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 <a href="http://instructions.

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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 <a href="http://Intelligence.

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5. Workings of Machine Learning <a href="http://algorithms.

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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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