Course Content

Unit 1: INTRODUCTION TO AI

Foundational concepts of AI

• Session: What is Intelligence?
• Session: Decision Making.
  • How do you make decisions?
  • Make your choices!

Basics of AI: Let's Get Started

• Session: Introduction to AI and related terminologies.
  • Introducing AI, ML & DL.
  • Introduction to AI Domains (Data, CV & NLP)
• Session: Applications of AI – A look at Real-life AI implementations
• Session: AI Ethics

Unit 2: AI PROJECT CYCLE

Introduction

• Session: Introduction to AI Project Cycle

Problem Scoping

• Session: Understanding Problem Scoping & Sustainable Development Goals

Data Acquisition

• Session: Simplifying Data Acquisition

Data Exploration

• Session: Visualising Data

Modeling

• Session: Introduction to modeling
  • Introduction to Rule-Based & Learning Based AI Approaches
  • Introduction to Supervised Unsupervised & Reinforcement Learning Models
  • Neural Networks

Evaluation

• Session: Evaluating the idea!

Unit 3: ADVANCE PYTHON (To be assessed through Practicals)

• Session: Jupyter Notebook/or any other platform
• Session: Introduction to Python
• Session: Python Basics

Unit 4: DATA SCIENCES (To be assessed through Practicals)

Introduction

• Session: Introduction to Data Science
• Session: Applications of Data Science
• Session: Revisiting AI Project Cycle

Concepts of Data Sciences

• Session: Python for Data Sciences
• Session: Statistical Learning & Data Visualisation

K-nearest neighbor model (Optional)

• Activity: Personality Prediction (Optional)
• Session: Understanding K-nearest neighbor model (Optional)

Unit 5: COMPUTER VISION (To be assessed through Practicals)

Introduction

• Session: Introduction to Computer Vision
• Session: Applications of CV

Concepts of Computer Vision

• Session & Activity: Understanding CV Concepts
  • Pixels
  • How do computers see images?
  • Image Features

OpenCV

• Session: Introduction to OpenCV
• Hands-on: Image Processing

Convolution Operator (Optional)

• Session: Understanding Convolution operator (Optional)
• Activity: Convolution Operator (Optional)

Convolution Neural Network (Optional)

• Session: Introduction to CNN (Optional)
• Session: Understanding CNN (Optional)
• Activity: Testing CNN (Optional)
  • Kernel
  • Layers of CNN

Unit 6: NATURAL LANGUAGE PROCESSING

Introduction

• Session: Introduction to Natural Language Processing
• Session: NLP Applications
• Session: Revisiting AI Project Cycle

Chatbots

• Activity: Introduction to Chatbots

Language Differences

• Session: Human Language VS Computer Language

Concepts of Natural Language Processing

• Hands-on: Text processing
  • Data Processing
  • Bag of Words
  • TFIDF (Optional)
  • NLTK

Unit 7: EVALUATION

Introduction

• Session: Introduction to Model Evaluation

Confusion Matrix

• Session & Activity: Confusion Matrix

Evaluation Score Calculation

• Session: Understanding Accuracy, Precision, Recall & F1 Score

Activity: Practice Evaluation