Industry-leading courses and practical resources for students and professionals pioneering the future of AI.
Acquire the engineering skills to construct, train, and optimize sophisticated large language models.
Approx. 80 hours
Programming and Deep Learning
Master the theory, mathematics, and implementation of advanced Transformer architectures for modern LLMs.
Approx. 30 hours
Deep Learning & Python Proficiency
Develop and operationalize complex, scalable LLM applications using advanced LangChain features and best practices.
Approx. 32 hours
Python & Basic LangChain
Acquire the core Python skills needed to write clear, functional code and begin your programming path.
Approx. 20 hours
No prior programming experience.
Implement LLM quantization techniques (PTQ, QAT, GPTQ, GGUF) to reduce model size and improve inference speed.
Approx. 15 hours
LLM Fundamentals & Python
Build and manage LLM applications using Python, LangChain, LlamaIndex, and essential development practices.
Approx. 18 hours
Intermediate Python skills
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
Analyze time-dependent data and build statistical forecasting models like ARIMA and SARIMA.
Approx. 15 hours
Basic Python and Pandas
Apply Reinforcement Learning from Human Feedback (RLHF) principles and techniques to align large language models.
Approx. 24 hours
Advanced ML & DL knowledge
Implement and apply advanced reinforcement learning algorithms to solve complex sequential decision-making challenges.
Approx. 70 hours
Python, ML & RL Fundamentals
Effectively implement, tune, and interpret advanced gradient boosting models for sophisticated machine learning applications.
Approx. 28 hours
Python & ML Fundamentals
Build, optimize, and deploy complex deep learning models using PyTorch's advanced capabilities.
Approx. 36 hours
Intermediate PyTorch & DL concepts
Courses, references, and tools are utilized and cited by top universities and industry-leading tech companies worldwide.
MASTERCLASS
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 NowJul 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.
Jul 4, 2025
List of the best local LLMs for Apple Silicon Macs, optimized for your specific RAM configuration.
Jul 3, 2025
Evaluate 7 different practical statistical methods for distinguishing between human and AI-generated text. This guide provides an in-depth analysis of metrics, compares the effectiveness of different Llama models, and offers code examples.
Jul 3, 2025
Guide to the specific GPU VRAM requirements for running all variants of Baidu's new Ernie 4.5, from the small 0.3B to the largest 424B model
Jun 27, 2025
GPU and RAM requirements for Gemma 3n, Google's cutting-edge on-device AI model. Learn how its innovative architecture redefines efficient AI deployment.
Jun 23, 2025
The AI Engagement Index ranks nations by their engagement in technical AI content, offering a fresh perspective on the global AI adoption.
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.