ML Compass Guide

ML Compass Guide is your hands-on guide to the world of machine learning. It helps you quickly understand how algorithms work and when to use regression, classification or clustering. You will also explore neural networks, reinforcement learning, and key metrics that show how to measure model performance. Everything comes with simple decision paths and real-world examples. The site is constantly expanding, so you will always find new tips, ideas, and inspiration for your projects.

How to Use This Site

Start by selecting the Machine Learning card below. It’s the starting point of the knowledge map. From there, you can explore different branches like Supervised Learning, Unsupervised Learning, and Reinforcement Learning. Each branch contains various algorithms, each with its own decision path. Follow the paths to learn about the algorithms, their use cases, and how they can be applied to your projects.

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Machine Learning

Machine learning is a field of artificial intelligence that focuses on the development of algorithms and statistical models that enable computers to perform tasks without explicit instructions, relying instead on patterns and inference.

Machine learning is a dynamic field of computer science and artificial intelligence that enables systems to automatically learn and improve from experience without being explicitly programmed. It revolves around developing algorithms that can identify patterns in data, adapt to new inputs, and make decisions with minimal human intervention. These models rely on large datasets and statistical techniques to uncover hidden structures or predictive insights. One of the most compelling aspects of ML is its ability to generalize, which means it can perform well on new, unseen data after being trained on a smaller representative sample.

Use Case Examples:

• Email Spam Classification: Given a dataset of emails labeled as spam or non-spam, classify new emails as spam or non-spam.
• Predicting Housing Prices: Given features like size, location, and number of bedrooms in dataset of houses, predct their selling prices.
• Handwritten Digit Recognition: Given images of hardwritten digits along with their labels (0-9), classify new images into the correct digit category.
• Customer Churn Prediction: Given customer data and their churn status (churned or active), predict whitch customers are likely to churn in the future.
• Fault Detection in Circuits: Given sensor data from different components of a circuit, classify whether the circuit is faulty or functioning properly.