Artificial Neural Networks tutorial - theory & applications
4.8 (3 ratings)
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Artificial Neural Networks tutorial - theory & applications

Machine learning algorithm (ANN) - simplified. See the use cases with R to understand the application
4.8 (3 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
9 students enrolled
Last updated 8/2017
English
Price: $20
30-Day Money-Back Guarantee
Includes:
  • 1 hour on-demand video
  • 6 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • 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
View Curriculum
Requirements
  • Should know basic R programming
  • Basic computer skills
  • Ability to locate resource supplied with this course on Udemy platform
Description

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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  1. what is neural network
  2. Terms associated with neural network
    1. What is node
    2. What is bias
    3. What is hidden layer / input layer / output layer 
    4. What is activation function
    5. What is a feed forward model
  3. How does a Neural Network algorithm work?
    1. What is case / batch updating
    2. What is weight and bias updation 
    3. Intuitive understanding of functioning of neural network 
    4. Stopping criteria 
    5. What decisions an analyst need to take to optimize the neural network?
  4. Data Pre processing required to apply ANN

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Section 2 : Application of artificial neural network

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  1. Application of ANN for binary outcome
  2. Application of ANN for multi level outcome
  3. Assignment of ANN - learn by doing
Who is the target audience?
  • 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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Curriculum For This Course
13 Lectures
01:09:37
+
Introduction to Neural Network
6 Lectures 32:15

How to study this course?
01:23

Inspiration and linkage with animal thinking process
What is neural network? Motivation behind neural network
07:03

  • What is node
  • What is bias
  • What is hidden layer / input layer / output layer
  • What is activation function
  • What is a feed forward model
Terms Associated with Neural Network
07:12

Understand

  1. What is case / batch updating
  2. What is weight and bias updation
  3. Intuitive understanding of functioning of neural network
  4. Stopping criteria
  5. What decisions an analyst need to take to optimize the neural network?
Preview 09:58

High level discussion on how to pre process the data for neural network
Data Preprocessing required to apply ANN
05:24
+
Application of Neural Network using R
7 Lectures 37:22

Can it predict outcome - when dependent variable has two possible outcome?

Demo of neural network application on cheese data - can it predict the outcome?
07:57

Can it predict outcome - when dependent variable has more than two possible outcome?

Demo of neural network application on multi class dependent variable?
11:42

Pros n Cons of Neural Network Models
03:57

Check your learning of ANN
10 questions


Assignment tasks
02:03

Sample solution of assignment
08:43

Closing Note
01:30
About the Instructor
Gopal Prasad Malakar
4.3 Average rating
1,594 Reviews
20,135 Students
16 Courses
Credit Card Analytics Professional - Trains on Data Mining

I am a seasoned Analytics professional with 16+ years of professional experience. I have industry experience of impactful and actionable analytics. I am a keen trainer, who believes that training is all about making users understand the concepts. If students remain confused after the training, the training is useless. I ensure that after my training, students (or partcipants) are crystal clear on how to use the learning in their business scenarios. My expertise is in Credit Card Business, Scoring (econometrics based model development), score management, loss forecasting and MS access based database application development.