Curriculum

Core math, Python, and conceptual building blocks for AI/ML

NumPy, Pandas, and data visualization for ML

Master classical ML algorithms, evaluation, and pipelines with scikit-learn

Neural networks, TensorFlow, Keras — from basics to production

The research-first deep learning framework — tensors, models, and training

Image classification, object detection, segmentation, and generative models

Text processing, embeddings, transformers, and modern NLP

Transformers library, model hub, datasets, tokenizers, and PEFT

Vector databases, RAG pipelines, LangChain, and building AI agents

Agents, environments, Q-learning, and policy gradients

Classical and deep learning methods for temporal data

Collaborative filtering, content-based, and deep learning recommendations

Model deployment, serving, monitoring, and ML pipelines

Designing, scaling, and governing enterprise AI systems

Large language models, RAG, agents, and prompt engineering

Ethics, governance, privacy, and building trustworthy AI systems

AWS SageMaker, Google Vertex AI, and Azure ML

Model optimization, quantization, and deployment to mobile and IoT

SHAP, LIME, feature importance, and model interpretability

Feature stores, data versioning, and quality pipelines

Automated model selection, tuning, and architecture design