Foundation Course on Artificial Intelligence (AI)
MONTH- 1
- Module 1: Introduction to AI
- What is AI? History and Evolution
- AI vs Machine Learning vs Deep Learning vs Generative AI
- Real-world Applications of AI (healthcare, robotics, recommendation engine, etc.)
- Responsible AI
- Module 2: Python for AI
- Quick Python refresher (data types, control structures, libraries)
- NumPy, Pandas, Matplotlib for data handling and visualization
- Module 3: Data Handling and Preprocessing
- Data is the new oil- understanding importance of data & IT security, basics of data engineering
- Understanding data types: structured, unstructured
- Exploratory Data Analysis
- Data Pre-Processing and Feature engineering
- Splitting datasets (train/test/validation)
MONTH- 2
- Module 4: Basics of Machine Learning (ML)
- Supervised vs Unsupervised Learning
- Linear Regression
- Classification (KNN, Decision Trees) and Clustering (K-Means)
- Evaluation metrics (accuracy, precision, recall)
- Module 5: Neural Networks & Deep Learning
- Perceptron and neural network basics
- Concepts of Computer Vision
- Building simple neural networks
- Image classification with MNIST
MONTH-3
- Module 6: Natural Language Processing (NLP)
- NLP – the foundation of Large Language Models (LLMs)
- Text preprocessing (tokenization, stemming, stop words)
- Sentiment analysis and Chatbot basics
- Module 7: Generative AI
- Introduction to Large Language Models (LLMs)
- Prompt Engineering for LLMs
- Building Generative AI applications using LangChain, HuggingFace
- AI Agents, Understanding AI Stack and Emerging AI trends
MONTH-4 - For Advanced Course
- Module 8: Final Project Presentation
- Choose your own project along with implementation, report, and presentation