Uploaded byMohamed Essam
Part 1 AI
This document provides an introduction to artificial intelligence and machine learning. It outlines the content that will be covered, including definitions of artificial intelligence, machine learning, and the differences between traditional programming and machine learning programming. It also describes the types of machine learning problems including supervised learning problems like regression and classification, unsupervised learning problems to find latent relationships in unlabeled data, and reinforcement learning problems where an agent receives feedback to learn how to interact with an environment. Examples of each type of problem are provided. The next part will cover Python.
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Part 1 AI
- 1. INTRODUCTION TO Artificial intelligence 01 Computers areable to see, hear and learn. Welcome to the future.” ~Dave Waters
- 2. CONTENT OF THISCOURSE Here’s what you’ll find in this Course : ● What Artificial intelligence is ? ● Artificial Intelligence used in! ● Applications of Artificial intelligence. ● Machine Learning definition. ● Machine Learning Algorithms.
- 3. Introduction to AIwith ML What Artificial Intelligence is! Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
- 4. Introduction to AIwith ML Artificial Intelligence used in!
- 5. Introduction to AIwith ML Artificial Intelligence used in!
- 6. Introduction to AIwith ML What Artificial Intelligence is! Artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems.
- 7. Introduction to AIwith ML Artificial Intelligence used in!
- 8. Introduction to AIwith ML Artificial Intelligence used in!
- 9. Introduction to AIwith ML Artificial Intelligence used in!
- 10. Introduction to AIwith ML Artificial intelligence Techniques
- 11. Introduction to AIwith ML What Machine Learning is!
- 12. Introduction to AIwith ML What Machine Learning is! Machines can work and act like a human if they have enough information. Grew out of work in AI. It is one of the applications of AI where machines are not explicitly programmed to perform certain tasks; rather, they learn and improve from experience automatically. Examples: Database mining Large datasets from growth of automation/web. E.G Medical records, biology, engineering Applications can’t program by hand. E.g., Autonomous helicopter, handwriting recognition, most of Natural Language Processing (NLP), Computer Vision.
- 13. Introduction to AIwith ML Different between Traditional Programming VS Machine Learning Programming Traditional programming I know the equation Example Speed function is S𝐩𝐞𝐞𝐝 = 𝑫𝒊𝒔𝒕𝒂𝒏𝒄𝒆 𝑻𝒊𝒎𝒆 Machine Learning Programming I use Function called (train) to get the equation Example if there is the house cost 600$ and it’s a 200m then the equation will be price= 𝟑𝒙 100 200 300 400 1200 900 600 300 size
- 14. Introduction to AIwith ML Magic? No, more like gardening Seeds = Data Nutrients = Algorithms Gardener = You Plants = Model
- 15. Introduction to AIwith ML Machine Learning Model
- 16. Introduction to AIwith ML Magic? No, more like gardening Seeds = Data Nutrients = Algorithms Gardener = You Plants = Model
- 17. Introduction to AIwith ML Machine Learning Model
- 18. Introduction to AIwith ML Machine Learning Model
- 19. Introduction to AIwith ML Types of Machine LearningProblems Supervised Unsupervised Reinforcement Output is a discrete variable (e.g.,cat/dog) Classification Regression Output is continuous (e.g.,price, temperature)
- 20. Introduction to AIwith ML Supervised Algorithm-Regression
- 21. Introduction to AIwith ML Supervised Algorithm-Classification
- 22. Introduction to AIwith ML Supervised Algorithm-Classification SPAM
- 23. Introduction to AIwith ML Types of Machine LearningProblems Unsupervised Supervised There is no desired output. Learn somethingabout the data. Latent relationships. I want to find anomalies in the credit cardusage patterns of my customers. Reinforcement I have photos and want to put them in 20 groups.
- 24. Introduction to AIwith ML Types of Machine LearningProblems
- 25. Introduction to AIwith ML Types of Machine LearningProblems Unsupervised Supervised Reinforcement Useful for learning structure in the data(clustering), hidden correlations, reduce dimensionality,etc.
- 26. Introduction to AIwith ML Types of Machine LearningProblems Environment gives feedback via a positiveor negative reward signal. Unsupervised Reinforcement Supervised An agent interacts with an environment andwatches the result of the interaction.
- 27. Introduction to AIwith ML Next part : Python