In the world of data science and machine learning, visualization is the bridge between raw numbers and meaningful insights. Among Python’s visualization tools, Matplotlib stands as the most widely used and versatile library. From quick exploratory data analysis plots to highly customized publication-ready graphs, Matplotlib provides the flexibility to do it all. This Matplotlib Cheatsheet … Read more
If you’re working in machine learning with Python, Scikit-learn (sklearn) is one of the most powerful and beginner-friendly libraries you’ll ever use. It provides tools for data preprocessing, model selection, evaluation and deployment. This Scikit-learn cheatsheet is your one-stop guide, covering everything from the basics to advanced techniques. Bookmark it and you’ll never get stuck … Read more
NumPy Cheatsheet 2025: From Basics to Advanced in One Guide
Working with data often means juggling messy files, cleaning inconsistencies, and reshaping tables until they’re analysis-ready. That’s where Pandas shines it’s the backbone of data manipulation in Python. But with hundreds of methods and options, it can be overwhelming to remember them all. This cheatsheet brings together the most essential Pandas methods, organized by category, … Read more
Artificial Intelligence is transforming industries at an unprecedented pace, and AI engineering is at the heart of this revolution. Whether you’re building large language models, designing machine learning systems, or deploying AI at scale, having a strong foundation is essential. One of the best ways to gain that foundation is by learning from the top … Read more
In the world of artificial intelligence, the way you ask a question often matters just as much as the answer you receive. With the release of GPT-5, OpenAI has delivered a model that excels in reasoning, instruction-following, and agentic workflows but to unlock its full potential, you need to master the art of prompting. This … Read more
In today’s fast-paced AI landscape, building a powerful large language model (LLM) is only half the battle. The real challenge lies in ensuring the model understands the right context to deliver accurate, relevant, and goal-aligned results. Without context, even the most advanced AI agents risk producing inconsistent and misleading outputs. Context Engineering is the process … Read more
Retrieval-Augmented Generation (RAG) is transforming how AI delivers accurate, context-rich answers by combining large language models with real-time data retrieval. Two leading tools in this space—LangChain and LlamaIndex—take very different approaches to achieving the same goal: smarter, more reliable AI outputs. In this blog, we’ll break down their strengths, differences, and best use cases, helping … Read more
If you’ve been exploring the world of Large Language Models (LLMs), you’ve probably noticed that while the possibilities are endless, finding well-structured, ready-to-use examples can be a challenge. You might stumble upon fragmented code snippets or one-off demos, but rarely do you find a single place that brings everything together in a usable format. That’s … Read more
Artificial Intelligence (AI) has moved from research labs into our daily lives, powering tools we use for work, study, and entertainment. At the heart of many modern AI applications are Large Language Models (LLMs) advanced algorithms capable of understanding, generating, and reasoning with human language. Models like GPT-4, Claude, Mistral, and LLaMA can do everything … Read more
Artificial Intelligence is moving at an unprecedented pace. Just a few years ago, chatbots and simple predictive models were at the cutting edge. Today, Large Language Model (LLM) Agents are redefining what’s possible delivering systems that can plan, decide, and act autonomously in real-world situations. Built on advanced models like GPT-4, Claude and LLaMA, these … Read more