A 4-phase parallel learning roadmap from linear algebra to neural networks
kmeanskaran · x · 2026-09-09
The author lays out a 4-phase ML learning roadmap emphasizing parallel study: Phase 1 pairs linear algebra with Python and statistics with Pandas, then covers feature engineering, basic algorithms (regression, classification, clustering) and evaluation metrics, with hands-on Jupyter Notebook projects. Phase 2A starts neural network basics without derivations. The core idea: apply the parallel-subjects approach from school, driven by curiosity.
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