“Applied Artificial Intelligence” courses typically focus on the practical applications of artificial intelligence in various domains. Here are some common features and topics covered in applied AI courses:

1. Practical AI Applications: These courses emphasize real-world applications of AI, including areas like healthcare, finance, marketing, robotics, and more.

2. Hands-On Projects: Students often work on hands-on projects that involve implementing AI algorithms, building AI models, and solving practical problems.

3. Machine Learning: The courses may cover machine learning techniques, including supervised learning, unsupervised learning, and reinforcement learning, with a focus on how to apply them to real data.

4. Deep Learning: Deep learning, including neural networks and deep neural networks, is a significant component of applied AI courses, especially for tasks like image recognition and natural language processing.

5. Data Analysis: Understanding and working with data are crucial skills in AI. Courses may include topics like data preprocessing, feature engineering, and data visualization.

6. Tools and Frameworks: Students may learn how to use popular AI frameworks and tools like TensorFlow, PyTorch, scikit-learn, and more.

7. Ethical Considerations: Many applied AI courses address ethical considerations related to AI, such as bias in AI models and responsible AI development.

8. Industry-Relevant Skills: The goal is to equip students with skills that are in demand in industries where AI is applied.

9. Certification: Completing the course often results in a certification, which can be valuable for career advancement.