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Introduction to Machine Learning with ENCOG 3

by Abhishek Kumar

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

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.

Table of contents

About the author

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. He is also a public speaker and has delivered talks in top data conferences across the globe including Strata Data, AI conference, ODSC, and Fifth Elephant. His focus area is machine learning and deep learning at scale and is also a recipient of the... more

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