Data Science & AI

Welcome to the AI Leadership course, a comprehensive exploration of artificial intelligence (AI) and its transformative potential in today's world. As AI rapidly evolves, it is reshaping industries, revolutionizing workflows, and driving innovation across a range of sectors. This course is designed to equip future leaders with the essential knowledge and skills to harness AI effectively, understand its applications, and navigate the complexities of AI-driven initiatives.

Throughout the course, we will delve into AI in context, using real-world examples to illustrate where and how AI is being applied—from healthcare to finance, from entertainment to manufacturing. We will explore core AI concepts, including learning paradigms(such as supervised, unsupervised, and reinforcement learning) and examine the unique challenges and opportunities in working with audio and video data.

A key tool you'll engage with is the AI Value Canvas, a framework designed to help you strategically plan and implement AI projects. We will also focus on critical considerations for starting an AI project, including ethical, operational, and technological aspects, ensuring you are prepared to lead AI initiatives responsibly and effectively.

In addition to theoretical understanding, the course offers practical sessions on cutting-edge topics like generative AI and object detection. These hands-on experiences will empower you to experiment with AI technologies, fostering the confidence to apply them in real-world contexts.

By the end of this course, you will not only have a strong foundational understanding of AI but also the leadership insight needed to guide AI-driven projects to success.

This course covers the basic background for data science and data analysis, starting from a complementary introduction to the tools of probability theory. Data science is concerned with the extraction of predictive information from bulk or raw data, either from numerical data streams or from a mix of numerical and categorical data. To this end, a number of well-established statistical processing techniques are usually employed, ranging from regression, to classification and model assessment. The course will endow the students both with the background and the applied skills necessary to develop autonomously their data science projects.

Welcome to the Machine Learning course! In this course, you will dive into the fundamentals of machine learning, exploring both supervised and unsupervised techniques. We will begin by covering regression analysis, building a solid understanding of how to predict continuous outcomes. Then, we’ll explore the full ML pipeline, from data preprocessing to model evaluation. You will also learn about logistic regression for binary classification, support vector machines (SVM), handling imbalanced data, and performing cross-validation to assess model performance.

The course will also introduce you to probabilistic models like Naive Bayes, powerful algorithms like decision trees, and ensemble methods that combine multiple models to boost accuracy. Finally, we’ll cover unsupervised learning techniques, providing insights into clustering and dimensionality reduction. By the end of the course, you’ll have a solid foundation in machine learning techniques and be ready to apply them to real-world problems.

Course Description

This comprehensive course is designed to introduce participants to the fundamental concepts and applications of deep learning, a pivotal technology in the field of artificial intelligence that is reshaping industries, enhancing decision-making processes, and creating new paradigms for human-computer interaction. By blending theoretical knowledge with practical applications, this course aims to equip learners with a robust understanding of deep learning models and the skills needed to apply these models to solve real-world problems.

Who Should Enroll

This course is ideal for students, professionals, and enthusiasts with a basic understanding of machine learning who are eager to dive deeper into the world of deep learning. Whether you aim to enhance your academic knowledge, boost your career prospects, or simply satisfy your curiosity about how deep learning technologies work, this course will provide the foundation you need.

Course Modules

1. Introduction to Deep Learning: Begin your journey with an overview of deep learning, understanding its significance, applications, and how it differs from traditional machine learning approaches. This module sets the stage for the entire course by highlighting the motivation behind the development and use of deep learning technologies.

2. Convolutional Neural Networks (CNNs): Dive into the world of CNNs, the backbone of image recognition and computer vision. Learn how these networks can automatically and adaptively learn spatial hierarchies of features from image data.

3. Autoencoders: Explore the fascinating architecture of autoencoders, a type of artificial neural network used for unsupervised learning of efficient data codings. Understand their applications in feature learning, image reconstruction, and denoising.

4. Generative Neural Networks: Unveil the power of generative models, focusing on Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Discover how these networks can generate new data instances that resemble your training data.

5. Recurrent Neural Networks (RNNs): Step into the domain of sequential data with RNNs and their variants like LSTM and GRU. Learn how they are used to model time-series data, natural language processing, and more.

6. Text Embeddings: Gain insights into the techniques for representing text data in forms understandable by neural networks. Learn about Word2Vec, GloVe, and BERT, and how these embeddings capture semantic relationships between words.

7. Transformers: Explore transformers, the innovative neural network architecture pivotal in advancing natural language processing (NLP). This module covers the self-attention mechanism that uniquely allows transformers to process words in context, enhancing efficiency and accuracy. Learn through examples like machine translation and text summarization, gaining insights into why transformers are leading in AI advancements.

