Artificial Intelligence & Machine Learning

In this module, students will explore a variety of machine learning algorithms, including supervised, unsupervised, and reinforcement learning methods. They will gain a deep understanding of how machines analyse and learn from data, identify patterns, and make predictions.

Through practical applications and real-world examples, students will learn how to select and implement the appropriate algorithm for different tasks, equipping them with the knowledge to solve complex problems and build intelligent systems in various domains.

In this module, students will dive deep into the fundamentals of deep learning, focusing on advanced neural network architectures such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs). They will explore how these powerful models are used to drive cutting-edge AI applications, including image recognition, speech processing, and natural language understanding.

By gaining hands-on experience with these architectures, students will understand how deep learning techniques enable machines to perform complex tasks with high accuracy and efficiency in real-world scenarios.

In this module, students will gain comprehensive proficiency in key data science techniques, including data pre-processing, exploratory data analysis (EDA), and feature engineering. They will learn how to clean, transform, and optimize datasets to ensure they are ready for effective model training.

By exploring various tools and methodologies, students will acquire the skills to identify trends, handle missing data, and create relevant features, ultimately improving model performance and ensuring more accurate and reliable predictions in data-driven applications.

In this module, students will develop hands-on experience with industry-standard AI tools and frameworks, such as TensorFlow, PyTorch, and Scikit-learn. They will learn how to use these powerful libraries to build, train, and deploy machine learning and deep learning models.

Through practical exercises, students will gain proficiency in selecting the right tools for specific tasks, optimising model performance, and efficiently deploying AI solutions in real-world scenarios, preparing them for successful careers in AI and data science.

In this module, students will master key techniques for understanding and processing human language. They will explore various NLP tasks, such as text analysis, sentiment analysis, and language modelling, to extract meaningful insights from textual data.

Students will gain practical experience in applying NLP algorithms to solve real-world problems, including chatbots, translation, and content recommendation systems. By the end of the course, they will be equipped to build and deploy advanced language processing solutions using state-of-the-art techniques.