Machine Learning (ML) is an interesting subject and a trending topic.
Knowledge in ML is valuable for resumes and career prospects.
Understanding Machine Learning
Definition Breakdown
Machine: A device that performs work, often using electricity and providing output.
Learning: The process of acquiring new knowledge or skills.
Combining the Concepts
Machine Learning: A machine's ability to learn from data and improve its performance without explicit programming.
Example to Illustrate Machine Learning
Teaching a Robot:
Imagine a robot that doesn't recognize an orange.
If you show it 100 oranges and label them, it learns to recognize the fruit.
When presented with a 101st orange, it identifies it based on prior examples.
Key Definitions
Machine Learning Defined:
A branch of artificial intelligence (AI) where computers make decisions based on learned data without explicit instructions.
Formal Definition: "Machine learning is a subset of artificial intelligence in which machines learn how to complete a certain task without being explicitly programmed."
Learning from Data: The machine improves its recognition through data input and output feedback (e.g., identifying oranges vs. bananas).
Flow of Machine Learning
Data Input ➡️ Program (learning from data) ➡️ Output (answers based on learned knowledge).
Example: With 100 items, where 50 are oranges and 50 are bananas, the machine learns to identify them based on prior input.
Important Concepts to Remember
ML allows systems to learn and improve from experiences without explicit programming.
The key takeaway is that in ML, systems learn from data rather than from pre-programmed instructions.
Upcoming Topics
Next lecture will cover the three types of machine learning:
Supervised Learning
Unsupervised Learning
Reinforced Learning
Conclusion
The lecture provided a foundational understanding of machine learning and its importance in AI.