Machine learning (ML) is a branch of artificial intelligence (AI) that focuses on developing algorithms and models that allow computers to learn from data without being explicitly programmed. It's about equipping systems with the ability to learn, identify patterns, and make decisions based on data, rather than solely relying on pre-defined instructions.
Speaker(s): Stephen,
Agenda:
1. Machine Learning and relationship to the Artificial Intelligence circle
2. Defining Machine Learning
3. Need for Machine Learning
4. Difference between Machine Learning, Traditional Programming, and Artificial Intelligence.
5. Workings of Machine Learning algorithms.
6. The Machine Learning Lifecycle
7. Types of Machine Learning
8. Various Applications of Machine Learning
9. Limitations of Machine Learning
10. Machine Learning Future
11. Deep Learning and how it relates to Machine Learning
12. Difference between Machine Learning and Deep Learning
13. Deep Learning Workings
14. Deep Learning in Machine Learning Paradigms
15. Evolution of Neural Architecture
16. Deep Learning Applications
17. Challenges in Deep Learning
18. Advantages/Disadvantages of Deep Learning
19. Software/models for development
20. Deep Learning Future
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