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Most machine learning models work fine on their own — but what if we could make them work together and perform even better? That's exactly what Ensemble Learning does. In my latest blog, I dive into how combining multiple models (just like a band of musicians) can create smarter, stronger systems —

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Learn how a PID controller works with Python and C++ examples. Understand tuning, visualization, and real-world use in robotics and self-driving cars.

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Learn camera-based lane detection using Python & OpenCV. A hands-on guide for autonomous vehicle enthusiasts with code, tips, and ML insights.

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LiDAR vs. Cameras: Which one should self-driving cars trust? LiDAR brings laser-sharp 3D vision, while cameras rely on AI-powered perception. But can either work alone? Let’s break down the pros, cons, and why redundancy is the real MVP. 🚗💨 #SelfDrivingTech

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Transfer Learning Explained: Save Time and Improve Model Performance

Want faster, better machine learning? Transfer learning uses existing models to get great results, even with less data. Discover how!

Imagine you’re learning to play the guitar. Instead of figuring everything out from scratch, you watch an experienced musician, follow their techniques, and build upon their skills. That’s exactly how transfer learning works in the world of machine learning! Instead of training a model from the ground up (which takes forever and a lot of data), we take a model that has already learned from vast…

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Gradient Boosting explained: How to Make Your Machine Learning Model Supercharged using XGBoost

Ever wondered what happens when you mix XGBoost's power with PyTorch's deep learning magic? Learn how combining these two can level up your models, with XGBoost feeding predictions to PyTorch for a performance boost.

Ever felt like your model’s nearly perfect but needs that little extra “boost”? Your model is not too bad to be thrown away in trash but also not good enough to get a green signal for deployment. I mean, you can just deploy it anyway if it is the last day of your internship. But, for those you still like to keep their jobs, I’ve got exactly what you need for your “almost good” machine learning…

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Pathetic Programming 1: Creating a Random Excuse Generator with Python

You want to say no but can't think of any excuse? There is a program for this too. Here's a Python Excuse Generator that will generate an excuse for you to bail out of anything. Give it a try.

Welcome to the Pathetic Programming series where I intend to build programs with some silly and stupid goals. The idea was just to have fun with programming, not think much about efficiency and those warnings (as if we ever cared!), and code for some pathetic solutions that one would hardly need in the first place. So let us start with the very first exercise of Pathetic Programming series:…

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3 Practical SVM Examples to Boost Your Machine Learning Skills

Curious how to make the most of Support Vector Machines in machine learning? This blog dives into three must-try examples—Which example will you start with?

In the previous guide on Support Vector Machines, we understood the basic implementation and functioning of the machine learning classifier. In this post, I’ll walk you through three practical examples where we’ll use SVM for both classification and regression tasks. We start by a simple linear classification example and climb up to a more challenging non-linear classification in the second one.…

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My First Python App is Here! Meet the Productivity Tracker to Monitor Your Progress

There are days when we just do not want to do anything except lie down in bed all day. There are also days when we are completely on fire and doing our job in full-performance mode. And then, there are those days when we pretend to do our work but end up scrolling through the programming memes by Machine Learning Site on Instagram rather than doing actual work. Whether you are learning a new…

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3/100 Days of C++: Linking a Library in C++

A major project can consist of thousands of lines of code with several other libraries that are used as dependencies. The code, however, does not magically know the existence of functions or variables that are borrowed from those libraries. When we use any function from a library, the code needs to know its reference to understand how it is declared. In other words, the code wants to know what…

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5 Things You Need to Know About YOLOv11

YOLO (You Only Look Once) has long been a popular machine learning model for real-time object detection, offering an impressive balance between speed and accuracy. Since its existence, the model has evolved significantly, from early versions focusing on faster processing to more recent iterations that enhance precision and task versatility. YOLO’s hallmark is its ability to detect objects in a…

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Understanding Regularization in Machine Learning: Ridge, Lasso, and Elastic Net

Struggling with overfitting in your machine learning models? Have a look at this complete guide on Ridge, Lasso, and Elastic Net regularization. Learn these regularization techniques to improve accuracy and simplify your models for better performance.

A machine learning model learns over the data it is trained and should be able to generalize well over it. When a new data sample is introduced, the model should be able to yield satisfactory results. In practice, a model sometimes performs too well on the training set, however, it fails to perform well on the validation set. This model is then said to be overfitting. Contrarily, if the model…

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Choosing the Right Gradient Descent: Batch vs Stochastic vs Mini-Batch Explained

The blog shows key differences between Batch, Stochastic, and Mini-Batch Gradient Descent. Discover how these optimization techniques impact ML model training.

In my previous post on gradient descent, I explained briefly what gradient descent means and what mathematical idea it holds. A basic gradient descent algorithm involves calculating derivatives of the cost function with respect to the parameters to be optimized. This derivative is calculated over the entire training set as a whole. Now if the data has samples in hundreds of thousands, the…

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MSE vs RMSE: Choosing the Right Error Metric for Machine Learning

This blog provides an intuitive comparison between the metrics MSE and RMSE in machine learning.

When entering into the world of machine learning, one of the first concepts you encounter is error metrics or cost functions. These metrics help us understand the model performance by quantifying the difference between predicted values (commonly known as hypothesis) and actual values. Two commonly used metrics are Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). At first glance, these…

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How to Create a Header File in ROS Package?

Learn the importance of header files in ROS2 development. This guide walks you through setting up and configuring header files for a simple ROS package in C++.

It is not until you work on an important project that you realize the importance of small things in programming. For me, it is the header file in C++. “Why not declare the variables and functions in the .cpp file itself?” I kept asking myself which I now realize is stupid. Now, those who have read my past blogs might have realized that I work on a project involving automated mobility. An…

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Precision-Recall vs. ROC AUC Curve: Choosing the Right Metric for Your Machine Learning Model

Understand the essential metrics for binary classification with this comprehensive guide on precision-recall and ROC AUC curves.

In the previous blog on Confusion Matrix 101: Understanding Precision and Recall for Machine Learning Beginners, we understood the meaning of precision and recall which are important metrics while building a classifier model. Precision and recall share an inversed relationship, that is, if a model is optimized to have higher precision, the recall of it will decrease, and vice versa. Based on the…

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Confusion Matrix 101: Understanding Precision and Recall for Machine Learning Beginners

Evaluating classification models? Dive deep into confusion matrix and unlock the power of precision & recall for peak performance.

When we build a machine learning model, choosing the appropriate metric is the key factor for the feasibility of that model. This factor is only realizable when a model is implemented into a practical use-case. While accuracy remains the suitable metric for linear regression problems, it does not work well with classification problems. Instead, the performance of a classifier model is better…