Introduction to Machine Learning with ENCOG 3

This course is focused on implementation and applications of various machine learning methods.
Course info
Rating
(481)
Level
Intermediate
Updated
Jul 22, 2013
Duration
2h 19m
Table of contents
Introduction to Machine Learning
Applications of Machine Learning
Machine Learning Tasks
Introduction to Neural Networks
Introduction to ENCOG 3
Neural Network Components in ENCOG for .NET
Propagation Training
Data Normalization
Case Studies (Classification and Regression Task)
Description
Course info
Rating
(481)
Level
Intermediate
Updated
Jul 22, 2013
Duration
2h 19m
Description

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.

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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Section Introduction Transcripts
Section Introduction Transcripts

Introduction to Machine Learning
Hi, this is Abhishek Kumar. Welcome to this course on Introduction to Machine Learning with ENCOG. This is the first module which is on introduction to machine learning where I'll try to give you some glimpse and background with machine learning.

Applications of Machine Learning
Hi, this is Abhishek Kumar. This is the second module of the course. This module is on applications of machine learning. In this module we will look at a few of the interesting and real-world application areas where machine learning techniques are used quite extensively.

Machine Learning Tasks
Hi, this is Abhishek Kumar, and this is the third module of the course. This module is on machine learning tasks. In this module we will learn about broad categories of tasks, which are performed by machine-learning algorithms. In the previous module we looked at various real-world applications of machine learning so if you want to categorize all applications broadly, these can be divided into three tasks. The first is classification, the second is regression, and third is clustering. Most of the applications of machine learning fall into either one or are a combination of these categories. Now we'll try to learn the basics of each of these machine-learning tasks, one by one.

Introduction to Neural Networks
Hi, this is Abhishek Kumar and this is the fourth module of the course. This module is an introduction to neural networks. In this module we will learn about one of the widely used machine-learning techniques, which is neural networks. We will learn the fundamentals of neural networks in this module. Neural networks is not the only technique in the machine-learning field. There are many techniques available. A few of them are listed here. Support vector machines, genetic programming, Bayesian statistics, decision trees, case-based reasoning, information fuzzy networks, and many more. However, this course will be focused on neural networks. Neural networks are one of the most important and vitally used techniques in the machine-learning area. We will cover other machine-learning techniques in upcoming courses.

Introduction to ENCOG 3
Hi, this is Abhishek Kumar and this is the fifth module of the course. This module is on Introduction to ENCOG 3. In this module I'll introduce you to an open-source advanced machine-learning framework that is ENCOG.

Neural Network Components in ENCOG for .NET
Hi, this is Abhishek Kumar, and this is the sixth module of the course. This module is on neural network components in ENCOG for. NET. In this module we will learn to create various neural network components, which we had discussed in the previous modules. We will use C# language to construct various neural network components with the help of ENCOG machine-learning framework.

Propagation Training
Hi, this is Abhishek Kumar, and this is the seventh module of this course. This module is on propagation training. In this module we will go into the basics of propagation training process. We will also look at various ENCOG supported propagation algorithms in this module.

Data Normalization
Hi, this is Abhishek Kumar and this is the eighth module of this course. This module is on data normalization. In the real-world scenario, you may not always get data in a format which can be directly used with neural network models. In this module we will learn about various kind of data processing that is required so that the data can be processed in such a manner so that it can be used by neural network models efficiently.

Case Studies (Classification and Regression Task)
Hi, this is Abhishek Kumar and this is the ninth module of the course. This module will be focused on two case studies of two important machine-learning tasks. We'll develop these applications from the ground up. In this module we will learn various techniques to tackle real-world machine-learning problems using ENCOG framework.