Generative AI Roadmap

Programming Basics

Python fundamentals, variables, loops, functions, OOP.

Math for AI

Linear algebra, probability, statistics, vectors, matrices.

Checkpoint — AI Foundations

Data & Analysis

Data Handling

NumPy, Pandas, data cleaning, preprocessing.

Data Visualization

Matplotlib, Seaborn, data insights, EDA.

Checkpoint — Data Readiness

Machine Learning

ML Fundamentals

Supervised, unsupervised learning, model evaluation.

ML Algorithms

Regression, classification, clustering, decision trees.

Checkpoint — ML Basics

Deep Learning

Neural Networks

ANNs, activation functions, backpropagation.

Deep Learning Frameworks

TensorFlow, PyTorch, Keras.

Checkpoint — Deep Learning

NLP & Transformers

Natural Language Processing

Tokenization, embeddings, text preprocessing.

Transformers

Attention mechanism, encoder-decoder models.

Checkpoint — NLP Core

Generative AI

Generative Models

LLMs, GANs, VAEs, diffusion models.

Prompt Engineering

System prompts, few-shot learning, prompt chaining.

Checkpoint — GenAI Basics

Building Applications

AI Integrations

APIs, chatbots, AI agents, automation.

Deployment

Cloud platforms, model hosting, scaling.

Checkpoint — Production Ready

Practice & Mastery

Projects

Text generators, chatbots, image generators.

Research & Ethics

AI safety, bias, responsible AI usage.

Industry-Ready Generative AI Engineer