MIT 6.S191 Introduction to Deep Learning vs Data Scientist with Python Career Track
Same Bayesian formula, same rubric — so the difference in scores reflects the difference in the courses, not the difference in how we evaluated them.
Massachusetts Institute of Technology (introtodeeplearning.com) · AI & ML Courses
MIT 6.S191 Introduction to Deep Learning
DataCamp · AI & ML Courses
Data Scientist with Python Career Track
Per-criterion
MIT 6.S191 Introduction to Deep Learning
Reviewers consistently praise that the curriculum is refreshed annually and reaches modern topics — Transformers, generative modeling, LLMs, AI for science — that older courses do not cover. The honest catch is that depth is sacrificed for breadth in eight lectures.
Alexander Amini is described as clear, energetic and good at building intuition from first principles. The recurring caveat is the rotating-lecturer format — multiple reviewers wish Amini taught every lecture rather than alternating with guests and co-instructors.
Completely free — lectures on YouTube, slides on introtodeeplearning.com, labs on GitHub, runnable in free Google Colab. No paywall on any core material. The optional MIT Professional Certificate is not the path most reviewers take.
There is no official forum for online learners. Reviewers credit the GitHub issue tracker as the de facto Q&A channel, but multiple 2024-2025 issues report unresolved bugs in the PyTorch Sequential labs and outdated Colab dependencies.
Three Colab labs (music generation, vision, LLMs) are short but hands-on in both TensorFlow and PyTorch. Reviewers note this is a foundation, not a job-ready portfolio — you finish with intuition and small projects, not a deployed model.
Data Scientist with Python Career Track
Scoring methodology applies identically to every course on the site — see the formula.