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Artificial Neural Networks tutorial – theory & applications

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Artificial Neural Networks tutorial – theory & applications, available at $19.99, has an average rating of 4.45, with 13 lectures, 1 quizzes, based on 26 reviews, and has 125 subscribers.

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You will learn about 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 This course is ideal for individuals who are Analytics professionals, who are trying to learn artificial neural network or Students, who are trying to make their career into analytics domain or Finance professionals, who want to get first hand exposure of artificial neural network concepts It is particularly useful for Analytics professionals, who are trying to learn artificial neural network or Students, who are trying to make their career into analytics domain or Finance professionals, who want to get first hand exposure of artificial neural network concepts.

Enroll now: Artificial Neural Networks tutorial – theory & applications

Summary

Title: Artificial Neural Networks tutorial – theory & applications

Price: $19.99

Average Rating: 4.45

Number of Lectures: 13

Number of Quizzes: 1

Number of Published Lectures: 13

Number of Published Quizzes: 1

Number of Curriculum Items: 14

Number of Published Curriculum Objects: 14

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

  • 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

Target Audiences

  • 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

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

Course Curriculum

Chapter 1: Introduction to Neural Network

Lecture 1: What will you learn in this course?

Lecture 2: How to study this course?

Lecture 3: What is neural network? Motivation behind neural network

Lecture 4: Terms Associated with Neural Network

Lecture 5: How does a Neural Network algorithm work?

Lecture 6: Data Preprocessing required to apply ANN

Chapter 2: Application of Neural Network using R

Lecture 1: Demo of neural network application on cheese data – can it predict the outcome?

Lecture 2: Demo of neural network application on multi class dependent variable?

Lecture 3: Pros n Cons of Neural Network Models

Lecture 4: About assignment – solve a binary outcome case using ANN

Lecture 5: Assignment tasks

Lecture 6: Sample solution of assignment

Lecture 7: Closing Note

Instructors

Rating Distribution

  • 1 stars: 2 votes
  • 2 stars: 1 votes
  • 3 stars: 3 votes
  • 4 stars: 10 votes
  • 5 stars: 10 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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