This category includes courses related to Machine Learning and Artificial Intelligence:

  1. MLAI_101: Foundations of Machine Learning
    This course introduces the fundamental concepts of machine learning, including supervised, unsupervised, and reinforcement learning. Students will explore algorithms such as linear regression, decision trees, and support vector machines while working on real-world datasets to build hands-on experience.

  2. MLAI_102: Introduction to Artificial Intelligence
    Designed as an entry point to the world of AI, this course covers the history, principles, and applications of AI. Topics include problem-solving, search algorithms, and an introduction to natural language processing and computer vision, with an emphasis on ethical considerations in AI.

  3. MLAI_103: Applied Machine Learning
    This course focuses on the practical implementation of machine learning models using popular frameworks such as TensorFlow and Scikit-learn. Students will work on projects involving data preprocessing, model evaluation, and optimization techniques to solve real-world problems.

  4. MLAI_104: AI Ethics and Governance
    A critical exploration of ethical issues in AI, this course examines bias in algorithms, data privacy concerns, and the broader societal impact of AI technologies. Students will analyze case studies and develop frameworks for ethical decision-making in AI development and deployment.

  5. MLAI_105: Foundations of Deep Learning
    This course provides a comprehensive introduction to deep learning, covering key architectures such as convolutional and recurrent neural networks. Students will explore optimization techniques, backpropagation, and modern advancements like transformers. Hands-on projects using frameworks like TensorFlow and PyTorch will reinforce theoretical concepts.

  6. MLAI_106: Deep Learning for Computer Vision
    This course explores deep learning techniques for image recognition, object detection, and generative modeling. Students will study convolutional neural networks (CNNs), transfer learning, and advanced architectures like Vision Transformers. Hands-on projects using TensorFlow and PyTorch will reinforce practical applications in fields such as medical imaging, autonomous driving, and facial recognition.

  7. MLAI_107: Natural Language Processing with Deep Learning
    Focusing on deep learning for text data, this course covers word embeddings, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers. Students will implement models for tasks such as sentiment analysis, machine translation, and text generation using frameworks like TensorFlow and PyTorch. Ethical considerations in NLP, including bias and fairness, will also be discussed.

MLAI_101: Foundations of Machine Learning

This course introduces the fundamental concepts of machine learning, including supervised, unsupervised, and reinforcement learning. Students will explore algorithms such as linear regression, decision trees, and support vector machines while working on real-world datasets to build hands-on experience.

MLAI_105: Foundations of Deep Learning

This course provides a comprehensive introduction to deep learning, covering key architectures such as convolutional and recurrent neural networks. Students will explore optimization techniques, backpropagation, and modern advancements like transformers. Hands-on projects using frameworks like TensorFlow and PyTorch will reinforce theoretical concepts.

MLAI_106: Deep Learning for Computer Vision

This course explores deep learning techniques for image recognition, object detection, and generative modeling. Students will study convolutional neural networks (CNNs), transfer learning, and advanced architectures like Vision Transformers. Hands-on projects using TensorFlow and PyTorch will reinforce practical applications in fields such as medical imaging, autonomous driving, and facial recognition.

MLAI_107: Natural Language Processing with Deep Learning

Focusing on deep learning for text data, this course covers word embeddings, recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and transformers. Students will implement models for tasks such as sentiment analysis, machine translation, and text generation using frameworks like TensorFlow and PyTorch. Ethical considerations in NLP, including bias and fairness, will also be discussed.