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Machine Learning Algorithms Simplified | Easy Notes with Real-Life Examples | B.Sc CS / B.E CSE / AI

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Boost your understanding of Machine Learning with these simplified notes covering the most popular algorithms with real-life examples. Perfect for 2024–25 university exams, this guide is ideal for students in B.Sc Computer Science, B.E CSE, B.Tech AI/ML, and M.Sc CS. What’s Inside: Overview of Machine Learning & its types Supervised, Unsupervised & Reinforcement Learning Detailed explanations of top ML algorithms: Linear Regression Decision Tree K-Nearest Neighbors (KNN) K-Means Clustering Naive Bayes SVM Random Forest ANN (Neural Networks) Visual summary table of algorithms Real-world use cases (YouTube, Netflix, Google, etc.) 5 key exam-focused questions

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​Artificial Intelligence & Machine Learning Notes – Full Set (Units 1 to 5 + ML Algorithms)


Title: AI & ML Notes | Units 1–5 + Machine Learning Algorithms with Real-Life Examples | 2024-
25




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✅ UNIT 1 to UNIT 5: Artificial Intelligence
(Refer to previous Units 1–5 content: Introduction, Agents, Problem Solving, Knowledge
Representation, Learning & Applications)




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✅ MACHINE LEARNING ALGORITHMS SIMPLIFIED
1. Introduction to Machine Learning (ML)


Machine Learning is a part of AI that enables computers to learn from data without being explicitly
programmed.


Difference between AI and ML:


AI: Goal is to simulate intelligence


ML: Goal is to learn from data




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2. Types of Machine Learning


🔹 Supervised Learning
Learns using labeled data


Algorithms: Linear Regression, Decision Trees, KNN


Examples:

, Email spam detection


Predicting house prices




🔹 Unsupervised Learning
Learns using unlabeled data


Algorithms: K-Means, PCA


Examples:


Grouping customers based on behavior




🔹 Reinforcement Learning
Learns from reward and punishment


Example: AI playing games, self-driving cars




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3. Key ML Algorithms (with Examples)


✅ Linear Regression
Predicts a numeric value


Example: Predicting marks based on study hours




✅ Decision Tree
Splits data like a flowchart
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