I’ve been working in frontend technologies for the greater part of my entire career as an engineer, from implementing complex web applications and mobile apps to working on UI frameworks and contributing to widely used open-source component libraries. However, all along, I was looking at AI out of the corner of my eye.
Every time I tried to dip my toes into the AI waters, it felt like swimming against the current. I devoured books, and scoured articles, and yet, the process of how a model learns seemed shrouded in mystery. It was as if an essential piece of the puzzle was missing, hidden behind a curtain of complex equations and terminologies.
You see, even with a degree in computer science and having crunched plenty of math during my student days, I realized that most of it was done to get good grades and pass exams. It’s funny, I didn’t fully appreciate the value of those math classes until I started delving into AI.
But, I’ve always had a stubborn streak, and the idea of not fully understanding something has never sat well with me. I adopted the principle that no book in the library should scare me away from learning. So, I’ve been rolling up my sleeves, delving into the math behind AI, not at a super deep level, but deep enough to feel confident navigating through the jargon and equations.
As I’m journeying through this new learning curve, I’ve decided to write some blog posts. Consider them letters to my future self and to anyone who’s eager to decode the secrets of AI. And, my approach? I strive to simplify concepts, touch on key points, and use plenty of examples. So, let’s get started with Linear Regression.
Linear Regression is a predictive modeling technique used to predict a target variable based on one or more input features. It’s like drawing the best straight line through a scatter plot of data points that can best predict the output for any new input data.
The process of Linear Regression can be boiled down to these steps:
1. Initialize Parameters: We start with random values for the coefficients and bias in our model. Think of these as the starting point of our learning process.
2. Calculate Predictions: We calculate predictions using these coefficients and the feature values. It’s like a basic math equation. We multiply each coefficient with its corresponding feature and add them up to get the predicted value.
3. Calculate Errors: The error of a prediction is the difference between the predicted and the actual value. It’s like a reality check that tells us how far off our prediction was. We square these errors and average them out to get Mean Squared Error (MSE), a measure of how well our model is performing.
4. Update Parameters: To improve our model, we use a technique called Gradient Descent. Think of this as a navigation tool. It tells us which direction to adjust our coefficients in to minimize our error.
5. Iterate: We repeat this process, adjusting our coefficients each time until our model’s predictions are as good as they can get. It’s like refining a skill — with practice, we get better and better.
Understanding Linear Regression gave me a new perspective on machine learning. It’s not just about feeding data into a black box and getting predictions. It’s a systematic process of learning from errors and continuously improving. And the beauty of it? The principles of Linear Regression apply to many other machine learning models too!
So, if you’re starting your journey into AI, take your time with Linear Regression. It’s more than just a machine learning algorithm. It’s a foundational stone that will give you the insight and skills to explore the vast and exciting landscape of AI.
Keep learning, keep exploring!