Capstone Project

The course culminates in a capstone project where participants will apply the concepts learned to design, implement, and evaluate a deep learning model tailored to a specific problem statement. This project not only solidifies the learning experience but also enables participants to showcase their skills through a tangible, impactful creation.

Learning Outcomes

Upon completion of this course, participants will be able to:

• Understand the foundational principles and advanced concepts of deep learning.
• Apply deep learning models to address problems in areas such as image recognition, natural language processing, and time-series analysis.
• Evaluate and select appropriate deep learning models for various applications.
• Implement and fine-tune deep learning models using popular frameworks like TensorFlow and PyTorch.

Get Ready to Transform Your Understanding of AI

Join us in this exciting journey through the depths of deep learning. Empower yourself with the knowledge and skills to harness the potential of AI and open doors to endless opportunities in the evolving technological landscape.

Welcome to the MLOps course, where you'll learn to bridge the gap between data science and operations, enabling you to deploy and scale machine learning models efficiently. In this course, we will cover essential tools and frameworks that are foundational to modern machine learning operations:

- Docker: Learn how to containerize your applications to ensure consistent environments across development, testing, and production.

- Kubernetes: Dive into Kubernetes for orchestrating containers, ensuring scalability, high availability, and resource optimization.

- FastAPI: Build and deploy high-performance APIs that expose your machine learning models to the world in a fast and easy-to-use manner.

- AI Services: Explore how to leverage cloud-based AI services to enhance and scale your machine learning solutions seamlessly.

By the end of this course, you'll have the skills to deploy, manage, and monitor machine learning models in production environments with confidence.

Welcome to the exciting world of Reinforcement Learning (RL)! This course is designed to introduce you to the fundamentals and advanced concepts of RL, a type of machine learning where agents learn to make decisions by interacting with an environment. Through a combination of theory and hands-on projects, you will explore how RL algorithms enable agents to optimize their actions to achieve specific goals, learn from rewards and penalties, and ultimately improve their performance over time.

Whether you're aiming to develop intelligent systems for gaming, robotics, finance, or any other domain, this course will equip you with the knowledge and skills to harness the power of reinforcement learning. Get ready to dive into the intricacies of Markov decision processes, value functions, policy gradients, and deep Q-networks, and embark on a journey that will transform the way you understand and apply machine learning.

Welcome to the Applied AI Course! This course is designed to equip you with practical skills and knowledge in the rapidly evolving field of artificial intelligence. Throughout this course, we will delve into key areas of AI, starting with data labeling, the foundational step that ensures high-quality input for training AI models. You will then explore computer vision and object detection, learning how machines interpret and identify objects in images. We’ll cover image embeddings, which transform images into numerical representations for easier analysis.

The course also includes time series analysis, vital for predicting and understanding patterns over time. Our journey into natural language processing (NLP) will reveal how AI comprehends and generates human language, leading to the fascinating world of generative AI, where you’ll see how machines can create content. By the end of this course, you’ll have hands-on experience and a solid understanding of these critical AI applications, preparing you to tackle real-world challenges and innovate in your field. Join us and take the first step towards mastering applied AI!

Course Description

This course teaches a practical application of AI and IoT, needed to work in a team with students from other disciplines, backgrounds and skills and characterising the current industry trends. Steps taken include problem identification in a domain of choice, idea generation, prototyping and problem solving. The course requires students to organise teamwork, document their ideas, develop solutions and make oral and visual presentation of ideas and solutions with guidance from faculty members while ensuring sustainability, innovativeness and originality

Course Objectives

To train students: -

1.  How to select a problem, analyse it and propose AI or IoT-based solutions
2. How to design and organise innovative projects over different knowledge fields
   
3. How to communicate the work and results of the innovation project with the different target groups using artificial intelligence and Internet of Things Technologies

 Course outcomes

At the end of this course, Students are expected to:-

1. Have the capability to manage and use knowledge gained in class to plan and implement AI or IoT  projects while utilising the collective skill set of the team to overcome various types of difficulty that may emerge
2. Demonstrate interpersonal skills, teamwork, and effective use of AI or IoT with system planning and development
   
3. Think objectively, analytically and critically in identifying and solving real-life problems using AI or IoT
   
4. Deliver or present the project findings in oral and written form
   

Indicative content

Concepts of project development with specific emphasis including problem identification, technical documentation, defining objectives of the project, project monitoring and communication (including report writing and presentation), collaboration planning, teams

Course Outline

1. Introduction to project  
2. Design thinking
   
3. Project Report writing and documentation
   
4. Project Implementation, Progress tracking and midterm reviews
   
5. Tools for collaboration