Amit Yadav

Have you ever heard the saying, “Not everything in life is linear”? Well, the same applies to data. While linear models work great for straight-line relationships, most real-world data doesn’t fit neatly into that category. This is where Polynomial Regression steps in. It’s like giving your linear model a makeover, allowing it to bend, twist, […]

Introduction to Reinforcement Learning Imagine you’re teaching a dog to fetch a ball. You throw the ball, and every time the dog brings it back, you give it a treat. Over time, the dog learns that fetching the ball leads to a reward, so it repeats the behavior. This is the essence of reinforcement learning

Introduction to Reinforcement Learning (RL) You’ve probably heard the phrase, “Learning from experience is the best teacher.” Well, that’s essentially what Reinforcement Learning (RL) is all about—learning from interactions with an environment to make smarter decisions over time. So, let’s break it down: Reinforcement Learning is a branch of machine learning that focuses on how

Picture this: You’re scrolling through Netflix, unsure what to watch, and suddenly, a show you didn’t even know existed pops up in your recommendations. Somehow, it’s exactly what you’re in the mood for. That’s the magic of recommender systems at work. From Netflix to Amazon, and even the playlists Spotify curates for you, these systems

“Optimization is at the heart of machine learning.” Think about it: every time you train a model, you’re essentially trying to minimize errors and improve predictions, right? But here’s the deal—you can’t just throw data into an algorithm and hope for the best. The real magic happens during the optimization process, where you tweak and

In machine learning, the difference between success and failure can sometimes come down to a single choice—how you optimize your model. Imagine training a high-stakes deep learning model, and you realize the process is painfully slow, or worse, your results are inconsistent. This might surprise you, but the method you use to compute gradients can

What is Gradient Descent? “Optimization is at the heart of machine learning.” – That’s a quote you’ll often hear when diving into the world of training algorithms. At its core, gradient descent is the backbone of most optimization processes in machine learning. Simply put, it’s the algorithm that helps your model learn by minimizing the

What Is TensorFlow and Why Consider Alternatives? “The right tool for the job changes as often as the job itself.” – Let’s start with a simple truth: machine learning is a fast-paced world, and TensorFlow has been one of its most powerful tools. But as you’ll soon see, the landscape is far from one-size-fits-all. What

What is Bias and Variance? Before we jump into the complex terms, let me ask you this: Have you ever heard the saying, “Too much of anything is bad”? Well, in machine learning, this couldn’t be more accurate when it comes to bias and variance. Bias and variance are two competing sources of error in

Overview of Regression Techniques When it comes to predicting outcomes or uncovering relationships between variables, regression is your go-to tool. Whether you’re estimating house prices, forecasting sales, or figuring out the impact of advertising on product demand, regression models are the backbone of most predictive analysis. Now, you’ve probably come across some familiar ones—linear regression,