Start your AI journey * Through Innovation

Artificial Intelligence
Fundamentals

Lecture Series (Tamil / English Technical Concepts)
Course delivered by PhD graduates working in top AI companies in USA and UK. Certificate provided.

Prerequisites

  • Basic computer usage and internet familiarity
  • Interest in AI, machine learning, and problem-solving
  • Basic coding skills preferred but not mandatory
Structured AI Roadmap Step-by-step path from AI basics to real model building.
Practical + Industry Focused Clear technical concepts with hands-on implementation.
Future-Ready Skills Deep Learning, Reinforcement Learning, and Generative AI highlights.
Delivered by AI Domain Experts Sessions led by experienced professionals from leading AI teams.
Certificate on Completion Industry-recognized completion certificate to showcase your skills.
Career Transition Friendly Designed for students, working professionals, and AI enthusiasts.

Course
Overview

10 Lectures | 20 Hours

Course Statistics

  • Total Lectures: 10
  • Total Duration: 20 hours
  • Language: Tamil (with technical English terms)
  • Level: Beginner to Intermediate
  • Price: INR 20,000

Contact Us

Email: support@abhidoc.com

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Artificial Intelligence Fundamentals * Tamil / English Technical Concepts
Course Structure * Start your AI journey

Lecture 1 - Introduction to Artificial Intelligence

What is AI, history of AI, real-world applications, human-AI interaction, recent trends and future impact.

Lecture 2 - Machine Learning Basics (Part 1)

ML fundamentals, learning from data, supervised learning, house-price prediction example.

Lecture 3 - Machine Learning Basics (Part 2)

Data/features, outputs, train vs test, learning rate, activation and loss functions, epochs, forward/backward pass.

Lecture 4 - Architecture of Neural Networks

Input, hidden/output layers, neuron behavior, and core neural network computation flow.

Lecture 5 - Training Neural Networks (Backpropagation)

Training process, backpropagation algorithm, gradient computation, and parameter updates.

Lecture 6 - Practical Issues in Neural Networks

Underfitting/overfitting, diagnostics, regularisation techniques, and architecture selection.

Lecture 7 - Common Neural Architectures

Supervised, unsupervised, reinforcement paradigms with FNN, CNN, RNN, Generative AI, and GANs.

Lecture 8 - Hands-on Example: MNIST Dataset

Digit recognition demo, MNIST training pipeline, and baseline implementation workflow.

Lecture 9 - Convolutional Neural Networks (CNN)

CNN architecture, feature extraction, image processing, and computer vision applications.

Lecture 10 Spotlight: Reinforcement Learning, Generative AI and LLMs
Duration per lecture: 60 mins session + 60 mins hands-on practical
Total Duration 20 Hours * Tamil with Technical English Terms