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                     Artificial 
Intelligence v/s 
Machine Learning
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Artificial Intelligence vs. 
Machine Learning
 Overview of differences
 Importance in 2025 tech landscape
 Real-world industry adoption
 Beginner-friendly breakdown
 Includes examples and comparisons
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        Introduction to Artificial Intelligence
 AI enables machines to mimic human intelligence
 Includes reasoning, learning, and decision- making
 Works with rules, logic, and intelligent systems
 Used in automation, robotics, and analytics
 Aims to augment human cognitive abilities
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Introduction to Machine Learning
 ML is a subset of AI focused on learning from data
 Models improve automatically over time
 Uses algorithms for pattern detection
 Common tasks: classification & regression
 Requires training datasets to learn
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Key Differences: AI v/s ML
 AI is the larger concept; ML is a subset
 AI mimics intelligence; ML learns patterns
 AI uses logic; ML uses statistical models
 AI can be rule-based; ML is data-driven
 ML helps AI achieve adaptive intelligence
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How AI Works
 Define problem and goals
 Input rules, logic, or knowledge bases
 Processing by intelligent agents
 Decision-making through reasoning
 Improves using feedback mechanisms
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How ML Works
 Collect and preprocess data
 Split into training/validation/test sets
 Train algorithms on labeled or unlabeled data
 Evaluate and tune model performance
 Deploy model for real-time predictions
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AI v/s ML Use Cases
 AI: Self-driving cars
 AI: Fraud detection systems
 ML: Spam email filtering
 ML: Recommendation engines
 ML: Medical diagnosis support
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Conclusion
 AI is broader; ML is a core component
 AI uses logic + ML + deep learning
 ML focuses solely on data-driven learning
 Both drive modern innovations
 Work together to build intelligent systems
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