Week 1-2
Introduction to AI Basics
- Overview of AI, ML, and DL.
- Introduction to Python for AI.
- Basics of supervised and unsupervised learning.
- Simple hands-on exercises using Python libraries.
- Project: Implementing a basic AI concept in Python.
Week 3-4
Core Machine Learning Concepts
- Exploring decision trees for beginners.
- Introduction to ensemble methods.
- Understanding Support Vector Machines (SVM).
- Basic algorithms: Gradient Boosting.
- Project: Building a straightforward ML model.
Week 5-6
Introduction to Deep Learning
- Basics of neural networks.
- Simple deep learning models using TensorFlow or PyTorch.
- Overview of Convolutional Neural Networks (CNNs).
- Basic applications of RNNs.
- Project: Basic image classification using a simple neural network.
Week 7-8
Introduction to Generative AI and LLMs
- Basic concepts of Generative Adversarial Networks (GANs).
- Overview of pre-trained language models.
- Simple fine-tuning exercises.
- Basic applications of generative AI.
- Project: Simple text generation using pre-trained models
Week 9-10
Basic Deployment and Scaling
- Introduction to cloud deployment with simplified tools.
- Basics of containerization using Docker.
- Overview of basic DevOps practices.
- Basic scaling concepts.
Week 11 - Capstone + Assessments
Advanced AI and Capstone Project
- Participants will apply their accumulated knowledge and skills to tackle a comprehensive Capstone Project. This hands-on project will challenge participants to design and implement an end-to-end AI-driven solution, incorporating elements from each module of the program.
- Skills assessed: Algorithm Implementation, Programming Proficiency, Model Deployment and Integration, Cloud Computing and Deployment, Data Preprocessing and Feature Engineering, Advanced AI Technologies.
