine learning programming. Then we'll learn to create various neural network components using ENCOG and how to combine these components for real world scenarios. We'll go in detail of feed forward networks and various propagation training methodologies supported in ENCOG. We'll also talk about data preparation for neural networks using normalization process. Finally, we will take a few more case studies and will try to implement tasks of classification & regression. In the course I will also give some tips & tricks for effective & quick implementations of neural networks in real world applications.

Introduction to Machine Learning with ENCOG 3
Intermediate
2h 19m
(484)
Table of contents

About the author
Abhishek Kumar - Pluralsight course - Introduction to Machine Learning with ENCOG 3
Abhishek Kumar
11 courses 4.4 author rating 1641 ratings

Abhishek Kumar is a data science consultant, author, and Google Developers Expert (GDE) in machine learning. He holds a master’s degree from the University of California, Berkeley, and has been featured in the "Top 40 under 40 Data Scientist" list.

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