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Introduction to Neural Networks

Expand your skills through the Neural Networks course to work with AI and Deep Learning tasks. Build and train artificial neural networks for industry-related problems using key calculations that underlie modern technology.

Instructor:

Dr. Kumar Muthuraman
4.57
average rating

Ratings

Beginner

Level

0.75 Hrs

Learning hours

5.1K+
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Learners

Skills you’ll Learn

About this course

This Introduction to Neural Networks course is designed to impart knowledge to work with Artificial Intelligence and Deep Learning tasks. The course begins by discussing the Artificial Neural Networks concepts and then continues explaining the biological neuron and the motivation to build ANN technique. You will learn what a neural network is by understanding perceptron concepts. Qualify in the quiz to gain the Artificial Neural Networks course completion certificate. 

 

After learning neural networks, escalate your knowledge through Artificial Intelligence courses and build a promising career in the domain. 


 

Why upskill with us?

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Course Outline

Introduction to Artificial Neural Networks

This section discusses what artificial neural networks are and their motivation. It explains how it is built and how it works to solve complex tasks. You will also understand the structure of artificial neural networks and gain a more profound knowledge of Deep Learning. 
 

Understanding Working of Perceptron

This section explains the working of perceptron with examples. It explains how summation and step functions are applied to the perceptron inputs and teaches what it does in a neural network. You will understand the mathematics behind the technique employed in Artificial Intelligence and Deep Learning tasks. 
 

Understanding Biological Neurons and Perceptrons

This section explains how biological neurons work and how it influenced the development of ANN technology. You will also know about the history of perceptrons. 
 

Our course instructor

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Dr. Kumar Muthuraman

Faculty Director - Centre for Research and Analytics <br> 20+ Years Work Experience

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50.3K+ Learners
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7 Courses
Dr. Kumar Muthuraman is the H. Timothy (Tim) Harkins Centennial Professor in the Department of Information, Risk and Operations Management and the Department of Finance at McCombs School of Business, University of Texas at Austin. He received his Ph.D from Stanford University. Dr. Muthuraman's research focuses on decision-making under uncertainty. Application areas of interest to him are quantitative finance, operations management, and health care.

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What our learners say about the course

Find out how our platform helped our learners to upskill in their career.

4.57
Course Rating
74%
17%
6%
1%
2%

Ratings & Reviews of this Course

Reviewer Profile

5.0

A well-structured course with clear explanations and useful resources. I gained valuable insights and feel more confident in the subject now.
This course was truly exceptional! The instructor's clear and concise explanations made even the most complex topics easy to understand. The course content was thoughtfully organized, and each module built upon the previous one, creating a seamless learning experience. The real-world examples and practical assignments allowed me to apply the concepts immediately, reinforcing my understanding. I especially appreciated the additional resources provided, which were incredibly helpful in deepening my knowledge. Overall, this course has greatly enhanced my skills and confidence in the subject. I highly recommend it to anyone looking to expand their expertise.
Reviewer Profile

5.0

I’m grateful for the excellent teaching and clear explanations. The course was enriching and worth the investment. Thanks for a great learning experience!
I greatly appreciated the thorough exploration of neuron models and the perceptron’s development. The instruction was clear and engaging, making complex concepts like the McCulloch-Pitts Neuron and real neuron functions accessible. The detailed explanation of binary inputs and outputs was particularly helpful. The course provided a well-structured learning experience that effectively connected foundational knowledge with practical applications. I am thankful for the opportunity to deepen my understanding and found the experience both enriching and valuable.
Reviewer Profile

5.0

I had a very nice experience with this course.
Great course. The concepts are well explained by the instructor.
Reviewer Profile

5.0

Enjoyable learning curve and curriculum
I took this course because I wanted to learn about neural networks. I found the course to be well-organized and easy to follow. The instructor was knowledgeable and kept the material interesting. Overall, I enjoyed the course and would recommend it to anyone interested in learning about neural networks.
Reviewer Profile

4.0

Engaging, informative, and hands-on experience
My experience with Great Learning's course on artificial neural networks was excellent. The course provided a solid understanding of key concepts and hands-on projects, making complex topics accessible. The instructors were knowledgeable and supportive, enhancing my skills in AI and machine learning. Overall, it prepared me well for real-world applications.
Reviewer Profile

5.0

I really enjoyed the lesson. It was easy to follow and well-structured
I really enjoyed the lesson. It was easy to follow and well-structured, making the concepts clear and understandable. The explanations were concise, and the examples helped reinforce the material. Overall, it was an engaging and informative session that kept me interested throughout.
Reviewer Profile

5.0

Simplified explanation by the instructor was great
The simplified explanation by the instructor and the scribbling during the course made it easier to digest and understand.

