Back to Learning

AI

Foundational AI concepts and practical applications in engineering.

AI Topics

ML Fundamentals

Core concepts: supervised learning, evaluation, and features.

Model Deployment & MLOps

Serving models, monitoring, and versioning.

NLP Basics

Text representation, transformers, and applied NLP tasks.

High-level Overview

AI combines statistical learning, data engineering, and model development to build systems that can make predictions, understand language, or recognize patterns. Practical AI in engineering focuses on solving concrete problems and deploying models reliably.

Learning Path

  1. Math & ML basics: linear algebra, probability, regression/classification.
  2. Modeling: supervised, unsupervised, and deep learning basics.
  3. Model evaluation and interpretability.
  4. Deployment and MLOps: serving, monitoring, versioning.

Recommended Resources

  • Online courses with hands-on notebooks (Coursera, Fast.ai)
  • Experiment with pre-trained models (Hugging Face, TensorFlow Hub)