History & Background

Basic Syntax

Data Types: Lists, Tuples, Dictionaries etc

Basic Operators

Control Flow, Functions, File I/O & Exceptions

Classes & Libraries

Functional Programming

Tools Covered: Python IDLE (for writing and running Python programs)

URLLIB, LOGGING, REQUESTS

FLASK (Buildings Simple websites)

Serving models with Flask Restful

FASTAPI, PYDANTIC, SQLAlchemy

Serving models with FASTAPI

OS, SYS

JSON, CSV

Linux Command

Docker & Docker-Compose

NumPy (package for fast numerical calculation)

Pandas (Tabular data manipulation package)

Plotting and Charting with Matplotlib,Seaborn ang plotly (Data Visualization package)

Tools covered: NumPy, Pandas, Matplotlib, Seaborn, plotly

Basics: Scalars, vectors, matrices, notations

Vector Operations: Addition, subtraction, scalar multiplication, dot & cross product, norms

Matrix Operations: Addition, multiplication, transpose, inverse

Special Matrices: Identity, diagonal, symmetric, orthogonal, determinants

Solving Linear Equations: Row echelon forms, Gaussian elimination

Descriptive Statistics: Types of data, measures of central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), skewness, kurtosis

Probability Theory: Basic probability, conditional probability, Bayes’ theorem, discrete & continuous distributions

Inferential Statistics: Population vs sample, sampling methods, central limit theorem, hypothesis testing (null/alternative, p-value, type I/II errors)

Statistical Tests & Confidence Intervals: t-tests (one-sample, two-sample), chi-square test, ANOVA, confidence intervals for means & proportions

Correlation & Regression: Correlation coefficient, simple & multiple linear regression, interpretation of results

Learn to analyze, visualize, and summarize data to uncover patterns, insights, and relationships before modeling.

Machine Learning Overview

Types of Machine Learning

Predictive modelling with scikit-learn (Machine Learning package)

Basics of building Machine Learning Models

Tools Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn.

Linear Regression

Neural Network

K-Nearest Neighbors: Classification and Regression

Linear Regression: Least-Squares, Ridge

Lasso, and Polynomial Regression

Logistic Regression, Support Vector Machines, Decision Trees

Neural Networks, Naive Bayes

Ensemble Modelling, Random Forest, Gradient Boosted Decision Trees

Tools Covered: Python, NumPy, Pandas, Matplotlib, Seaborn, and scikit-learn

Model Evaluation & Selection

Overfitting and Underfitting

Cross-Validation

Confusion Matrices & Advanced Evaluation

Precision-recall and Receiver Operating Characteristic (ROC) curves

Tools Covered: Python, scikit-learn, Matplotlib, and Seaborn

Kernel Density Estimation (KDE)

Dimensionality Reduction – Principal Component Analysis (PCA) and Manifold Learning

Clustering – KMeans, Hierarchical and Density-Based Spatial Clustering of Applications with Noise (DBSCAN)

Tools Covered: Python, scikit-learn, Matplotlib, and Seaborn

Deploy your machine learning models seamlessly through a web API using Flask for efficient and scalable real-time predictions

Tools Covered: Python, Flask, scikit-learn

Programming with Tensorflow/Keras and Pytorch

Deep Learning Architectures: Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) Networks, Autoencoders

Transfer Learning and Fine Tuning

Generative AI algorithms: Generative Adversarial Networks (GANs), Transformers, Diffusion models,Large Language Models (LLMs)

Tools Covered: Python, TensorFlow/Keras

Introduction to Natural Language Processing (NLP)

NLP with transformer package (Text Classification, Summarisation, Paraphrasing, Translation,Information Extraction)

Tools Covered: Python, Hugging Face Transformers, Natural Language Toolkit (NLTK) (optional)

Introduction to Image and Video Processing with OpenCV

Computer vision with deep neural networks: Image Classification, Object Detection, Segmentation & Image Generation

Computer vision with Yolo and Detectron

Tools Covered: Python, OpenCV, TensorFlow/Keras, PyTorch, YOLO, Detectron2

Introduction to Generative AI and Popular LLMs: Definition, history, key characteristics, and applications; popular LLMs (GPT, BERT, T5, LLaMA, Phi)

Fundamentals of LLMs: Transformer architecture and attention mechanism; pre-training and fine-tuning; ethics in Generative AI

Working with LLMs: Hugging Face setup and usage; NLP tasks (text generation, classification, summarization); evaluation metrics (Perplexity, BLEU, Precision, Recall, F1 Score)

LangChain & Application Development: Core components (Chains, Agents, Memory, Prompts); chatbot development and integration with LLMs

Vector Databases & Retrieval-Augmented Generation (RAG): Vector databases for storing and searching embeddings; RAG for combining retrieved knowledge with LLM generation to produce accurate, context-aware responses

Transfer Learning & Fine-Tuning: Transfer learning concepts; fine-tuning LLMs on domain-specific data; LoRA (Low-Rank Adaptation) for efficient fine-tuning

Agentic AI & Model Context Protocol(MCP): Understanding agentic models; multi-step reasoning and planning

Advanced Techniques: RLHF (Reinforcement Learning from Human Feedback); Chain-of-Thought (CoT) prompting; agentic models and decision-making

Open-Source Models & Ollama: Setup and usage of Ollama; comparison with commercial LLMs; demo

Chatbot Development & Best Practices: APIs, context management, personalization; evaluating chatbots using BLEU, Perplexity, F1 Score

Future Directions: Multi-modal models (text + image + video); few-shot and zero-shot learning; scalability and real-time processing

Tools Covered: Python, Hugging Face Transformers, LangChain, OpenAI API, Ollama, PyTorch, TensorFlow, Vector Databases, evaluation tools

Learn techniques to design effective prompts for AI models to generate accurate, relevant, and optimized responses.

IBM Watson

Microsoft Azure

Amazon Al

Google Cloud ML

Introduction to Reinforcement Learning

Markov Decision Process (MDP)

Elements of Reinforcement Learning: agent,environment, policy, reward signal, value function

Traditional RL methods

Deep Reinforcement Learning

Option 1 – Guided Project – AI & Data Science (Mentor-Led, Production-Style Projects) Learners will apply their AI and Data Science skills to end-to-end, production-grade projects under mentor supervision, resulting in an industry-ready portfolio of deployable AI applications.

Option 2 – Cloud DevOps & Deployment (cloud infra, CI/CD, MLOps deployment practices)

Introduction & Theory

Cloud Infrastructure

CI/CD

MLOps Deployment (End-to-End ML Lifecycle)

Option 3 – Introduction to Big Data with Databricks and Apache Spark

Introduction and Fundamentals

Databricks Notebook

Data Handling and SQL in Databricks

Data Analysis and Visualization

Machine Learning with Databricks