MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 13 lectures (1h 19m) | Size: 132 MB
Machine learning algorithm (ANN) - simplified.
See the use cases with R to understand the application
Basics of Artificial Neural Network (ANN)
Terms and defintions associated with ANN
How does ANN work
How to solve binary classification problem using artificial neural network in R
How to solve multi level classification problem using artificial neural network in R
Data treatment guideline for using ANN
Pros and Cons of Neural Network
Should know basic R programming
Basic computer skills
Ability to locate resource supplied with this course on Udemy platform
This course aims to simplify concepts of Artificial Neural Network (ANN). ANN mimics the process of thinking. Using it's inherent structure, ANN can solve multitude of problem like binary classifications problem, multi level classification problem etc.
The course is unique in terms of simplicity and it's step by step approach of presenting the concepts and application of neural network.
The course has two section
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Section 1 : Theory of artificial neural network
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what is neural network
Terms associated with neural network
What is node
What is bias
What is hidden layer / input layer / output layer
What is activation function
What is a feed forward model
How does a Neural Network algorithm work?
What is case / batch updating
What is weight and bias updation
Intuitive understanding of functioning of neural network
Stopping criteria
What decisions an analyst need to take to optimize the neural network?
Data Pre processing required to apply ANN
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Section 2 : Application of artificial neural network
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Application of ANN for binary outcome
Application of ANN for multi level outcome
Assignment of ANN - learn by doing
Analytics professionals, who are trying to learn artificial neural network
Students, who are trying to make their career into analytics domain
Finance professionals, who want to get first hand exposure of artificial neural network concepts
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