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Thoughts on ML, Python, and the dev journey.
Supervised vs Unsupervised Learning: What's the Difference?
Learn the difference between supervised and unsupervised learning, how each method works, common algorithms, examples, advantages, limitations and when to use them.
Read: Supervised vs Unsupervised Learning: What's the Difference? →What Is Machine Learning? A Beginner's Guide
Learn what machine learning is, how it works, its main types, common algorithms, real-world applications, and how to start learning machine learning with Python.
Read: What Is Machine Learning? A Beginner's Guide →How to Train Neural Networks: A Practical Guide for Beginners
A step-by-step breakdown of how neural networks actually learn—covering prediction, loss, gradients, and parameter updates.
Read: How to Train Neural Networks: A Practical Guide for Beginners →How to Evaluate an AI Model: Beyond the Accuracy
Why accuracy isn't enough, and how we should actually evaluate AI models using precision, recall, F1 score, and real-world robustness.
Read: How to Evaluate an AI Model: Beyond the Accuracy →How Retrieval Augmented Generation Actually Works
A breakdown of RAG, from basic vector search to complex Agentic RAG, and why traditional retrieval sometimes fails.
Read: How Retrieval Augmented Generation Actually Works →How AI Is Shaping the Future of India's IT Giants
A look at how companies like TCS, Infosys, and Wipro are adapting to the AI revolution — and what it means for students entering the industry.
Read: How AI Is Shaping the Future of India's IT Giants →