Become a full‑stack AI and data professional by mastering the complete lifecycle: problem framing, data engineering, ana...
Core concepts, roles, workflows, and ethics that frame end‑to‑end AI projects.
Practical Python skills and libraries essential for data manipulation and analysis.
The mathematical pillars underpinning models, optimization, and inference.
Techniques to gather, validate, clean, and prepare diverse data for modeling.
Querying data efficiently and designing analytical storage for scale.
Systematic EDA and clear visual communication to uncover insights.
Algorithms, workflows, and evaluation for predictive modeling.
Structure discovery, representation learning, and recommender systems.
Modern neural architectures, training strategies, and model compression.
From classic NLP to transformer‑based LLMs, tuning, and safe deployment.
Productionizing models with robust pipelines, CI/CD, monitoring, and governance.
Building scalable, secure data platforms and real‑time pipelines in the cloud.