Subject
11 entries
Course
Bookmarks
Solve It With Code: problem-solving course with AI and craftsmanship
Solve It With Code is a 5-week course by Jeremy Howard (fast.ai) and Eric Ries (Lean Startup) teaching the SolveIt method — problem-solving that combines AI tools with independent thinking and craftsmanship. The anti-vibe-coding curriculum.
Advanced AI Agents Course (DAIR.AI)
DAIR.AI's Advanced AI Agents course covers sophisticated agentic patterns — prompt chaining, routing, parallelization, multi-agent architectures, evaluator-optimizer patterns, and deployment on Google Cloud Run. 38 lessons, 4.5 hours, taught by Elvis Saravia.
NLP Demystified
NLP Demystified is a free video course covering NLP fundamentals from text preprocessing through transformers and modern language models — aimed at practitioners who want a solid conceptual foundation rather than just API usage. One of the cleaner free NLP curricula available.
fast.ai: From Deep Learning Foundations to Stable Diffusion
fast.ai's Part 2 2022 course preview — the first two lessons of their deep learning foundations to Stable Diffusion curriculum, taught bottom-up from first principles. Jeremy Howard teaching diffusion models the way fast.ai teaches everything: by building it yourself.
MIT 6.S898: Deep Learning (Fall 2022)
MIT's 6.S898 Deep Learning course taught by Phillip Isola, covering the full modern deep learning stack from fundamentals through generative models and transformers. One of the cleaner academic deep learning curricula, with public materials.
Modern NLP with Large Language Models (Sinan Ozdemir, Maven)
Sinan Ozdemir's Maven cohort course on modern NLP with GPT-3/4 and BERT — covering information retrieval, multi-task pipelines, and prompt engineering. One of the early structured courses teaching practitioners how to build with LLMs.
Full Stack Deep Learning (Spring 2021)
Full Stack Deep Learning is a free course bridging ML research and production deployment — covering the full pipeline from data management and model training to testing, monitoring, and team structures. The course for researchers who want to ship and engineers who want to understand ML.
ML Zoomcamp — Free Cohort Machine Learning Course
Alexey Grigorev's ML Zoomcamp — a free cohort-based machine learning course covering regression, classification, deployment, and MLOps fundamentals. A comprehensive practical curriculum from the author of Machine Learning Bookcamp.
ISchool 296A: Data Science Algorithms (Berkeley Spring 2012)
UC Berkeley's iSchool 296A course on Data Science Algorithms from Spring 2012 — one of the early university data science courses before the field had a standard curriculum. Represents Berkeley's role in formalizing data science education.
Introduction to Machine Learning — Alex Smola, CMU 2013
Alex Smola's Introduction to Machine Learning course at CMU (10-701, 2013) — a graduate-level survey of ML theory and methods from one of the field's top researchers. Course materials publicly available, covering optimization, probabilistic models, and learning theory.
Analyzing Big Data with Twitter — UC Berkeley iSchool
UC Berkeley iSchool's 2012 course on analyzing big data with Twitter — an early academic offering that bridged social media data and distributed computing tooling before data science programs existed at most universities.