Advanced Machine Learning with ENCOG - Part 2

In this course, you will learn to implement dimensionality reduction and clustering using self-organizing maps, pattern recall and reconstruction using Hopfield networks, time series forecasting using temporal dataset, and optimization using genetic algorithm. This course will not only provide you fundamental knowledge of aforementioned topics, but also will help you to implement these applications using ENCOG machine learning framework.
Course info
Rating
(55)
Level
Advanced
Updated
Mar 24, 2014
Duration
3h 42m
Table of contents
Course Introduction
Unsupervised Competitive Networks
Unsupervised Auto-Associative Networks
Case Study: Time Series Forecasting
Optimization Using Genetic Algorithm
Course Summary
Description
Course info
Rating
(55)
Level
Advanced
Updated
Mar 24, 2014
Duration
3h 42m
Description

Finding patterns in a multidimensional dataset has always been a challenging task, but self-organizing maps can simplify this process and can help to find interesting patterns and inferences. In this course, you will learn not only the fundamentals of self-organizing maps but also the implementation in a C# application using the ENCOG machine learning framework. In this course, you will also learn to use Hopfield networks in a pattern recall and reconstruction application. This course will also provide a real world case study on time series forecasting, where you will learn to forecast future behavior using historical values. The course also covers another very important aspect of machine learning: optimization. You will learn to solve optimization problems with the help of genetic algorithms. The concepts learned in this course are applicable for developers working in any other framework in any other language.

About the author
About the author

Abhishek Kumar is a data science consultant, author and speaker.He holds Master's degree from University of California, Berkeley.His focus area is machine learning & deep learning at scale.

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