2013
Pluralsight
Abhishek Kumar
2h 16m
English
This course is focused on implementation and applications of various machine learning methods.
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
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Introduction
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Why Machine Learning ?
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Why This Course ?
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Key Concepts
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Spam Filtering
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Course Structure
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This module is unavailableApplications of Machine Learning
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Introduction
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Internet
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Financial Sector
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e-Commerce
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Process Industry
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Others
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Summary
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This module is unavailableMachine Learning Tasks
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Introduction
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Classification
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Regression
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Clustering
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Summary
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This module is unavailableIntroduction to Neural Networks
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Introduction
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Outline
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Human Neuron vs Artificial Neuron
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Neuron Computation
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Neural Network Component : Neuron Types
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Neural Network Component : Weights
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Neural Network Component : Activation Function
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Neural Network Component : Layers
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Neural Network Computation
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Model Creation
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Model Training
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Model Validation
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Summary
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This module is unavailableIntroduction to ENCOG 3
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Introduction
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Outline
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About ENCOG
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Why ENCOG ?
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ENCOG Coverage
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ENCOG Resources
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Summary
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This module is unavailableNeural Network Components in ENCOG for .NET
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Introduction
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Outline
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Data
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Network
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Training
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Evaluation
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XOR Problem
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Demo : XOR problem with ENCOG 3 in C#
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This module is unavailablePropagation Training
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Introduction
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Outline
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Propagation Training
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Basic Concepts
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Back Propagation Algorithm
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Manhattan Update Rule
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Quick Propagation Algorithm
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Resilient Propagation Algorithm
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Scaled Conjugate Gradient
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Levenberg Marquardt Algorithm
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Demo
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Summary
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This module is unavailableData Normalization
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Introduction
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Outline
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Field Types
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Need Of Normalization
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Normalization and De-Normalization
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Numeric Data Field Normalization
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Numeric Data Field Normalization in ENCOG
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Nominal Data Field Normalization
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ENCOG Analyst
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Summary
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This module is unavailableCase Studies (Classification and Regression Task)
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Introduction
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Outline
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Case Study 1: Classification Task
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Flow Chart 1
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Demo : Case Study 1 : Shuffle 1
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Demo : Case Study 1 : Segregate
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Demo : Case Study 1 : Normalize
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Demo : Case Study 1 : Create Network
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Demo : Case Study 1 : Train Network
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Demo : Case Study 1 : Evaluate Network
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Case Study 2: Regression Task
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Flow Chart 2
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Demo : Case Study 1 : Shuffle 2
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Demo : Case Study 2 : Segregate
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Demo : Case Study 2 : Normaliz
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Demo : Case Study 2 : Create Network
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Demo : Case Study 2 : Train Network
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Demo : Case Study 2 : Evaluate Network
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Summary
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http://pluralsight.com/training/Courses/TableOfContents/introduction-to-machine-learning-encog

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