Machine Learning (ML) is a subset of artificial intelligence that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
In traditional programming, we write rules to get answers from data. In machine learning, we provide data and answers, and the algorithm learns the rules.
- Supervised vs. Unsupervised Learning
- Training and Test datasets
- Model evaluation metrics
- Overfitting and Underfitting
- The ML workflow
Exercise: ML Problem Identification
For each of the following scenarios, identify whether it's a supervised or unsupervised learning problem, and what type of algorithm might be appropriate:
- Grouping customers based on purchasing behavior
- Predicting house prices based on features like size, location, etc.
- Identifying fraudulent credit card transactions
- Recommending movies to users based on what they've watched
- Categorizing news articles into topics
Google ML Crash Course
Main resource for today
Machine Learning Mastery
What is Machine Learning?
Andrew Ng's ML Course
Introduction to Machine Learning
ML for Beginners
Microsoft's curriculum
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