Machine Learning.

Practical machine learning for people who ship: algorithm choices, evaluation that survives production, and the habits that separate working models from demos. Pair these posts with our algorithm selector and its 21 engineering dossiers.

01Scaling Laws in AI: How Model Growth Predicts PerformanceScaling laws explain how model size, data, and compute shape AI performance. This primer covers power laws, compute-optimal training, limits, and practice.29 min read 02LLMs vs World Models: When Each Learns and PlansCompare LLMs and world models for prediction, planning, and control. Learn how to evaluate data needs, grounding, and failure modes.24 min read 03Implementing K-Means Clustering: A Beginner’s Guide to Unsupervised LearningIntroduction to Unsupervised Learning Unsupervised learning is a category of machine learning where algorithms learn patterns from data without any labeled outcomes or explicit instructions on what to predict (Supervised vs. Unsupervised Learning: What’s the Difference? | IBM). I17 min read 04Reinforcement Learning: A Beginner’s GuideIntroduction to Reinforcement Learning Reinforcement learning (RL) is a powerful branch of machine learning that has gained significant attention in recent years. Unlike supervised and unsupervised learning, RL focuses on learning through interaction with an environment. In this 20 min read 05Uncovering the Power of Density-Based Clustering with DBSCANIntroduction to Density-Based Clustering Density-based clustering is a powerful unsupervised machine learning technique that aims to identify dense regions of data points and group them into clusters. Unlike other clustering algorithms like K-means, which require specifying the n7 min read 06Introduction to K-Means ClusteringIn the vast realm of machine learning, K-means clustering stands out as a fundamental unsupervised learning algorithm. Its simplicity and effectiveness have made it a go-to choice for data scientists and analysts alike. In this comprehensive blog post, we’ll dive deep into the wo8 min read 07A Guide to Overfitting and Underfitting in Machine Learning1. Introduction to Overfitting and Underfitting When training machine learning models, one of the key challenges is balancing model complexity. Models that are too simple may struggle to detect the underlying patterns in the data. This is called underfitting. On the other hand, m7 min read 08The Amazing World of Machine LearningThe Marvelous World of Machine Learning Machine Learning (ML) has become the buzzword of the 21st century. You’ve probably heard it mentioned in tech circles, on the news, or even at the water cooler. But what exactly is this mysterious concept that seems to hold the keys to the 6 min read 09Linear Regression: Simplifying Data PredictionsIntroduction to Linear Regression Linear regression is one of the most popular and commonly used machine learning algorithms. It is a statistical method used to predict continuous, numeric values based on historical data. Regression analysis involves modelling the relationship be5 min read 10An Introduction to Gradient DescentWhat is Gradient Descent? Gradient descent is an optimization algorithm that is commonly used to minimize cost functions and train machine learning models. It works by taking steps in the direction of steepest descent, which means moving towards lower values of the cost function.5 min read