Into The World of AI/ML

What Is Linear Regression? Linear regression is one of the simplest and most widely used machine learning algorithms. It looks at the available data and tries to figure out the overall trend that describes all the data — especially when the data points are linearly related (when one increases or decreases, the other follows a similar pattern).  This trend is shown as a straight line and it helps in making future predictions. Why the Name “Linear Regression”? The term linear refers to a straight line. In this algorithm, the data features are linearly related — meaning, as one feature changes, the other changes in a consistent way (not always exact, but with a similar trend). The word regression has its roots in statistics and history. The concept was first introduced by Sir Francis Galton in the late 1800s while studying the relationship between parents' heights and their children's heights. He observed that tall parents tended to have tall children, but the children weren’...

A Responsible Leader — Leadership Is More Than Just a Position Sharing some thoughts I believe many of us can relate to. Just because we’re promoted to a leadership position— Does that really make us a leader? Did we truly deserve it? Did we ask for it? Or was it simply the need of the hour? Whatever the reason, we became a “leader.” At least, that’s what the title says. But here’s the real question— Once we’re in that role, do we ever pause and reflect on what we’re really doing with it? Do we actually justify the responsibility we’ve been given? Not just in delivering projects or achieving results— But in the part that’s often left unspoken: How we treat people, the culture we shape, the energy we transmit. It’s easy to take credit when things go right—and yes, we should. We’ve put in the hard work. But what happens when things go wrong? Do we take ownership? Or do we pass the blame—just to keep our image clean in front of higher-ups? These situations say a lot ab...

Let's Explore What a Machine Learning Model, Really Is When we say a machine has “learned,” what does that really mean? And what exactly is a machine learning model?  Think of it this way: just like students learn from books and lectures, machines learn from data using algorithms. The result of that learning is what we call a model —something the machine can reuse to make predictions or decisions.  In this blog, we’ll break down what a model really is, how it’s built, how it differs from an algorithm, and how it’s used in real-world applications.  Learning Once, Predicting Many Times Each time a machine makes a prediction, it shouldn't have to learn from scratch. Just like humans, once it has learned something, it should reuse that learning when needed. This reusable knowledge is what we call a  model. This  is the outcome of the learning process. What exactly is a model and how can we visualize it? A model is essentially a mathematical representation of wh...

We know that Machine learning is all about math. To truly understand ML we need to understand several key mathematical concepts: Linear Algebra, Calculus, Probability & Statistics, Optimization, Discrete Mathematics & Logic, Information theory But why are these concepts important? Before diving into the mathematical depth of AI/ML let's first understand the basics of  Machine learning. The most important ingredient of machine learning is  data .    “No data, no ML.”    To build a Machine Learning model, the first step is to define a  goal —like predicting house prices, recognizing faces in photos, or classifying emails. Then we need some  data,  an algorithm to learn from the data, and an optimization technique to fine-tune the model parameters. All of this leads us to a well-performing model ready for deployment and capable of making predictions on new data. Let's visualize the AI/ML pipeline: Data → Algorithm → Optimization → M...