AI From Zero - Guidelines to Exercises - Lesson 3
Guidelines to Exercises - AI From Zero - Lesson 3: How AI Learns
Guidelines to Exercises - Lesson 3
1. What is Machine Learning (ML) in simple terms?
Guideline: Focus on the core definition: learning from data without explicit programming.
Correct Answer: b) A way for computers to learn from data without explicit programming.
2. How is Machine Learning different from traditional programming?
Guideline: Traditional programming involves explicit rules; ML involves learning rules from data.
Example Answer: In traditional programming, humans provide specific rules and data to get an answer. In Machine Learning, humans provide data and answers, and the machine figures out the rules itself.
3. Why is "data" so important for Machine Learning models?
Guideline: Data is the foundation upon which ML models learn patterns and improve accuracy.
Example Answer: Data is crucial because Machine Learning models learn patterns and relationships from the data they are fed, allowing them to make accurate predictions or generate relevant outputs. More data usually means smarter AI.
4. What does "Supervised Learning" mean?
Guideline: Focus on the concept of "labeled" data.
Example Answer: Supervised Learning is a type of Machine Learning where the AI learns from data that has already been labeled with the correct answers, using these examples to find patterns.
5. What does "LLM" stand for, and what do they specialize in?
Guideline: Recall the acronym and their primary function.
Example Answer: LLM stands for Large Language Model, and they specialize in understanding and generating human-like text. Examples include ChatGPT, Google Gemini and Anthropic Claude.
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