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Unsupervised Learning in Machine Learning: Definition, Examples, and Applications

 Learn what unsupervised learning is, how clustering works, and see real-world examples like Google News grouping, DNA data analysis, and customer segmentation. What Is Unsupervised Learning? After supervised learning, the second most widely used type of machine learning is unsupervised learning . Unlike supervised learning—where algorithms are trained on labeled data (inputs X with known outputs Y )—unsupervised learning works with unlabeled data . The algorithm is not given the "right answers." Instead, it must discover patterns, structures, or groupings within the data on its own. How Is It Different from Supervised Learning? Feature Supervised Learning Unsupervised Learning Input Data Labeled (X → Y pairs) Unlabeled (X only) Goal Predict outputs (labels) Find structure, clusters Example Spam detection Customer segmentation Clustering: The Most Common Unsupervised Learning Technique One of the most popular forms of unsupervised learning is clustering , where a...
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Supervised Learning in Machine Learning: Regression vs Classification Explained

Meta Description: Discover what supervised learning is, understand the difference between regression and classification algorithms, and see real-world examples like breast cancer detection, spam filtering, and price prediction. What Is Supervised Learning? Supervised learning is a core technique in machine learning (ML) where algorithms learn to map inputs (X) to outputs (Y) using labeled data. The system is trained on examples where the correct answer is already known. Once trained, it can make predictions on new, unseen data. Two major types of supervised learning algorithms are: Regression – Predicts continuous numerical values. Classification – Predicts categories or discrete labels. This post focuses on classification , a powerful method widely used in healthcare, security, finance, and beyond. Classification Algorithms: Predicting Categories, Not Numbers In regression, algorithms predict numbers from infinitely many possibilities (e.g., house prices). Class...
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What Is Supervised Learning? A Complete Beginner’s Guide to the Most Powerful Machine Learning Technique

Meta Description: Learn what supervised learning is, how it works, real-world examples (spam filters, self-driving cars, advertising), and why it drives 99% of machine learning’s economic value. Introduction: Why Supervised Learning Matters in Machine Learning Machine learning (ML) is transforming industries worldwide. But did you know that supervised learning accounts for nearly 99% of machine learning’s current economic value ? In this beginner-friendly guide, we’ll explain: What supervised learning is How it works Real-world applications Types (regression vs. classification) Why it’s critical for AI success By the end, you’ll understand why supervised learning is the foundation of most practical machine learning systems today. What Is Supervised Learning? Supervised learning is a type of machine learning where algorithms learn to map inputs (X) to outputs (Y) by studying labeled examples. Input (X): The data you provide. Output (Y): The correct l...
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What is Machine Learning

  What Is Machine Learning? A Beginner-Friendly Introduction Machine learning (ML) is one of the most transformative technologies in today’s world. But what exactly does it mean, and when should you consider using it? Let’s break it down. The Classic Definition of Machine Learning Arthur Samuel, a pioneer in the field, defined machine learning as: “The field of study that gives computers the ability to learn without being explicitly programmed.” In simpler terms, rather than writing step-by-step instructions for a computer to follow, we design algorithms that allow the computer to learn patterns and improve its performance automatically based on experience. A Historical Example: Teaching a Computer to Play Checkers Back in the 1950s, Arthur Samuel developed a checkers-playing program. Interestingly, Samuel himself wasn’t a strong checkers player. So, how did the program become good at the game? He let the computer play tens of thousands of games against itself . Over time...
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  Learn effectively on this course This short guide is here to support you as you go through the course, especially if you're studying on your own. You'll find some simple, research-informed strategies to help you stay motivated and get more out of your study time, without feeling overwhelmed.  Staying motivated Many students say they need motivation to learn, but motivation is unpredictable – it comes and goes. What really keeps you moving forward is building habits. Creating small, manageable learning routines helps you to stay on track even when you're not feeling particularly inspired. Start by keeping your study sessions short and focused, just enough to get into the flow without feeling overwhelmed. You can try using the pomodoro technique (25 minutes of focused work followed by a 5-minute break) to stay on track. Celebrate small wins along the way – progress is more powerful than perfection. Even completing one item is a success and helps build momentum.  To make...
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  Course structure and navigation Welcome to Machine Learning for ALL and thank you for joining us. This course has been developed by University of London Worldwide in collaboration with Goldsmiths, University of London, and we’re excited to share it with a global community of learners. Whether you’re here to develop your knowledge, develop new skills or explore a personal interest, we hope the journey ahead will be both rewarding and enjoyable. What this course offers In this course, you’ll be guided through the topic step-by-step, using a mix of videos, readings, practical exercises, discussions and opportunities for reflection. While there’s no live tutor support, you won’t be learning in isolation. Peer interaction and community learning are central to the course experience, and we encourage you to connect with others and learn together. Before you get started, make sure to check the course page for a weekly overview of the topics you’ll be covering, and visit the grades page t...
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