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Machine Learning (ML) is a subset of artificial intelligence that allows computers to learn from data and improve autonomously. It includes three main types: supervised learning, unsupervised learning, and reinforcement learning, each with distinct applications such as spam detection and customer segmentation. ML is widely used in various fields, including image recognition, fraud detection, and autonomous vehicles, highlighting its transformative impact on industries.

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17 views2 pages

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Machine Learning (ML) is a subset of artificial intelligence that allows computers to learn from data and improve autonomously. It includes three main types: supervised learning, unsupervised learning, and reinforcement learning, each with distinct applications such as spam detection and customer segmentation. ML is widely used in various fields, including image recognition, fraud detection, and autonomous vehicles, highlighting its transformative impact on industries.

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Lecture Notes: Introduction to Machine Learning

What is Machine Learning?

Machine Learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from
data and improve their performance over time without being explicitly programmed. Instead of
following fixed instructions, ML models identify patterns and make decisions based on input data.

Types of Machine Learning

1. Supervised Learning:

o The model is trained on labeled data, meaning each input has a corresponding
output.

o Example: Email spam detection, where emails are labeled as “spam” or “not spam.”

2. Unsupervised Learning:

o The model works with unlabeled data and tries to find hidden patterns or groupings.

o Example: Customer segmentation in marketing.

3. Reinforcement Learning:

o The model learns by interacting with an environment and receiving rewards or


penalties.

o Example: Training a robot to navigate a maze.

Key Concepts

 Features: Input variables used to make predictions.

 Labels: The desired output in supervised learning.

 Training: The process of teaching the model using data.

 Testing: Evaluating model performance on unseen data.

Applications of Machine Learning

 Image and speech recognition

 Fraud detection

 Personalized recommendations (e.g., Netflix, Amazon)

 Autonomous vehicles

Conclusion
Machine Learning is transforming industries by enabling systems to automatically improve and make
smarter decisions. Understanding its basics is essential for leveraging AI technologies effectively.

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