Introduction to Machine Learning for Trading: What Actually Works

A clear-eyed, myth-busting path into using machine learning for trading, built for quant analyst aspirants, prop trading applicants, and traders looking to systematize their process. Covers where ML actually adds value versus where it is just curve-fitting in disguise, correct problem framing (prediction vs signal vs execution), the data leakage and look-ahead traps that quietly wreck most beginner models, honest model selection between linear, tree-based, and deep learning approaches, and how a validated signal turns into a strategy once transaction costs and slippage are accounted for. Built on real NSE and BSE data, with Python throughout.