Machine Learning
Machine Learning
Prompt Engineering vs Fine-Tuning: Which One Should You Use in 2025? Paid Members Public
In today’s AI landscape, developers leveraging powerful large language models (LLMs) like GPT-4o, Claude 3, and Mistral face a critical decision: Should they rely on prompt engineering, or invest in fine-tuning? Both methods enable AI customization—but they differ significantly in: ✔ Cost (Fine-tuning requires more compute resources) ✔ Complexity (Prompt
LLMs Decoded: Architecture, Training, and How Large Language Models Really Work Paid Members Public
Learn how GPT, Claude, and Mistral actually work. This visual guide decodes LLM architecture, training, tokenization, and generation—perfect for developers, students, and AI enthusiasts.
The Complete Guide to Retrieval-Augmented Generation (RAG) in 2025 Paid Members Public
Discover how Retrieval-Augmented Generation (RAG) works in 2025. Build intelligent apps using OpenAI or local models like Mistral, with LangChain and FAISS—no fine-tuning required.
The Modern Beginner’s Guide to Machine Learning in 2025: Concepts, Tools, and Career Path Paid Members Public
Discover how to start your Machine Learning journey in 2025 with this beginner-friendly guide. Learn core concepts, top tools, real-world projects, and career paths in AI—perfect for students, developers, and tech enthusiasts.
MNIST Dataset Analysis (Part-1) Paid Members Public
MNIST database, alternatively known as the Mixed National Institute of Standards and Technology database. It is the collection of large Images dataset (70K Images) commonly used for testing of Machine Learning Classification algorithms.
Pandas in a Nutshell (Part-1) Paid Members Public
Welcome to another tutorial of Python. In this blog we learn about What is Pandas Installation of Pandas What is DataFrame? How to make DataFrames? Operations and Manipulations on DataFrame?
