Interactive SkLearn Series - Built-in Datasets
Don't spend hours finding data. We'll use sklearn.datasets to load toy datasets for quick learning and fetch larger, real-world datasets to test your models on actual problems.
Interactive SkLearn Series - The Estimator API
The heart of sklearn lies in fit, predict, and transform. Master these three methods to unlock the entire library, treating every algorithm as a standardized "estimator" object.
Interactive SkLearn Series - Data Representation
ML models expect specific structures. We'll cover feature matrices (X) and target vectors (y), and how to bridge the gap between Pandas DataFrames and NumPy arrays for seamless integration.
Interactive NumPy Series - Customization
Extend NumPy’s capabilities. We look at structured arrays for heterogeneous data (like C structs) and how to write custom ufuncs to apply your own logic with NumPy’s native speed.
Interactive NumPy Series - Advanced Indexing Techniques
Tackle complex data access with ix_ for open meshes and einsum (Einstein Summation). This powerful mini-language allows you to express complex tensor contractions efficiently.
Interactive NumPy Series - Memory Management and Strides
Peek under the hood at how NumPy traverses memory. Understanding strides and memory layout (C vs. Fortran order) is the secret to optimizing performance and avoiding unnecessary data copying.
Interactive NumPy Series - Statistical Analysis
Extract insights from noise. We use aggregation functions to calculate histograms, correlations, and percentiles, rapidly summarizing millions of data points along specific axes.
Interactive NumPy Series - The Random Generator
Move beyond simple rand. We use the modern Generator API to sample from rigorous statistical distributions (Normal, Poisson), ensuring reproducible and scientifically valid simulations.
Interactive NumPy Series - Decompositions and Solvers
Dive into advanced linear algebra. We explore eigenvalues, singular value decomposition (SVD), and solving linear systems—tools essential for dimensionality reduction and physics simulations.
Interactive NumPy Series - Vector and Matrix Products
The engine of ML. We differentiate between dot products, inner/outer products, and matrix multiplication (matmul), utilizing the @ operator to transform vector spaces efficiently.
