Essential concepts and skills for Python.
Essential concepts and skills for Mathematics for AI.
Essential concepts and skills for Statistics and Probability.
Essential concepts and skills for Data Structures and Algorithms.
Essential concepts and skills for Version Control with Git.
Essential concepts and skills for Data Handling with Pandas and NumPy.
Essential concepts and skills for Machine Learning Basics (Scikit-learn).
Essential concepts and skills for Model Evaluation and Validation.
Essential concepts and skills for Neural Networks Basics.
Essential concepts and skills for Convolutional Neural Networks (CNNs).
Essential concepts and skills for Recurrent Neural Networks (RNNs) and LSTMs.
Essential concepts and skills for Transformers and Attention Mechanisms.
Essential concepts and skills for Generative Adversarial Networks (GANs).
Essential concepts and skills for Variational Autoencoders (VAEs).
Essential concepts and skills for Diffusion Models.
Essential concepts and skills for Large Language Models (LLMs).
Essential concepts and skills for Multimodal Generative Models.
Essential concepts and skills for TensorFlow / Keras.
Essential concepts and skills for PyTorch.
Essential concepts and skills for Hugging Face Transformers.
Essential concepts and skills for JAX (Optional Advanced).
Essential concepts and skills for Jupyter Notebooks and Environments.
Essential concepts and skills for Docker for AI.
Essential concepts and skills for Cloud Platforms (AWS, GCP, Azure for...
Essential concepts and skills for Experiment Tracking with MLflow.
Essential concepts and skills for CI/CD for ML (MLOps).
Essential concepts and skills for Model Deployment (Flask, FastAPI, Streamlit).
Essential concepts and skills for Scaling and Optimization.
Essential concepts and skills for Model Monitoring and Maintenance.
Essential concepts and skills for Fine-tuning and Transfer Learning.
Essential concepts and skills for Reinforcement Learning for Generative AI.
Essential concepts and skills for AI Ethics and Bias Mitigation.
Essential concepts and skills for Reading and Implementing Research Papers.
Frequently Asked Questions
Common questions about this roadmap
Traditional ML classifies or predicts. Generative AI creates new content — text, images, code, audio. It uses models like GANs, Diffusion Models, and LLMs to generate outputs that didn't exist before.
Yes, this is non-negotiable. Transformers are the backbone of LLMs (GPT, Claude, Llama) and modern diffusion models. Understanding self-attention, positional encoding, and fine-tuning is critical.
Diffusion Models (like Stable Diffusion) have largely overtaken GANs for image generation. Learn the concepts of GANs for foundational understanding, but prioritize Diffusion Models and LLMs for practical work.
Both are essential skills. Prompt engineering is faster and cheaper. Fine-tuning gives you more control and better performance for specific tasks. A Gen AI Engineer needs to know when to use each approach.
For learning, Google Colab's free GPU tier is sufficient. For serious work, cloud GPUs (AWS, GCP) or services like Lambda Labs are needed. You rarely need to own expensive hardware.