Have you ever wondered how we predict things—like how much your grocery bill will be or how much website traffic to expect at a certain time? Prediction isn’t just a modern trick; it’s an ancient skill we’ve relied on for survival for centuries. And now, there’s a YouTube playlist that makes this fascinating science accessible to everyone: Ancient Science of Prediction.
This machine learning series is designed for students, non-tech learners, and total beginners, breaking down complex ML concepts in a clear, approachable style. Whether you’re excited to explore new ideas or just starting out, this series will guide you through the foundations of prediction and machine learning in an engaging way!
Why This Playlist Is Perfect for Beginners
Machine learning might sound like something only tech wizards understand, but this playlist proves it’s for everyone. Designed with clarity and curiosity in mind, it takes you from “What’s prediction?” to solving real-life problems with code from scratch—step by step. Big ideas are broken down into bite-sized, relatable lessons that anyone can follow. And the best part? It’s growing, with more videos on the way to keep you engaged!
What’s Inside the Playlist
Here’s a hint of what you’ll find in the first four videos:
Why does prediction matter? This video introduces the core idea of prediction, showing how it drives science and decision-making.
It also outlines the series roadmap, covering solving equations, automating predictions with Python, calculus fundamentals like differentiation and gradient descent, and building ML models from scratch—all without relying on libraries like Scikit-learn or TensorFlow.
- Video 2: Solving Linear Equations
Ever wondered how math helps in real-world decisions? This video breaks it down using a simple supermarket example: How does the total cost change when you buy more apples—or add mangoes?
You’ll learn to set up and solve linear equations step by step, uncovering the concept of prediction. We’ll start with two-variable equations, using elimination and graphical methods to find costs. Then, we’ll level up to three-variable equations, plotting data in three dimensions and solving them systematically.
By the end, you’ll see how prediction isn’t just about numbers—it’s the foundation of machine learning. Try solving the given challenge and see if you can apply what you’ve learned before we dive into coding these concepts in Python in the next video!
This video explores how to use Python to efficiently solve a system of linear equations through matrix operations. Building on the concepts covered in the previous video—where we used the elimination method to determine the values of three variables—we now transition to a computational approach by leveraging matrices and structured programming.
The video covers:
- Representing a system of equations in matrix form
- Understanding augmented matrices and their role in solving linear equations
- Implementing the eliminate function to systematically reduce the matrix using row operations
- Applying the solve_elimination function to determine variable values through back-substitution
- A step-by-step breakdown of the Python code, explaining loops, indexing, and computational logic
By the end of this tutorial, you will gain a clear understanding of how Python can be used to automate complex algebraic calculations efficiently. This video is ideal for students, researchers, and professionals looking to integrate computational methods into problem-solving.
What happens when relationships between variables don’t form a straight line? This video expands on our previous discussion of linear interpolation by introducing quadratic interpolation, which models curves for more accurate predictions.
Using website traffic growth as an example, we demonstrate how quadratic functions help analyze trends when data points don’t align perfectly in a straight line. We define the quadratic equation y = ax² + bx + c, derive it from a dataset, and frame a system of equations to solve for a, b, and c using matrices. To make computations efficient, we break down matrix-vector multiplication and its role in solving quadratic interpolation problems.
Finally, we automate the process in Python, eliminating manual calculations for seamless predictions.
Concepts covered in this video:
- Quadratic equations in interpolation
- Converting data into a system of equations
- Matrix representation for efficient computation
- Using Python to automate solving and predictions
What’s Coming Next
The journey doesn’t stop here! We’re adding more exciting concepts and videos to this series, building towards deeper insights in machine learning. Here’s a sneak peek at what’s coming soon:
- Linear Approximation: A powerful tool that helps us estimate functions using straight lines—simplifying complex calculations.
- Differentiation in Machine Learning: Understanding how things change—crucial for optimization problems.
- Gradient Descent: The heart of machine learning algorithms—this technique helps models learn by finding the best possible values for predictions.
What Makes This Playlist Stand Out
This playlist makes machine learning accessible, breaking down complex concepts into simple, engaging explanations. It uses relatable examples—such as fruits and websites—to illustrate key ideas in an intuitive way. It’s structured to be accessible for learners from all backgrounds. Plus, the Python bits are very beginner-friendly!
Begin Your Journey Today
If you’re ready to unlock the secrets of prediction and dip your toes into machine learning, head over to Ancient Science of Prediction on YouTube. With just four videos, it’s already a well-curated resource, and more are on the way. Watch it, try the examples, and see how fun ML can be!
You can explore the Ancient Science of Prediction GitHub repository here: https://github.com/girisandeep/asop.
Does this sound like the perfect playlist for you? Share your thoughts in the comments and pass it along to anyone who enjoys learning!
