⚠️ AI Safety: Navigating the Risks of Advanced AI
A comprehensive look at AI safety concerns, from current challenges to future risks, with practical approaches to building safer AI systems.
Archive
machine-learning-series
🧩 Mixture of Experts: Many Brains Are Better Than One
Learn how Mixture of Experts (MoE) works like a team of specialists, each handling what they do best, to solve complex problems.
🤔 Chain of Thought: Teaching AI to Think Step by Step
Discover how Chain of Thought prompting helps AI break down complex problems into simple steps, just like humans do.
🧠 Deep Learning: Understanding Neural Networks
Dive into the world of neural networks and deep learning, from basic concepts to practical implementation, with clear examples and intuitive explanations.
📊 Model Evaluation: Beyond Accuracy (Intermediate Series)
Learn why accuracy alone isn't enough and discover the key metrics for properly evaluating machine learning models.
🔧 Machine Learning Intermediate: The Art of Feature Engineering
Discover how to transform raw data into meaningful features that improve your model's performance, with practical examples and best practices.
🔄 Cross-Validation and Overfitting (Intermediate Series)
Learn how to properly validate your machine learning models and avoid the pitfalls of overfitting.
🧩 Machine Learning Basics: Understanding Unsupervised Learning (Part 4)
Learn how unsupervised learning algorithms discover hidden patterns and group similar items together, with real-world examples and applications.
🎮 Machine Learning Basics: Understanding Reinforcement Learning (Part 5)
Explore how machines learn through trial and error, just like humans do, with practical examples from gaming to robotics.
📈 Machine Learning Basics: Understanding Regression (Part 2)
Understand how regression works in machine learning through practical examples, from predicting house prices to estimating sales.
🎯 Machine Learning Basics: Understanding Classification (Part 3)
Discover how classification in machine learning works through simple examples, from email spam detection to image recognition.
🤖 Machine Learning Basics: A Beginner's Guide (Part 1 - Introduction)
A beginner-friendly introduction to machine learning concepts, explained with real-world examples and minimal technical jargon.
🧹 Data Science Fundamentals: Data Preparation and Model Fitting
Learn about data cleaning, deduplication, and how to split your data properly to build effective machine learning models.