Learning Resources & Tools
That Push The Boundaries of AI
Industry-leading courses and practical resources for students and professionals pioneering the future of AI.
Most Popular Courses
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
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.
Advanced Transformer Architecture
Master the theory, mathematics, and implementation of advanced Transformer architectures for modern LLMs.
Approx. 30 hours
Deep Learning & Python Proficiency
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
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
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
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
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
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
Data Visualization with Matplotlib and Seaborn
Create insightful and customized plots using Python's essential Matplotlib and Seaborn libraries.
Approx. 12 hours
Basic Python helpful
Introduction to Machine Learning
Understand fundamental machine learning concepts and apply basic algorithms to build simple models.
Approx. 14 hours
Basic Python helpful
Introduction to Feature Engineering
Learn to prepare, create, and select impactful features to improve machine learning model performance.
Approx. 15 hours
Basic Python, Pandas required
Trusted by Machine Learning Students and Professionals
Courses, references, and tools are utilized and cited by top universities and industry-leading tech companies worldwide.
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
Read NowRecent Articles & Insights
Jun 20, 2025
List for NVIDIA RTX 50 series GPU for running large language models locally. Discover the best LLMs for every card, master quantization, and optimize performance for privacy and speed.
Jun 19, 2025
Discover how AI and machine learning priorities differ across the globe. We analyze our user data to reveal the specific tools, techniques, and challenges that engineers from the US, India, China, Germany, and more are focused on right now.
Jun 16, 2025
Learn how to critically evaluate LLM benchmarks and choose the right model for your specific coding needs with our step-by-step guide.
May 24, 2025
Choosing between PyTorch and TensorFlow? This guide details 5 differences covering API design, graph execution, deployment, and community, helping ML engineers select the optimal framework for their projects.
May 24, 2025
Understand the GGUF file format, its architecture, benefits for LLM inferencing, and its role in local model deployment. This guide offers technical professionals essential knowledge for creating, quantizing, and utilizing GGUF files effectively.
May 23, 2025
Discover 5 Proximal Policy Optimization (PPO) variants designed to elevate your Reinforcement Learning from Human Feedback (RLHF) pipelines. This technical guide explains how these modifications address common PPO limitations, leading to better LLM alignment and performance.
May 22, 2025
Selecting the right database is fundamental for building high-performing RAG applications. This guide explores essential criteria, compares database types (vector-native vs. extended traditional DBs), and provides insights to help developers and ML engineers choose the optimal solution for vector search, scalability, and low-latency retrieval.
May 20, 2025
Understand how effective chunking transforms RAG system performance. Explore various strategies, from fixed-size to semantic chunking, with practical code examples to help you choose the best approach for your LLM applications and improve context retrieval.