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Simplify predictions on structured data. Build and deploy models quickly when you need fast analysis, less code.
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Learn the essential Structured Query Language (SQL) skills needed for data science tasks. This course provides a solid foundation in querying relational databases to retrieve, filter, aggregate, and join data for analysis.
Understand the fundamentals of databases and data management systems. This course covers core concepts, the differences between relational (SQL) and NoSQL databases, and how to interact with data using basic SQL commands. Learn essential principles for storing, organizing, and retrieving information effectively. Suitable for individuals starting with data management.
Learn to create informative and attractive data visualizations in Python using the Matplotlib and Seaborn libraries. This course provides a practical introduction to plotting fundamentals, common chart types, and customization techniques, essential skills for any data analysis or AI engineering task. Start building foundational visualization skills today.
This course provides a foundational understanding of data science concepts and techniques. Learn the core principles, how to work with data, perform basic analysis, and visualize findings. Designed for individuals starting their path in data-driven fields or AI engineering.
Learn the fundamental techniques for cleaning and preparing data for analysis and machine learning. This course covers identifying and handling common data quality issues, ensuring your data is accurate and ready for use.
A foundational course explaining how computers can interpret digital images and videos. Learn the fundamental concepts and basic techniques used in computer vision systems, starting from image basics to introductory object recognition.
This course provides a foundational understanding of machine learning concepts and techniques. Learn what machine learning is, how it differs from traditional programming, and the basic types of machine learning algorithms. Get acquainted with fundamental concepts like data, features, models, and evaluation. Build simple models for regression, classification, and clustering using practical examples.
Understand the fundamentals of Large Language Models (LLMs). Learn what they are, how they process language, and how to interact with them effectively. This course provides a practical foundation for anyone new to AI language technology.
Learn the fundamental concepts and techniques for deploying machine learning models. This course covers preparing models for production, creating simple prediction services, and understanding basic deployment patterns, enabling you to make your trained models accessible and useful.
Blog
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.
Apr 15, 2025
Learn the most effective prompt engineering techniques recommended by Google. Includes actionable examples and clear dos and don’ts to improve your prompts.