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Statistical machine learning, regression models, loss optimization, and math.
A comprehensive guide to 2D charting, figure layouts, subplots, visual hierarchy, custom styling, and exploratory data visualization using Matplotlib and Pandas.
A practical guide to evaluating classification and object detection models using Confusion Matrices, Precision, Recall, F1 Score, and Mean Average Precision (mAP).
An engineering teardown of Pandas internal memory structures, 1D Series vs 2D DataFrame block managers, label-based (.loc) vs positional (.iloc) indexing, missing data imputation, split-apply-combine aggregations, pivot tables, and vectorized string feature extraction.
A first-principles engineering teardown of NumPy ndarray memory layouts, strided access arithmetic, C vs Fortran order, broadcasting shape alignment, ufuncs, and linear algebra solvers.
A complete data visualization laboratory and capstone suite featuring exploratory data analysis, multi-panel custom figures, statistical distributions, and correlation matrices built with Matplotlib and Pandas.
A real-time computer vision and machine learning inference pipeline using YOLOv8, OpenCV, and Django to detect wildlife intrusion and trigger automated alert notifications.