ML Essentials Explained: Functions, Loss, and Gradient Descent + Iterative Live Demos - With Code

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/tensorf...
Connect with the author: José Cruz

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⏱️ *Timestamps*

00:00 What Machine Learning Really Is (Seniors and Juniors)
05:46 Example with House Prices (Error/Loss Function)
12:32 Iterative Example for Function Approximation
15:09 Iterative Example for Gradient Descent
20:36 Demo: Linear Regression in NumPy
26:41 Demo: K-means Clustering in NumPy
32:15 Core Machine Learning Algorithms Overview
34:49 A complete code example

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