Introduction to Neural Networks

0.75 Learning Hours . Beginner

Why upskill with us?

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1000+ free courses
In-demand skills & tools
access time
Free life time Access

Frequently Asked Questions

What are the prerequisites to learning this Neural Networks course?

This is a beginner-level course and needs no prior knowledge to learn from it. 
 

What knowledge and skills will I gain upon completing this Artificial Neural Networks course?

You will have acquired skills to work with ANN and perceptrons. You will also be able to employ them in Deep Learning Algorithms and techniques to work with industry-oriented applications. 
 

How much does this Artificial Neural Networks course cost?

Introduction to Neural Networks is a free course. Enroll in the course today and learn artificial neural networks and perceptron concepts for free online. 
 

Is there a limit on how many times I can take this Introduction to Neural Networks course?

Great Learning Academy does not imply any restriction to the number of repetitions of this course. You can always come back and continue learning.
 

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes. You can choose to enroll in many courses of your interest simultaneously. Great Learning Academy offers free courses so that you can learn as many courses at once according to your time suitability.
 

Why choose Great Learning Academy for this Introduction to Neural Networks course?

The full-time and short-term programs provided by Great Learning, a leading provider of ed-tech services, include various topics, including Data Science, Machine Learning, Artificial Intelligence, Product Management, Digital Marketing, and Big Data Engineering. Several reasons to select Great Learning include:

  • Great Learning is a leading ed-tech company that offers full-time, online, and offline instruction in various industries.
  • The experienced educators on the Great Learning team, who are experts in their field, will accompany you on your learning journey.
  • The courses that Great Learning offers are developed considering market demands and are often updated to reflect the most recent advancements.

Who is eligible to learn from this Neural Networks course?

Anybody interested in learning artificial neural networks and understanding perceptron concepts can learn from this Neural Networks course.
 

What are the steps to enroll in the Introduction to Neural Networks course?

Enrolling in the Introduction to Neural Network course is a 2-step process. You first need to pick the course you are interested in learning, enter your E-mail ID and set a password. You can dive into the modules and start learning them online.
 

How long does it take to complete this free Artificial Neural Networks course?

Although Artificial Neural Networks is half an hour-long course. You can learn it at your leisure since the course is self-paced. 

Will I have lifetime access to this free course?

Yes. Once you enroll in this Artificial Neural Networks course, you will have free lifetime access. 
 

What are my next learning options after this Artificial Neural Networks course?

After completing this course, you can either learn the machine learning and deep learning concepts individually or register for the Artificial Intelligence Degree Program and master essential concepts and gain skills under a single roof. 

 

Why is it essential to learn Neural Networks?

Many Artificial Intelligence and Deep Learning techniques are based on neural networks, often known as Artificial Neural Networks (ANN). Deep learning uses neural networks to simulate the activity of the layers of neuron cells in the neocortex region of the brain. While artificial neural networks may include hundreds of hidden layers to help solve problems and produce outputs, regular neural networks may just have a handful. Artificial neural networks give computers the time and space required to tackle more complex problems and provide sophisticated answers.
 

Why are Artificial Neural Networks so popular?

The universal approximation theorem is the mathematical foundation for neural networks' superior classification abilities, which states that on a small subset, an artificial neural network may roughly estimate any continuous real-valued function. The quantity of neurons affects how accurate the estimate is. Because of its grip in terms of accuracy when taught with large volumes of data, deep learning fields are becoming increasingly popular. The software sector is evolving toward artificial intelligence, and every industry now relies on machine learning to give machines intelligence. 
 

What jobs demand that you learn in Neural Networks?

Every artificial intelligence, machine learning, and deep learning professional must be proficient in working with neural networks. The prevalent careers for the subject include:

  • Machine Learning Engineer
  • Data Engineer
  • Research Analyst
  • Neuroinformatics.
  • Bioinformatician.
  • Image Recognition
  • Software Engineer
  • Software Developer
  • Designer in Human-Centered Machine Learning
  • Data Scientist
  • Computational Linguist
     

After completing this Artificial Neural Networks course, will I get a certificate?

Yes. The course includes various modules for different topics in neural networks and perceptrons. Qualify in the quiz after gaining knowledge from the course to gain a course completion certificate.  
 

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