Artificial Intelligence (223003114)

The training covers exception handling mechanisms, enabling effective error management within programs. Students will also learn to work with modules and packages, allowing them to integrate external functionality into their projects. Special focus will be placed on decorators, generators, and iterators, which help write more flexible and efficient code. Additionally, students will be introduced to asynchronous programming, testing, and programming architectures – essential for building higher-quality and more reliable applications.

In the Data Analysis module, participants will learn to use Pandas and NumPy libraries for data cleaning, processing, and analysis. The course also covers data visualization techniques using Matplotlib and Seaborn, allowing students to present data clearly and effectively. Additionally, students will be introduced to the basics of data classification and prediction using simple machine learning models. This module provides a solid foundation in data analysis and prepares learners for more complex analytical tasks.

This module is designed to provide students with foundational knowledge of database management systems (DBMS), with a particular focus on SQL and database integration with Python. The course will help students understand how to efficiently store, manage, and access data—an essential skill for projects and applications of any size.

This part of the course focuses on the fundamentals and core methods of machine learning (ML), equipping students with the essential understanding and skills needed to build and apply ML models. Students will learn to use the scikit-learn library—one of the most popular Python libraries for machine learning—covering both supervised and unsupervised learning techniques. Key ML models will be explored, including linear and logistic regression, decision trees, random forests, and support vector machines (SVM).

This part of the course introduces students to the core concepts of neural networks and deep learning (DL). We begin with the fundamentals—what artificial neurons are, how they are connected into networks, and how these networks learn to recognize patterns, sounds, images, and other complex types of data. Students will learn to work with TensorFlow and Keras, two key libraries that simplify the creation and training of DL models. The course will cover essential deep learning models in detail, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are crucial for tasks such as image recognition and natural language processing (NLP). It will also include time series analysis and models like LSTM and GRU.

In this segment of the course, students will explore various real-world applications of artificial intelligence (AI), including advanced assistant systems and automated customer service, while also learning about the legal and ethical aspects of AI projects. The course covers the analysis of successful AI solutions and their impact on industries such as business and healthcare. Legal and ethical topics—such as privacy, data protection, and intellectual property—will be discussed alongside ethical dilemmas related to automated decision-making, accountability, and societal impact. Students will gain an understanding of the importance of integrating ethical values and complying with legal requirements when developing AI solutions. The course will also examine international regulations and country-specific policies related to AI governance, preparing students for the responsible development and implementation of AI technologies.

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