{"product_id":"machine-learning-decoded","title":"Machine Learning Decoded: From Logic to Code","description":"\u003cp\u003eMost ML resources either drown you in math or skip the fundamentals entirely. This 30-page guide walks you through the actual workflow — from defining the hypothesis to shipping a production sklearn pipeline.\u003c\/p\u003e\u003cbr\u003e\u003cp\u003eWHAT'S INSIDE\u003cbr\u003e• Part 1 — Defining the Hypothesis (Pages 1–6): the formal learning problem, problem-type taxonomy, four essential loss functions (MSE, MAE, Binary Cross-Entropy, Huber), and a learning curve diagnostic\u003cbr\u003e• Part 2 — Data is 80% of the Work (Pages 7–15): the MCAR\/MAR\/MNAR missingness framework, Python imputation cookbook, scaling approaches (StandardScaler \/ MinMaxScaler \/ RobustScaler), pipeline-based leakage prevention, and feature engineering recipes\u003cbr\u003e• Part 3 — The Algorithm Library (Pages 16–24): linear models with regularization, decision trees with Gini\/IG, Random Forest with variance decomposition, MDI vs. Permutation importance, and an algorithm selection guide\u003cbr\u003e• Part 4 — The Production Workflow (Pages 25–30): a full ColumnTransformer pipeline, GridSearchCV inside CV, an evaluation suite, an 18-item three-phase production checklist, and a complete sklearn API cheat sheet\u003c\/p\u003e\u003cbr\u003e\u003cp\u003eWHO THIS IS FOR\u003cbr\u003e→ Software engineers transitioning into ML\u003cbr\u003e→ Data analysts moving up from descriptive analytics\u003cbr\u003e→ Self-taught ML learners who want a structured production-ready reference\u003c\/p\u003e","brand":"HiddenTech","offers":[{"title":"Default Title","offer_id":48948324728984,"sku":"MACHINE_LEARNING_DECODED","price":19.0,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0767\/1994\/7928\/files\/ml-decoded-cover.png?v=1777569870","url":"https:\/\/tlc-tech-products.myshopify.com\/products\/machine-learning-decoded","provider":"Hidden Tech","version":"1.0","type":"link"}