Topics:

1. Developing Mindset: How to start thinking to become a good data scientist or a good AI engineer

2. Fundamentals of AI/ML/DS:

   1. Types of Learning

   2. Understanding the Learning Framework

   3. Role of Mathematics in AI/ML/DS

   4. Math Foundation

      1. Scalars, Vectors, Vector Spaces and Subspaces, Matrices, Linear Transformation

      2. Probability and Statistics

      3. Mathematical Optimisations

   5. Basic AI/ML/DS algorithms

   6. Data Preprocessing Techniques and Statistical Measures

   7. Output Interpretation

3. AI Enabled Development Workspace

   1. Version Control

   2. Setting Up IDE for Power Developers

   3. AI Assisted Coding Best Practices

4. Data Analysis and Visualisation Techniques

5. Natural Language Processing

   1. Overview of NLP History

   2. Token; Tokenisation; Embedding; Embedding Space

   3. Neural Networks and Deep Neural Network Architectures

   4. How Language Processing Works

   5. Different Types of Language Models

   6. How Large Language Models (LLM) Works

   7. Identifying Which Type of Models is Best For Given Task

   8. Online and Offline LLM Tools

6. Agentic AI

   1. What is an AI Agent

   2. Prompt Engineering and Model Fine-tuning

   3. Running Different Size LLMs Locally

   4. Professionally using Agentic Frameworks

   5. LangChain and LangGraph Core Concepts

   6. Retrieval Augmented Generation (RAG)

   7. Integration with external APIs and tools

   8. Model Context Protocol (MCP)

   9. Deploying AI agents in cloud

7. Computer Vision

   1. Overview of Computer Vision

   2. Introduction to Convolutional Neural Networks (CNN)

   3. Applications of Computer Vision

   4. Off-The-Shelf Models for Various Tasks