Introduction to Artificial Intelligence (AI) with Python

The course introduces Artificial Intelligence (AI) and its applications, starting with Python programming basics, including syntax, data types and libraries like pandas and NumPy. It covers essential machine learning concepts, such as supervised and unsupervised learning, feature engineering and data preprocessing. The curriculum also explores deep learning, computer vision, natural language processing and pre-trained models. Practical skills in deployment strategies and hands-on projects are emphasized. Designed for beginners, it provides a solid foundation in AI tools and techniques for real-world problem-solving.

*One continuing education unit equals 10 contact hours.

1. Describe Artificial Intelligence, identify its applications, tools and techniques and summarize the basics of the Python programming language
2. Demonstrate proficiency in basic Python syntax, data types and variables by writing simple scripts
3. Implement control flow structures and loops to manage program execution in Python
4. Develop and utilize Python functions and modules for modular programming
5. Perform file handling operations and execute input/output functions in Python
6. Apply object-oriented programming principles to design Python applications
7. Utilize Python libraries such as pandas and NumPy to manipulate and analyze data
8. Analyze mathematical concepts for Machine Learning, including linear algebra, probability, and statistics
9. Explain the fundamentals of Machine Learning and its real-world applications
10. Implement feature engineering and data preprocessing techniques to prepare datasets for modeling
11. Apply supervised learning algorithms, such as linear regression, to solve predictive tasks
12. Use unsupervised learning techniques to uncover patterns in datasets
13. Summarize the basics of deep learning and its applications in AI
14. Apply image processing techniques and explore the fundamentals of computer vision
15. Describe key concepts of natural language processing (NLP) and its applications
16. Utilize pretrained models for various Machine Learning tasks to improve efficiency
17. Deploy Machine Learning models for real-world applications and assess their performance

EGP 10,500