🔥 09 ESSENTIAL ML ALGORITHMS You MUST Know! 🚀 Whether you’re a beginner or pro, these are the backbone of every Machine Learning project 👇 1️⃣ Linear Regression – Predict continuous values 📈 2️⃣ Logistic Regression – For binary classification 🔁 3️⃣ Decision Tree – Human-like decision making 🌳 4️⃣ SVM – The margin master ⚔️ 5️⃣ KNN – The neighbor-based learner 🧭 6️⃣ Dimensionality Reduction – Simplify the data 🎯 7️⃣ Random Forest – The power of many trees 🌲 8️⃣ K-Means – Cluster and conq
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Want a quick reference guide for all the types of Machine Learning Algorithms ? Whether you're a beginner or an advanced learner, this guide covers every essential algorithm you need to know in ML. Here’s a breakdown: 1. Supervised Learning Algorithms that learn from labeled data to make predictions. Includes regression, classification, and ensemble models like Random Forest, SVM, and Gradient Boosting. 2. Unsupervised Learning Learns patterns from unlabeled data. Covers clustering (K-Means, DBS
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Types of Machine Learning (ML) Systems: 1. Based on Learning Style: a. Supervised Learning Learns from labeled data. Goal: Predict output for new inputs. Common tasks: Classification, Regression. Examples: Linear Regression, Decision Trees, SVM. b. Unsupervised Learning Learns from unlabeled data. Goal: Discover hidden patterns. Common tasks: Clustering, Dimensionality Reduction. Examples: K-Means, PCA, DBSCAN. c. Semi-Supervised Learning Uses a small amount of labeled data large unlabeled data.
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