Build Your AI Knowledge, Comprehensive Courses
for Students & Practitioners
Structured learning paths made to take you from fundamental principles to advanced techniques in modern AI
Featured Courses
Master writing SQL queries to retrieve, filter, aggregate, and join data from relational databases for analysis tasks.
Approx. 7 hours
No prior knowledge required.
Understand core database concepts, models (relational, NoSQL), and basic SQL querying.
Approx. 8 hours
No prior knowledge needed
Gain the ability to design and understand basic ETL processes for moving and preparing data.
Approx. 8 hours
Basic computer literacy
Understand core data engineering principles for collecting, storing, processing, and managing data.
Approx. 15 hours
Basic computer literacy
Create insightful and customized plots using Python's essential Matplotlib and Seaborn libraries.
Approx. 12 hours
Basic Python helpful
Grasp fundamental data science principles and apply basic analysis and visualization techniques.
Approx. 12 hours
No prior knowledge required
Acquire the skills to clean and structure messy data, ensuring accuracy for analysis and machine learning tasks.
Approx. 6 hours
Basic data concepts
Grasp the fundamentals of Large Language Models and learn how to communicate with them effectively through prompts.
Approx. 7 hours
No specific prerequisites
Grasp how computers process images and perform basic tasks like feature detection.
Approx. 9 hours
Basic programming helpful
Understand fundamental machine learning concepts and apply basic algorithms to build simple models.
Approx. 14 hours
Basic Python helpful
Make your trained machine learning models usable by deploying them as simple prediction services.
Approx. 7 hours
Python and ML Basics
Confidently select, calculate, and interpret essential metrics to evaluate classification and regression model performance.
Approx. 4 hours
Basic ML concepts
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Comprehensive Content
Detailed material covering theory and practical aspects, suitable for academic study.
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Carefully organized courses and paths to guide your learning from start to finish.
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Clear explanations designed to make even complex AI topics understandable.
Recent Articles & Insights
May 1, 2025
Stop assuming MoE models automatically mean less VRAM or faster speed locally. Understand the real hardware needs and performance trade-offs for MoE LLMs.
Apr 23, 2025
Accurately estimate the VRAM needed to run or fine-tune Large Language Models. Avoid OOM errors and optimize resource allocation by understanding how model size, precision, batch size, sequence length, and optimization techniques impact GPU memory usage. Includes formulas, code examples, and practical tips.
Apr 18, 2025
Learn 5 key LLM quantization techniques to reduce model size and improve inference speed without significant accuracy loss. Includes technical details and code snippets for engineers.
Apr 18, 2025
Struggling with TensorFlow and NVIDIA GPU compatibility? This guide provides clear steps and tested configurations to help you select the correct TensorFlow, CUDA, and cuDNN versions for optimal performance and stability. Avoid common setup errors and ensure your ML environment is correctly configured.
Apr 18, 2025
Discover the optimal local Large Language Models (LLMs) to run on your NVIDIA RTX 40 series GPU. This guide provides recommendations tailored to each GPU's VRAM (from RTX 4060 to 4090), covering model selection, quantization techniques (GGUF, GPTQ), performance expectations, and essential tools like Ollama, Llama.cpp, and Hugging Face Transformers.
Apr 18, 2025
Learn the practical steps to build and train Mixture of Experts (MoE) models using PyTorch. This guide covers the MoE architecture, gating networks, expert modules, and essential training techniques like load balancing, complete with code examples for machine learning engineers.
Apr 17, 2025
Understand the core differences between LIME and SHAP, two leading model explainability techniques. Learn how each method works, their respective strengths and weaknesses, and practical guidance on when to choose one over the other for interpreting your machine learning models.
Apr 15, 2025
Transformer models can overfit quickly if not properly regularized. This post breaks down practical and effective regularization strategies used in modern transformer architectures, based on research and experience building large-scale models.