Course Catalog
15 courses across 12 AI domains
Featured Courses
Python for Machine Learning
Master Python fundamentals and essential libraries for ML including NumPy, pandas, and scikit-learn.
Mathematics for Machine Learning
Build the mathematical foundation you need: linear algebra, calculus, probability, and statistics.
Machine Learning Fundamentals
Supervised and unsupervised learning, model evaluation, feature engineering, and real-world ML pipelines.
Deep Learning with PyTorch
Neural networks, CNNs, RNNs, and transfer learning. Build and train deep models from scratch.
Transformer Architecture Deep Dive
Understand attention mechanisms, build transformers from scratch, and explore BERT, GPT, and beyond.
Large Language Models & GenAI
Master LLM architecture, prompt engineering, RAG, fine-tuning, agents, and production deployment.
All Courses
Prompt Engineering Mastery
Advanced prompting techniques: chain-of-thought, few-shot, constitutional AI, and systematic evaluation.
Large Language Models & GenAI
Master LLM architecture, prompt engineering, RAG, fine-tuning, agents, and production deployment.
Machine Learning Fundamentals
Supervised and unsupervised learning, model evaluation, feature engineering, and real-world ML pipelines.
Building RAG Applications
Vector databases, embeddings, chunking strategies, hybrid search, and production RAG systems.
Python for Machine Learning
Master Python fundamentals and essential libraries for ML including NumPy, pandas, and scikit-learn.
Deep Learning with PyTorch
Neural networks, CNNs, RNNs, and transfer learning. Build and train deep models from scratch.
Mathematics for Machine Learning
Build the mathematical foundation you need: linear algebra, calculus, probability, and statistics.
Transformer Architecture Deep Dive
Understand attention mechanisms, build transformers from scratch, and explore BERT, GPT, and beyond.
Natural Language Processing
From tokenization to modern NLP: embeddings, sequence models, text classification, and generation.
Computer Vision with Deep Learning
Image classification, object detection, segmentation, and generative models for visual data.
AI for Product Managers
AI literacy for PMs: capabilities, limitations, building AI products, metrics, and working with ML teams.
MLOps & Production ML
Deploy, monitor, and maintain ML systems. Docker, Kubernetes, CI/CD, experiment tracking, and drift detection.
ML System Design
Design scalable ML systems: recommendation engines, search ranking, fraud detection, and real-time serving.
Reinforcement Learning
MDPs, Q-learning, policy gradients, PPO, and deep RL. Train agents that learn from interaction.
AI Ethics & Safety
Bias detection, fairness, interpretability, adversarial robustness, AI alignment, and responsible AI practices.