Programme Parameters
Advanced Postgrad
4 Weeks (40 Hrs)
Online (e-LMS)
• Prior working knowledge of the field and comfort with core tools and quantitative reasoning.
• A laptop or desktop with a stable internet connection.
• Willingness to complete assignments and the capstone project.
About This Course
The Introduction to Neural Networks course is a free, beginner-friendly self-paced program designed to introduce learners to the basic concepts of neural networks and how they form the foundation of modern artificial intelligence and deep learning. Every participant receives a verified e-Certificate and e-Marksheet from the Deep Science & Technology Consortium.
🎯 Program Aim
The Introduction to Neural Networks course is a free, beginner-friendly self-paced program designed to introduce learners to the basic concepts of neural networks and how they form the foundation of modern artificial intelligence and deep learning.
📋 Course Objectives
1. Translate Artificial Intelligence theory into practical, reproducible analysis.
2. Build a defensible project you can showcase to supervisors, reviewers, or employers.
👥 Who Should Enroll?
• Master's and senior undergraduate students specializing in Artificial Intelligence
• R&D engineers and working professionals applying Artificial Intelligence in industry
• Academics and educators building research or teaching capacity in Artificial Intelligence
🚀 Key Learning Outcomes
• A demonstrable Artificial Intelligence project for your research or industry portfolio.
• A verified e-Certificate of competency and e-Marksheet from the Deep Science & Technology Consortium.
💎 What You'll Gain
Live & Recorded Sessions
Lifetime access to class recordingse-Certificate on Completion
Cryptographically verified credentialPost-Programme Support
Direct access to mentors & councilHands-On Experience
Notebooks, real-world code & datasetsCurriculum Outline
Introduction to Neural Networks
What are Neural Networks? • History and Evolution of Neural Networks • Neural Networks vs Machine Learning vs AI • Applications of Neural Networks
Basic Structure of Neural Networks
Understanding Neurons and Connections • Input Layer, Hidden Layers, and Output Layer • Weights, Bias, and Activation Concepts • How Data Flows Through a Network
How Neural Networks Learn
Training a Neural Network • Introduction to Forward Pass and Learning Process • Error, Loss, and Basic Optimization Idea • Simple Understanding of Model Improvement
Types of Neural Networks
Introduction to Different Neural Network Types • Feedforward Neural Networks • Basic Idea of Deep Learning • Overview of Real-World Neural Network Models
Applications and Next Steps
Neural Networks in Image, Text, and Speech Processing • AI Applications in Healthcare, Finance, and Technology • Introduction to Deep Learning Pathways • Mini Learning Activity / Concept-Based Practice
Technical Specifications
| Parameter | Requirement |
|---|---|
| Covered Tool / Platform | Neural Networks |
| Covered Tool / Platform | Deep Learning Basics |
| Covered Tool / Platform | Machine Learning Concepts |
| Covered Tool / Platform | Data Patterns |
| Covered Tool / Platform | Basic Python |
Frequently Asked Questions
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