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ML1-BasicConcepts.pdf
Unit 1Basic ML workflow
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Everything you need to master any topic - learn, practice, and ace itUnit 1Basic ML workflow
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Notes
Quiz
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Unit 2Model training
Unit 3Model evaluation
Unit 4Deployment
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AI generates your notes and answers questions as you studyIntroduction to machine learning
Machine learning is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.
1. Types of machine learning
There are three main types of machine learning algorithms:
•Supervised Learning: Uses labeled data to train models and make predictions
•Unsupervised Learning: Finds patterns in unlabeled data without guidance
•Reinforcement Learning: Learns through trial and error using rewards and penalties
Each approach has specific use cases and applications across different industries, from healthcare to finance.
2. Supervised learning in detail
Supervised learning algorithms learn from labeled training data, helping predict outcomes for unforeseen data. The algorithm learns the mapping function from the input to the output. Common algorithms include linear regression, logistic regression, decision trees, and neural networks.
3. Key algorithms
•Linear Regression: Predicts continuous values based on input features
•Decision Trees: Creates a tree-like model of decisions and outcomes
•Neural Networks: Mimics the human brain structure for complex pattern recognition
•Support Vector Machines: Finds optimal hyperplane for classification tasks
Understanding these algorithms is crucial for selecting the right approach for your specific problem domain.
4. Real-world applications
Machine learning is revolutionizing industries worldwide. In healthcare, it powers diagnostic tools and drug discovery. In finance, it detects fraud and predicts market trends. Autonomous vehicles rely on ML for navigation and object detection. Recommendation systems on streaming platforms use collaborative filtering to suggest content.
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What's the difference between supervised and unsupervised learning?
Great question! The key difference lies in the data:
Supervised learning uses labeled data where you know the correct answers. For example, training a spam detector with emails already marked as "spam" or "not spam."
Unsupervised learning works with unlabeled data to find patterns on its own, like grouping similar customers without predefined categories.
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Introduction_to_ML_Final_v3.pdf147 pages
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Unit 1Introduction to ML
Unit 2Model training
Unit 3Basic ML workflow
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