Hey there! Welcome to my corner of the internet where I break down complex machine learning concepts into digestible, engaging explanations. No PhD required - just curiosity and a willingness to learn!
I believe that the best way to truly understand machine learning is to build intuition from the ground up. That’s why I focus on explaining the fundamental algorithms that power everything from your Netflix recommendations to cutting-edge AI systems.
June 1, 2025
Ever wondered how machines actually “learn”? It all starts with gradient descent - the workhorse optimization algorithm that powers modern AI. In this comprehensive guide, I’ll take you from the intuitive “blindfolded on a mountain” analogy all the way through to advanced optimizers like Adam.
What you’ll learn:
June 5, 2025
Regression is where many people’s machine learning journey begins - and for good reason! It’s intuitive, powerful, and forms the foundation for understanding more complex models. This guide takes you from simple linear regression all the way through regularization techniques.
What you’ll learn:
June 10, 2025
Ever wondered how to draw the BEST possible line between two groups? Support Vector Machines don’t just find a boundary - they find the one with maximum confidence. In this guide, we’ll explore the elegant mathematics behind SVMs and the kernel trick that makes them so powerful.
What you’ll learn:
June 15, 2025
Remember playing “20 Questions” as a kid? That’s exactly how decision trees work! They learn to ask the right questions about your data to make predictions. This guide takes you from the intuitive concept through implementation, visualization, and even into the forest of ensemble methods.
What you’ll learn:
June 16, 2025
What if we could solve complex classification problems by making a “naive” assumption that all features are independent? Turns out, this simple idea leads to blazingly fast classifiers that work surprisingly well, especially for text classification. Join me as we explore the probabilistic foundations and practical applications of Naive Bayes.
What you’ll learn:
June 20, 2025
Journey from a single artificial neuron to the deep architectures powering modern AI. This comprehensive guide takes you from zero to implementing your own neural network, explaining how these brain-inspired systems learn to recognize patterns, make decisions, and even create art.
What you’ll learn:
I’m a Computer Engineering graduate student at NYU Tandon, and I’ve been on both sides of the learning curve - from struggling to understand these concepts to teaching them to others. My goal is to create the resource I wish I had when I was starting out.
Each post is crafted to:
Have questions? Found an error? Want to suggest a topic? Feel free to reach out!
Happy learning! 🎓
“The best way to learn is to teach. The second best way is to write about it.”