मशीन लर्निंग सीखें - उन्नत स्तर

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The Beginner book introduced features, labels, means, squared errors and held-out evaluation. Advanced study means making the boundaries of a model study inspectable: a question the split can answer, an objective the arithmetic actually optimises, and an evaluation that did not choose its own winner. All explanations are in Hindi. This 56-page guide runs through 20 original units on paper: define what the error is meant to estimate; derive a least-squares line from centred sums; read a two-parameter gradient update; separate fitting success from prediction evidence; fit transforms only inside the training boundary; compare penalties under a stated objective; turn a linear score into a probability model; choose a threshold against declared costs; separate ranking, probability error and calibration; state which fold average you are reporting; put selection inside the outer evaluation; respect groups and the direction of time; calculate a split gain and a pruning tradeoff; average errors with the cross-term visible; read boosting as sequential correction; make distance weights and similarities explicit; reduce dimensions while keeping reconstruction visible; evaluate clusters without inventing true classes; test mixture sensitivity without claiming a cause; and a capstone closing a selection study with a bounded open claim. Every unit has a lesson and a workshop with a worked case, safe activity and explained answers. Every workshop uses invented records on paper, with optional harmless offline arithmetic - no live account, personal data or automated action is needed or authorised. Derivatives are introduced through explicit formulas; programming and third-party machine learning packages are not required. Best suited to readers who have finished the Beginner edition or are already comfortable with features, labels, means, squared errors and held-out evaluation, and want to see what makes a model study trustworthy. School algebra and a calculator for square roots, ex