ML Essentials Explained: Functions, Loss, and Gradient Descent + Live Demos (Linear Regression & K-means)

Unlock the core ideas that power modern Machine Learning. In this video, we break down ML’s foundation: function approximation, supervised vs. unsupervised learning, and the optimization engine behind nearly every model—Loss Functions and Gradient Descent.

To make the concepts real, we walk through two hands-on demos using NumPy and Matplotlib:

• **Linear Regression** for supervised learning (predicting house prices)

• **K-means Clustering** for unsupervised learning (discovering patterns in unlabeled data)

By the end, you’ll understand not just the “how,” but the **why**—why loss functions measure model quality, why gradient descent converges, and why ML is ultimately about approximating unknown functions.

Full code demos will be available on GitHub https://github.com/josedacruz/tensorflow-demos

Connect with the author: José Cruz