What is Machine Learning, and How Do I Get Started?
summary
Clear checklist: justification for ML (when a human labels something in under a second), mindset shift away from line‑by‑line coding, concrete data rules of thumb (about 50 examples minimum, CrowdFlower finds data prep takes 50–80% of time), concrete tools and code snippets using scikit-learn (train_test_split, SVC, fit/predict, accuracy/precision/recall), notes on neural nets and Keras/transfer learning, and a PhD thesis example of a noninvasive fNIRS brain‑computer interface that sends movement predictions to a robot.
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chapters (9)
- 0:00Introduction to machine learning basics
- 3:00Changing mindset from traditional programming
- 6:12Is ML right?
- 9:26Data prep & cleaning
- 16:11Train/test split
- 18:20Simple ML model
- 22:14Model metrics
- 24:44Recap & next steps
- 26:55Q&A
