Course Description
After following this training course, the trainee will have the ability to understand what artificial intelligence and machine learning are practically (not just theoretically), to read any ML project and understand its stages, in addition to distinguishing between different types of learning and understanding how data is prepared and models are evaluated. He will also be able to implement a simple comprehensive project from beginning to end.
What you'll learn
- An introduction to artificial intelligence concepts: What is artificial intelligence? What is machine learning? The difference between machine learning and deep learning…
- The general structure of a machine learning system - the project lifecycle, and an explanation of the main offices for handling the various tasks within the project, with real-world examples.
- The main methods of machine learning: supervised learning, unsupervised learning, and reinforcement learning. With practical examples of each type.
- Examples of simple machine learning algorithms
- Training and evaluation strategies
- Data processing and attribute engineering dealing with data cleaning problems, handling missing values, converting data into a format suitable for the model,
- A small project to apply the concepts of the course.
Skills- Understanding what artificial intelligence and machine learning are practically (not just theoretically)
- Read any ML project and understand its phases
- Distinguishing between different types of learning
- Understanding how data is prepared and models are evaluated
- Implementing a simple, comprehensive project from start to finish.
Requirements
- computer
- Internet
- Programming basics.
