Machine Learning Handwritten Notes: Foundations, Data & Feature Engineering
English | October 12, 2026 | ASIN: B0HMQXMYQB | 84 pages | Epub | 3.72 MB
Learn Machine Learning the way toppers revise: from clear, handwritten notes.Machine Learning Handwritten Notes - Part 1 builds the foundation every ML engineer needs: understanding your data and preparing it for models. Prepared by Shishir Singh, M.Tech (AI) student at IIT Bhubaneswar.these notes explain every concept with hand-drawn graphs, simple examples, formulas and ready-to-use scikit-learn code.What you'll learn in Part 1:ML Fundamentals - supervised, unsupervised, semi-supervised and reinforcement learning; batch vs online learning; instance-based vs model-based learningChallenges & Life Cycle - overfitting, underfitting, data quality, and the complete ML Development Life Cycle (MLDLC)Tensors - 0D to 5D tensors with real examples from text, images and videoFraming ML Problems & Data Gathering - turning a business problem into an ML problem; CSV, JSON, SQL, APIs and web scrapingExploratory Data Analysis - univariate, bivariate and multivariate analysis, box Descriptions, histograms, heatmaps, pair Descriptions and Pandas ProfilingFeature Scaling - standardization, MinMax, MaxAbs and robust scaling, and when to use eachEncoding - ordinal, label and one-hot encoding, and the dummy variable trapColumn Transformer & Pipelines - clean, reusable preprocessing workflowsMathematical Transforms - log, reciprocal, Box-Cox and Yeo-JohnsonBinning & Binarization - equal-width, quantile, K-Means and custom binningMissing Data - mean/median, arbitrary, end-of-distribution, random, KNN and MICE imputationOutliers - detection and treatment with Z-score, IQR, percentile and
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