ML Foundations: The Naive Bayes Classifier Introduction In our journey through machine learning algorithms, we’ve seen models that find lines (Linear Regression), create S-curves (Logistic Regres...

ML Foundations: K-Nearest Neighbors (KNN) Introduction Welcome back to our machine learning foundations series! Today, we’re looking at one of the most intuitive and straightforward algorithms in...

ML Foundations: Unsupervised Learning and K-Means Clustering Introduction Throughout our machine learning series, we’ve focused on Supervised Learning. In supervised learning, our data is labeled...

ML Foundations: Support Vector Machines (SVMs) Introduction In our machine learning series, we’ve explored models that classify data in different ways. Logistic Regression finds a line that separ...

ML Foundations: Decision Trees and Random Forests Introduction So far in our series, we’ve explored models that are fundamentally mathematical, like Linear and Logistic Regression. Now, we’re goi...

ML Foundations: Preprocessing Data for Machine Learning Introduction You’ve learned about powerful machine learning models like Linear and Logistic Regression. But in the real world, data is rare...

ML Foundations: How to Evaluate Your Classification Model Introduction So, you’ve built your first classification model—perhaps a Logistic Regression to detect spam emails. It’s trained, and it’s...

Machine Learning Foundations: Logistic Regression for Classification Introduction In our previous post, we explored Linear Regression, a powerful tool for predicting continuous values like house ...

Async Programming in FastAPI: async def vs def Introduction One of FastAPI’s core strengths is its native support for asynchronous programming using Python’s async and await keywords. Understandi...

Machine Learning Foundations: Linear Regression and Gradient Descent Introduction Welcome to our foundational series on Machine Learning! This series is for everyone—whether you’re a student prep...