Eric Khang'ati

Learning. Coding. Evolving.

Blog

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.

Linear Regression With NumPy: House Price Prediction

Predict house prices using a linear regression model built entirely with NumPy. This beginner project covers data prep, cost function, and gradient descent.

Logistic Regression for Email Spam Detection with NumPy

Build a spam detection model using logistic regression and NumPy. Learn how to process text data, apply the sigmoid function, and classify emails effectively.

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 trees explained through the Titanic dataset

A beginner-friendly guide to using decision trees for predicting Titanic survival, featuring step-by-step code, clear explanations, pruning, and evaluation.

Customer Churn Prediction With Random Forest and XGBoost

Better customer churn prediction is possible: See how we applied both Random Forest and XGBoost models to telecom data to anticipate cancellations in advance.

On a journey to master machine learning — grounded in strong fundamentals and committed to producing clean, reliable, real-world solutions.