Artificial Intelligence & Machine Learning

Students will get a thorough grasp of machine learning and artificial intelligence algorithms, including deep learning, reinforcement learning, supervised learning, and unsupervised learning. They will be able to describe and use a variety of AI and ML algorithms, including neural networks, SVM, KNN, decision trees, and clustering approaches.

Python and R are two popular programming languages in the AI and ML fields, and students will get practical exposure with both. Additionally, they will learn how to use key AI frameworks and technologies like AWS AI, Scikit-learn, OpenCV, TensorFlow, and Keras. This makes it possible for them to effectively deploy and apply machine learning models.

Students will have the ability to create, train, and refine machine learning models for a range of applications, including dimensionality reduction, regression, classification, and clustering. Additionally, they will learn how to assess models using measures like confusion matrices, F1-score, recall, accuracy, and precision.

Neural networks, CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), and LSTMs (Long Short-Term Memory networks) are among the deep learning approaches that students will study. In order to create complex AI applications, they will also study computer vision and natural language processing (NLP).