AI Skills

🔁 K-Fold Cross Validation K-Fold exists to answer one honest question: Will this model work on unseen data? A single train/test split is unreliable, especially with small datasets. So K-Fold simulates multiple “future tests” using the same data. 🧠 What It Really Does Instead of one split, we: 🔀 Divide data into K folds 🔁 Train the model K times 📦 Each time: one fold validates, the rest train 📊 Average the scores Every sample gets validated once, which reduces evaluation noise and gives a more trustworthy estimate. Important: It improves evaluation, not the model itself. ⚠️ What People Often Miss 🚫 Do NOT use K-Fold as your final test. Keep a separate test set ⚖️ Use Stratified K-Fold for imbalanced classification. ⏳ Do NOT use standard K-Fold for time series. 📊 K = 5 or 10 is usually enough. ✅ In short K-Fold is just: A smart way to reuse limited data to simulate multiple real-world tests. No magic. Just careful evaluation.

📌 A comprehensive masterclass on Claude Code is available via this repository: https://github.com/luongnv89/claude-howto. This resource provides a detailed visual and practical guide for one of the most powerful tools for developers. The repository includes: • Step-by-step learning paths covering basic commands (/init, /plan) to advanced features such as MCP, hooks, and agents, achievable in approximately 11–13 hours. 📚 • An extensive library of custom commands designed for real-world tasks. • Ready-made memory templates for both individual and team workflows. • Instructions and scripts for: - Automated code review. - Style and standards compliance checks. - API documentation generation. • Automation cycles enabling autonomous operation of Claude without direct user intervention. ⚙️ • Integration with external tools, including GitHub and various APIs, presented with step-by-step guidance. • Diagrams and charts to facilitate understanding, suitable for beginners. 📊 • Examples for configuring highly specialized sub-agents. • Dedicated learning scripts, such as tools for generating educational books and materials to master specific topics efficiently. Access the full guide here: https://github.com/luongnv89/claude-howto

🔅 Deep Learning with TensorFlow: Insights and Innovations 📝 Explore the evolving world of deep learning with TensorFlow, including the basics of generative AI, with practical, hands-on examples. 🌐 Author: Isil Berkun 🔰 Level: Intermediate ⏰ Duration: 3h 6m 📋 Topics: TensorFlow, Deep Learning, Artificial Intelligence 🔗 Join Artificial intelligence for more courses

Key Nodes in n8n

Most people think AI automation is complex, but with n8n it comes down to just 7 building blocks.

1️⃣ Code Node → custom logic 2️⃣ HTTP Request → connect any API 3️⃣ Edit Fields → clean data 4️⃣ IF Node → conditional paths 5️⃣ Switch Node → handle multiple cases 6️⃣ Loop Over Items → process lists 7️⃣ Error Handling → keep workflows alive n8n makes it simple: drag, drop, connect.

🔅 Deep Learning: Getting Started 📝 Learn the basics of deep learning and get up and running with this technology. 🌐 Author: Kumaran Ponnambalam 🔰 Level: Intermediate ⏰ Duration: 1h 13m 📋 Topics: Deep Learning, Machine Learning, Artificial Intelligence 🔗 Join Machine Learning for more courses

🔅 Machine Learning Foundations: Linear Algebra 📝 Explore the fundamentals of linear algebra, the mathematical foundation of machine learning algorithms. 🌐 Author: Terezija Semenski 🔰 Level: Intermediate ⏰ Duration: 1h 21m 📋 Topics: Linear Algebra, Machine Learning, Artificial Intelligence 🔗 Join Machine Learning for more courses

📊 Scikit-learn is your go-to Python library for building machine learning models — fast, flexible, and beginner-friendly! Whether you're tackling classification, regression, clustering, or dimensionality reduction, it has all the tools you need. 💡 Built on NumPy, SciPy, and matplotlib, it makes tasks like model training, cross-validation, and evaluation super smooth.

AI Agent Engineering: Why Good Principles Surpass Raw Power 🤖✨ https://go.nzoko.com/srBGK