Course Overview:
This course aims to give a quick start to the participants who want to train themselves in the design and programming of artificial intelligence applications and have no experience in this field, and to carry them to a level that they can progress on their own. The content of the course includes Python programming, basic concepts of machine learning, design and testing principles, introduction to deep learning programming and applications based on real life examples.
Course Methodology:
The trainings will be used face to face, direct lecture, group work and collaborative teaching methods and techniques. Knowing and doing are different things. Therefore, the trainings are designed so that the participants participate not only as listeners but also as active participants in the activities.
Modules:
Module 1 – Python Basics
Module 2 – Object Oriented Programming
Module 3 – Dealing with Errors
Module 4 – Numpy Library
Module 5 – Pandas Library
Module 6 – Matplotlib Library
Module 7 – Object Oriented Programming (OOP)
Learning Outcomes:
- The mathematics behind Artificial Intelligence (Reinforcement Learning) algorithms, logic, theory and the coding of these algorithms with Python from scratch
- Developing different Artificial Intelligence (Reinforcement Learning) projects that we will code together and individually
- Learning how an Artificial Intelligence model (Agent) can be trained by itself regardless of data.
- Learning Artificial Intelligence (Reinforcement Learning) algorithms such as Q-Learning, Deep Q-Learning
- Creating a game environment (Atari Game) with Python to use Artificial Intelligence (Reinforcement Learning) algorithms
- Knowing how, why and for what Artificial Intelligence (Reinforcement Learning) algorithms are used in the world
- Developing an Artificial Intelligence (Reinforcement Learning) algorithm on your own