| Course Category | Course Name | Course Content | Learning Objectives |
| Foundation Course | Overview and Foundation of AI | Overview of AI, its applications, and development trends. Understand AI and its various fields of application. | Understand the overview and applications of AI. Grasp basic skills in the field of AI. |
| Foundation Course | Fundamentals of Data Structures and Algorithms | Covers data structures (arrays, lists, stacks, queues, trees, graphs) and basic algorithms. Understand data structures and algorithms in the context of AI. | Understand basic data structures and algorithms. Grasp programming and algorithmic skills for AI applications. |
| Foundation Course | Fundamentals of Python Programming | Covers basic Python programming (syntax, variables, functions, modules). Understand the use of the Python programming language in AI. | Understand the Python programming language and its application in AI. Grasp fundamental skills in Python programming. |
| Foundation Course | Data Analysis and Visualization | Covers data analysis (data processing, statistical analysis) and data visualization (using Pandas, Matplotlib, Seaborn). Understand the principles of data analysis and visualization. | Understand data analysis and data visualization. Grasp skills for data processing and visualization. |
| Advanced Courses | Fundamentals of Machine Learning | Overview of machine learning, supervised and unsupervised learning algorithms, and model optimization. Understand machine learning and its practical applications. | Understand machine learning along with supervised and unsupervised learning algorithms. Grasp the skills to develop and optimize machine learning models. |
| Advanced Courses | Natural Language Processing (NLP) | Covers Natural Language Processing (NLP), sentiment analysis, and text classification. Understand natural language processing and its practical applications. | Understand natural language processing and its practical applications. Grasp skills for analyzing and processing natural language. |
| Advanced Courses | Computer Vision | Covers image processing, feature extraction, and image recognition. Understand image processing and its practical applications. | Understand image processing and its practical applications. Grasp skills for image processing and recognition. |
| High-Level Courses | Deep Learning and Neural Networks | Covers basic neural networks, CNN, RNN, and GAN. Understand neural networks and their practical applications. | Understand neural networks and deep learning models. Grasp the skills to develop and apply neural networks. |
| High-Level Courses | Reinforcement Learning and its Industrial Applications | Covers Reinforcement Learning and its applications in industries such as manufacturing, healthcare, and finance. Understand Reinforcement Learning and its industrial applications. | Understand Reinforcement Learning and its applications in various industries. Grasp the skills to develop and apply AI in specific industrial contexts. |
