Machine Learning Algorithms from Scratch with Python and NumPy

Understanding how machine learning algorithms work under the hood is essential for any aspiring data scientist. Building algorithms from scratch demystifies complex math and reveals the inner mechanics of predictive models. By reading through step-by-step written explanations and code implementations, you will develop a deep intuition for how algorithms process data and optimize performance without relying on pre-built libraries. What you will learn: Understand foundational machine learning terminology and vector operations. Implement linear and logistic regression models using pure NumPy. Build decision trees and distance-based classifiers line by line. Apply optimization techniques like gradient descent to train models. Evaluate model performance using standard metrics like accuracy and mean squared error. Preprocess and clean datasets effectively using Pandas. The course begins with key terminology and basic array manipulation before guiding you through constructing algorithms step by step. Designed for beginners with basic Python knowledge, this text-based guide requires no prior machine learning experience. Read through the material and start building machine learning algorithms from the ground up.