Eric Khang'ati

Learning. Coding. Evolving.

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Explore a curated collection of beginner-friendly machine learning projects and tutorials. Each article breaks down complex topics into simple, practical steps, from algorithms built with NumPy to real-world classification tasks, with code, visuals, and clear explanations for all learners.

KNN with Scikit-Learn: A Heart Disease Prediction Guide

Learn how to build a heart disease prediction model using KNN with Scikit-Learn in Python. A beginner-friendly step-by-step guide.

Build a Support Vector Machine From Scratch using Numpy

Learn how to build a Support Vector Machine (SVM) from scratch using NumPy. This guide explains the math, Hinge Loss, and Gradient Descent for beginners.

Heart Failure Prediction Using SVM with Scikit-Learn Guide

Learn how to build a heart failure prediction model using SVM with Scikit-Learn. This step-by-step tutorial covers preprocessing, model training, and evaluation for beginners.

Neural Network from Scratch with NumPy for MNIST Digits

This project walks through creating a neural network using NumPy to recognize handwritten digits. Gain hands-on experience with forward and backpropagation.

Underfitting and Overfitting with Polynomial Regression

Learn how underfitting and overfitting affect model performance using polynomial regression on real housing data, with clear visuals and code examples.

Decision Tree from Scratch with NumPy Explained Simply

Learn how to build a decision tree from scratch using NumPy. Understand entropy, information gain, and step-by-step model construction in Python.

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