Machine learning (ML) has become one of the most sought-after skills in the modern tech landscape, driving innovation in AI, data science, finance, healthcare, robotics and beyond. For learners who want to go beyond surface-level tutorials, the Applied Machine Learning (CS 5785) course from Cornell Tech stands out as a comprehensive, rigorous, and practical resource. … Read more
August 23, 2025 by Vanita.ai Natural Language Processing (NLP) powers applications like chatbots, translation systems, sentiment analysis, and large language models (LLMs). But before machines can understand text, it must be cleaned, structured and normalized. This is where text preprocessing comes in. Think of preprocessing as preparing raw ingredients before cooking without it, even the … Read more
August 23, 2025 by Vanita.ai In today’s world, data is growing faster than ever, and charts are no longer just static pictures in reports, they’re interactive stories that drive decisions. Whether you’re analyzing business trends, visualizing AI model outputs or preparing a dashboard for executives, the way you present data can make or break your … Read more
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