Framing questions, describing and cleaning data, honest prediction pipelines, EDA and Simpson’s paradox.
5 topics53 lessons318 questions ~6 h
4 labs100-mark mock exam8 weeks coming soon
aNet.ai · two courses, one learning space
Learn data science and machine learning, one lesson at a time.
2 complete courses — 204 lessons, 425 practice questions, labs and a full mock exam. XP, streaks, levels and spaced review carry across both.
Start learning · Data science, AI, machine learning and deep learning0%of aNet.ai
From mathematical modeling and linear regression to neural networks, GANs and federated learning.
18 topics151 lessons107 questions ~21 h
84 formulasKnowledge graphLecture-slide examples
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Where the courses meet
Both open by separating AI, machine learning and deep learning — and asking what question the data should answer.
Means, variances and standardisation in DS become expectation, variance and covariance in ML.
Train/test splits and leakage in the DS pipeline are the foundation of honest model evaluation.
Lab 4’s feature vectors and distances are exactly how nearest-neighbour methods predict.