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Popular Guides

How To Build A Large Language Model

Acquire the engineering skills to construct, train, and optimize sophisticated large language models.

Approx. 80 hours

Programming and Deep Learning

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Advanced Transformer Architecture

Master the theory, mathematics, and implementation of advanced Transformer architectures for modern LLMs.

Approx. 30 hours

Deep Learning & Python Proficiency

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LangChain for Production-Ready LLM Applications

Develop and operationalize complex, scalable LLM applications using advanced LangChain features and best practices.

Approx. 32 hours

Python & Basic LangChain

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Python Programming Fundamentals

Acquire the core Python skills needed to write clear, functional code and begin your programming path.

Approx. 20 hours

No prior programming experience.

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Practical Quantization for Large Language Models

Implement LLM quantization techniques (PTQ, QAT, GPTQ, GGUF) to reduce model size and improve inference speed.

Approx. 15 hours

LLM Fundamentals & Python

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Getting Started with PyTorch

Build and train fundamental deep learning models using PyTorch's core features like tensors, autograd, and neural network modules.

Approx. 18 hours

Basic Python & ML knowledge

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Python for LLM Workflows: Tooling and Best Practices

Build and manage LLM applications using Python, LangChain, LlamaIndex, and essential development practices.

Approx. 18 hours

Intermediate Python skills

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Advanced PyTorch

Build, optimize, and deploy complex deep learning models using PyTorch's advanced capabilities.

Approx. 36 hours

Intermediate PyTorch & DL concepts

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Mastering Gradient Boosting Algorithms

Effectively implement, tune, and interpret advanced gradient boosting models for sophisticated machine learning applications.

Approx. 28 hours

Python & ML Fundamentals

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Time Series Analysis and Forecasting

Analyze time-dependent data and build statistical forecasting models like ARIMA and SARIMA.

Approx. 15 hours

Basic Python and Pandas

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RLHF: Reinforcement Learning from Human Feedback

Apply Reinforcement Learning from Human Feedback (RLHF) principles and techniques to align large language models.

Approx. 24 hours

Advanced ML & DL knowledge

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Advanced Reinforcement Learning Techniques

Implement and apply advanced reinforcement learning algorithms to solve complex sequential decision-making challenges.

Approx. 70 hours

Python, ML & RL Fundamentals

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MASTERCLASS

HOW TO BUILD A
LARGE LANGUAGE MODEL

30 Chapters, 700+ Pages of In-Depth Content

Guide to understanding and building state-of-the-art language models

Prerequisites: Strong foundations in programming and deep learning

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Recent Articles & Insights

30 Precise Terms to Use Instead of 'AI'

Oct 6, 2025

How to stop saying 'AI' for everything. This guide gives you 30 specific machine learning terms that you can use to demonstrate proficiency.

How to Connect Claude to an MCP Server

Sep 26, 2025

Learn what the Model Context Protocol (MCP) is and follow our step-by-step guide to connect Claude to an external MCP server, giving it access to live data and powerful tools.

How To Build a MCP Server with FastAPI (FastAPI-MCP)

Sep 25, 2025

Learn to build a secure and scalable Model Context Protocol (MCP) server using the fastapi_mcp library. This step-by-step guide covers setup, authentication, and integration with AI tools like Claude, turning your APIs into a powerful toolkit for large language models.

Is Fine-Tuning Your LLM Worth It? Usually, It Isn't

Sep 18, 2025

Before you spend a fortune fine-tuning an LLM, discover faster, cheaper, and often more effective methods: prompt engineering and RAG. Learn why fine-tuning should be your last resort.

XGBoost vs. LightGBM vs. CatBoost

Sep 18, 2025

Learn which gradient boosting model to choose for speed, accuracy, and handling categorical data, with code examples and diagrams to guide you.

Building with LLMs: A Machine Learning Course Recommendation Engine

Sep 13, 2025

This complete guide details how to build a sophisticated course recommendation engine using LLMs, vector embeddings, and advanced semantic search. Learn data enrichment, prompt engineering, vector database implementation, and the logic for creating truly personalized learning paths.

GPU System Requirement Guide for Kimi K2

Jul 12, 2025

Essential GPU and VRAM requirements for running Moonshot AI's Kimi LLM variants. This guide provides the specific hardware setups you need, from base models to Q4 quantized versions, to get started with this powerful AI.

The Best Local LLMs To Run On Every Mac (Apple Silicon)

Jul 4, 2025

List of the best local LLMs for Apple Silicon Macs, optimized for your specific RAM configuration.