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Machine Learning Algorithms (Every Data Scientist Must Know)
Machine learning powers so many things around us — from recommendation systems to self-driving cars!
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But understanding the different types of algorithms can be tricky.
This is a quick and easy guide to the four main categories: Supervised, Unsupervised, Semi-Supervised, and Reinforcement Learning.
1. Supervised Learning
In supervised learning, the model learns from examples that already have the answers (labeled data). The goal is for the model to predict the correct result when given new data.
Some common supervised learning algorithms include:
➡️ Linear Regression — For predicting continuous values, like house prices.
➡️ Logistic Regression — For predicting categories, like spam or not spam.
➡️ Decision Trees — For making decisions in a step-by-step way.
➡️ K-Nearest Neighbors (KNN) — For finding similar data points.
➡️ Random Forests — A collection of decision trees for better accuracy.
➡️ Neural Networks — The foundation of deep learning, mimicking the human brain.