Skill
Machine learning fundamentals
Supervised learning, features, loss, overfitting. Models are not magic.
Do these first
What you will be able to do
- Fit a baseline model
- Evaluate with an appropriate metric
- Diagnose overfitting vs underfitting
Curated resources
Start with the pick, then branch out
Scores are LearnPath editorial opinions (clarity, cost, freshness, project density) — not an objective ranking. Every YouTube item is embedded officially after a click-to-load facade.
Fastest path
LearnPath pickinteractivebeginner
Intro to Machine Learning
Kaggle3h
Short, free, in-browser notebooks.
Score 89
Open
6hProject-basedAlternativeyoutube coursebeginner
Machine learning with Python — full course
freeCodeCamp / freeCodeCamp.org6h
sklearn-oriented. Pair with official User Guide.
Score 87
Watch
50 minFastest pathQuick pathyoutube videobeginner
Python Machine Learning Tutorial (Data Science)
Programming with Mosh50 min
Fifty minutes that take you from a CSV to a trained, evaluated model with Jupyter and scikit-learn. The quickest honest answer to 'what does training a model feel like'.
Score 75
WatchOfficial docs
Official docsdocumentationintermediate
scikit-learn user guide
scikit-learn4h
Read Supervised learning + Model selection.
Score 95
Open
9h 52mBest for beginnersMore curatedyoutube coursebeginner
Machine Learning Course for Beginners
freeCodeCamp.org9h 52m
Ten hours of machine-learning fundamentals with Python — regression, classification, clustering, model evaluation — built around scikit-learn and real datasets. Broad enough to be a curriculum and practical enough to produce working models.
Score 82
Watch
53 minBest for beginnersMore curatedyoutube coursebeginner
AI Foundations for Absolute Beginners
freeCodeCamp.org53 min
A 2026 freeCodeCamp course for absolute beginners — what AI systems are, how they are trained, and how to use a pretrained model in code without a maths detour first.
Score 80
Watch18 videos
More curatedplaylistbeginner
Machine Learning
freeCodeCamp.org90h 58m
freeCodeCamp's machine-learning collection — including the 'Machine Learning for Everybody' course — gathered in one place. Useful as a curated shelf once you finish a first course and want breadth.
Score 77
Open
10 minFastest pathMore curatedyoutube videobeginner
AI, Machine Learning, Deep Learning and Generative AI Explained
IBM Technology10 min
Ten minutes that clearly separate AI, machine learning, deep learning and generative AI — including what each is not. Worth watching before any AI roadmap so the vocabulary stops blurring.
Score 65
Watch
16 minFastest pathMore curatedyoutube videobeginner
All Machine Learning algorithms explained in 17 min
Infinite Codes16 min
Sixteen animated minutes that put every common algorithm on one map — supervised, unsupervised, ensembles, boosting — so the names stop being noise. Best used before or alongside a full course, not instead of one.
Score 65
Watch