Foundations of AI:

• Understanding the fundamental principles and theories that underlie AI.
• Exploration of algorithms, logic, and problem-solving techniques.

Machine Learning:

• In-depth study of machine learning algorithms and models.
• Application of supervised and unsupervised learning methods.
• Practical experience with data preprocessing and feature engineering.

Deep Learning:

• Focus on neural networks and deep learning architectures.
• Training models for image recognition, natural language processing, and other applications.
• Understanding convolutional neural networks (CNNs) and recurrent neural networks (RNNs).

Natural Language Processing (NLP):

• Analysis and understanding of human language by machines.
• Text mining, sentiment analysis, and language generation.
• Building applications for speech recognition and language translation.

Computer Vision:

• Study of algorithms and techniques for image and video analysis.
• Object detection, image classification, and image segmentation.
• Application of computer vision in various industries.

Robotics and Autonomous Systems:

• Integration of AI in robotics and autonomous systems.
• Control algorithms, sensor integration, and decision-making in robotics.
• Real-world applications in fields such as self-driving cars and industrial automation.

Ethics and Responsible AI:

• Exploration of ethical considerations in AI development and deployment.
• Addressing biases and fairness in AI algorithms.
• Understanding the societal impact of AI technologies.