ML & AI
Master Machine Learning & Artificial Intelligence: Unlock the Power of Data and Automation
Step into the world of Machine Learning (ML) and Artificial Intelligence (AI) with our in-depth courses designed for all skill levels. Learn the key concepts, algorithms, and techniques behind ML and AI, and how they’re transforming industries such as healthcare, finance, and technology. From supervised and unsupervised learning to deep learning and neural networks, our courses provide hands-on experience with the latest tools and frameworks. Whether you’re a beginner or an advanced professional, develop the expertise to build intelligent systems and drive innovation with ML & AI.
The Top Open-Source RAG Frameworks to Know in 2025: Build Smarter AI with Real-World Context
Retrieval-Augmented Generation (RAG) is quickly redefining how we build and deploy intelligent AI systems. It isn’t a replacement for large language models (LLMs)—it’s the missing piece that makes them useful in real-world settings. With hallucinations, outdated knowledge, and limited memory being persistent LLM issues, RAG introduces a smarter approach: retrieve factual information from reliable sources,…
Understanding Different Types of LLMs: Distilled, Quantized, and More – A Training Guide
Large Language Models (LLMs) come in various optimized forms, each designed for specific use cases, efficiency, and performance. In this guide, we’ll explore the different types of LLMs (like distilled, quantized, sparse, and MoE models) and how they are trained. In the fast-evolving world of Large Language Models (LLMs), different model types serve different performance and deployment goals…
DeepSeek vs ChatGPT: A Technical Deep Dive into Modern LLM Architectures
The large language model (LLM) landscape is rapidly evolving, and two powerful contenders—DeepSeek and ChatGPT—are emerging as core engines in generative AI applications. While they both excel at generating human-like text, answering questions, and powering chatbots, they differ significantly in architecture, training objectives, inference capabilities, and deployment paradigms. Not long ago, I had my first…
Top 30 Machine Learning Interview Questions For 2025
Basic Machine Learning Interview Questions
Basic questions are related to terminologies, algorithms, and methodologies. Interviewers ask these questions to assess the technical knowledge of the candidate.
How to Crack Machine learning Interviews at FAANG!
As a candidate, I’ve interviewed at a dozen big companies and startups. I’ve got offers for machine learning roles at companies including Google, NVIDIA, Snap, Netflix, Primer AI, and Snorkel AI. I’ve also been rejected at many other companies. As an interviewer, I’ve been involved in designing and executing the hiring process at NVIDIA and…
Foundations of Large Language Models: Understand How LLMs Like GPT Work
Large language models originated from natural language processing, but they have undoubtedlybecome one of the most revolutionary technological advancements in the field of artificial intelligence in recent years. An important insight brought by large language models is that knowledgeof the world and languages can be acquired through large-scale language modeling tasks, andin this way, we…
How Learning to Build AI Agents Changed My Life (And How It Can Change Yours Too)
Just two years ago, I had zero experience in artificial intelligence. I didn’t come from a technical background, and I didn’t know how to code. But I taught myself how to build AI agents—one step at a time. Fast forward to today, I’ve: Generated over $5 million in revenue through multiple AI-focused businesses Grown my YouTube channel…
How to Build a Data Warehouse from Scratch
How to Build a Data Warehouse from Scratch: Cost + Examples Building a data warehouse from scratch can seem overwhelming, but it is a game-changer for organizations aiming to harness data for informed decision-making. While the initial investment in time and resources might be substantial, the long-term benefits—such as improved data quality, actionable insights, and…
What is Retrieval-Augmented Generation (RAG)?
Retrieval Augmented Generation (RAG) is a technique for augmenting Large Language Model (LLM) knowledge with additional data. In a standard Gen-AI application using LLM as its sole knowledge source, the model generates responses solely based on the input from the user query and the knowledge it has been trained on. It does not actively retrieve…
How can someone detect that my write-up was AI generated?
If you’re wondering how someone might detect that your writing was AI-generated, there are a few key indicators they might look for. These often come from patterns or characteristics that AI-generated text tends to exhibit, which can set it apart from human writing. Let’s dive into some of these signs, along with ways to make…