What Are Machine Learning Algorithms_.pdf
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Here are the top 4 types of Machine Learning Algorithms: 1. Supervised Learning 2. Unsupervised Learning 3. Semi-Supervised Learning 4. Reinforcement Learning
What Are Machine Learning Algorithms_.pdf
- 1. What Are Machine Learning Algorithms? Source: iabac.org Machine learning algorithms help computers learn from data. They allow machines to find patterns and make decisions without being told exactly what to do. In 2025, machine learning algorithms are very important. They are used in medicine, banking, education, and many other fields. This article explains what machine learning algorithms are. It talks about the different types and how they work. It also shows their features, how we measure their success, and the problems we may face when using them. What Are Machine Learning Algorithms?
- 2. Machine learning algorithms are methods that teach computers how to do tasks by learning from data. The computer looks at data and finds patterns. Then it uses those patterns to make predictions or choices. The more data it sees, the better it gets. ➤ Top 4 Types of Machine Learning Algorithms: 1. Supervised Learning Supervised learning means the computer learns with the help of answers. You give it data and also give the correct result for that data. The computer uses this to learn. After learning, it can give answers for new data.
- 3. Example: You show the computer many pictures of fruits. You also tell it the name of each fruit. The computer looks at the color, shape, and size. Then it learns to tell the name of a fruit it has not seen before. Some common supervised learning methods: ● Linear regression helps to guess numbers like the price of a house. ● Logistic regression helps to answer yes or no questions like whether a person will buy a product or not. ● Support vector machine helps to separate things into two or more groups. ● Decision trees and random forests help to make step-by-step decisions. 2. Unsupervised Learning Unsupervised learning means the computer learns without answers. You only give the data. The computer tries to find patterns or groups by itself. Example: You give the computer many pictures of animals but you do not tell the names. The computer looks at the pictures and puts animals with similar shapes or sizes in the same group. Some common unsupervised learning methods: ● K-means groups things that look the same. ● Principal component analysis helps to make big data smaller. ● Autoencoders help to find what is important in the data and remove the rest.
- 4. 3. Semi-Supervised Learning Semi-supervised learning is a mix of supervised and unsupervised learning. You give the computer a small amount of data with answers and a large amount of data without answers. The computer uses both types to learn better. Example: You give the names for ten pictures of animals but leave the rest without names. The computer learns from the ten pictures with names and uses that learning to guess the names for the rest. Some common supervised learning methods ● Linear Regression: Predicts numbers (e.g., prices). ● Logistic Regression: Predicts yes/no outcomes. ● Support Vector Machine (SVM): Classifies data into groups. ● Decision Trees: Makes step-by-step decisions. ● Random Forests: Combines many decision trees for better accuracy. 4. Reinforcement Learning Reinforcement learning means the computer learns by trying. It does something, sees the result, and learns from it. Good results get rewards. Bad results do not. Over time, the computer learns what actions give the best reward. Example: A computer plays a game. It tries many moves. When it wins, it gets a reward. When it loses, it gets nothing. After playing many times, it learns the best moves. Reinforcement learning is used in:
- 5. ● Self-driving cars ● Robots ● Game playing systems ➤ Features, Metrics, and Challenges The table below shows different machine learning algorithms, what they do, how we measure them, and what problems we face. Algori thm Featur es How We Proble ms
- 6. Measu re It Linear Regres sion Predic ts numb ers using a straig ht line Mean Squar ed Error Does not work well if data has outlier s Logisti c Regres sion Finds yes or no answe rs Accur acy, Precisi on Assu mes simple relatio nship betwe en data
- 9. Autoen coder Learn s what is impor tant in data Recon structi on Loss Needs a lot of tunin g Reinfo rceme nt Learni ng Learn s by gettin g rewar ds Total Rewar ds Hard to balanc e betwe en trying and learni ng ➤ New Trends in 2025 In 2025, some new machine learning algorithms are becoming more common. These include:
- 10. seldon.io ● Graph neural networks. These work well with data that shows connections, like social networks. ● Federated learning. This trains models on many devices without moving the data, which keeps it private. ● Self-supervised learning. This learns from data without needing labels. ● Diffusion models. These are good at creating pictures and text. ● Reinforcement learning with human feedback. This uses human choices to help computers learn better. These new types are helping many companies and researchers make better tools and services.
- 11. Final Thoughts Machine learning algorithms are changing the way we solve problems. They help computers learn from data, make smart guesses, and improve over time. In 2025, many companies and people use these methods every day. If you know how machine learning algorithms work, you can build better tools and make better decisions.