Machine Learning Methods

Obtain the foundational skills needed to begin your goal of becoming a programmer. This course introduces first-time programmers to basic programming techniques and principles using the popular Python Programming language. Python's simple syntax is similar to the English language making it easy to comprehend and allows programmers to write code with fewer lines. This course serves as a good starting point for students looking to further their training in the Python programming language. Course Highlights: Rules and syntax applicable to a modern programming language Understanding and...

Introduction to Statistics Statistics encompasses the collection, analysis, and interpretation of data and provides a framework for thinking about data in a rigorous fashion. Statistics is used in many areas of scientific and social research, is critical to business and manufacturing, and provides the mathematical foundation for machine learning and data mining. In this course, students will gain a comprehensive introduction to the concepts and techniques of elementary statistics as applied to a wide variety of disciplines. The course emphasizes problem solving, statistical thinking, and...

Linear algebra provides a mathematical framework for organizing information and then using that information to solve problems, especially physics, math, engineering, or data analytics problems. Linear algebra is essential for understanding and creating machine learning algorithms, especially neural network and deep learning models. In this course, you will learn the linear algebra skills necessary for machine learning and neural network modelling. The course reviews the basic matrices and vector algebra as applied to linear systems and progresses into more advanced concepts of...

An ever-increasing volume of research and industry data is being collected on a daily basis. Skilled data scientists are needed to process and filter the data, to detect new patterns or anomalies within the data, and gain deeper insight from the data. This course provides students with a foundation in basic data mining, data analysis, and predictive modelling concepts and algorithms. Using practical exercises, students will learn data analysis and machine learning techniques for model and knowledge creation through a process of inference, model fitting, or learning from examples. Practical...

Deep Learning is often called “Statistical Learning” and approached by many experts as statistical theory of the problem to find the best model approximation from a given collection of data. These models are produced today by Generative Deep Learning, which is a key to unlocking the latest sophisticated form of AI, comparable to human’s form of intelligence. AI is already present in our everyday lives, for example face recognition at airports, background generation in video conferencing, etc. You cannot understand CHAT GPT and Large LLMs if you don’t understand generative modeling and deep...

In the last decade, Neural Networks (NN) have attracted a lot of research due to their immense application potential. Breakthroughs of Deep Learning in image classification, speech recognition, and other challenging areas have provided the best solutions to many problems and significantly advanced “state of the art” AI machines in their ability to learn from data. This course will introduce students to fundamental concepts of Deep Neural Network (DNN) development. It will cover important algorithms and modelling used in the development of these networks. Utilizing rich sets of available NN...

Master Deep Learning with TensorFlow & PyTorch: End-to-End Neural Network Projects Unlock the skills to navigate the dynamic world of deep learning with this comprehensive course! From understanding when to use pre-trained models versus building your own, to diving into the critical algorithms behind neural networks, you will gain the foundational knowledge to thrive as an Artificial Intelligence (AI) practitioner. Explore cutting-edge machine learning techniques and deep neural network architectures such as CNNs, FCNNs, ResNet, RNNs, Transformers, Large Language Models, and more. Learn the...

Responsible AI: Ethical Implications and Societal Impact Dive into the ethical, social, and political dimensions of artificial intelligence (AI) with our Responsible AI course. Explore how AI technologies influence fairness, privacy, bias, democracy, and social justice. Through engaging case studies, dynamic discussions, and practical hands-on exercises, students will gain a deep understanding of the ethical challenges and societal implications of AI. Learn to develop a comprehensive framework for designing, developing, and deploying AI systems responsibly, ensuring fairness, transparency,...

Deep Learning is a layered version of Machine Learning that began in the 1980s with Image Processing Engineering and the popular Computer Vision (CV) application. Teaching computer prgrams to see and understand the content of images and videos is CV as the base model architecture of the course. However, Deep Learning has broadened its applications in the past decade and is now an extension of other Data Science frameworks. This course is a comprehensive introduction to Deep Learning as an extension of Supervised Learning, Unsupervised Learning, Reinforcement Learning, Graph Data Science,...

This course provides a comprehensive introduction to Natural Language Processing (NLP), tracing its evolution from rule-based systems to modern machine learning approaches. Students will learn foundational techniques such as text preprocessing, tokenization, stemming, lemmatization, and word embeddings, enabling machines to interpret the semantic meaning of words in context. The course explores deep learning architectures like Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks, essential for tasks like language modeling and machine translation. Advanced topics...

This course provides the end-to-end description from quantum phenomena to quantum computing hardware, software, and algorithms to hands-on use of IBM Q systems. This course is specifically designed to educate business users of quantum computers without requirements of advanced physics and mathematics for quantum mechanics. The course will compose the following four parts: I: Why we need to engage in quantum computing education? How can we effectively engage quantum computing II: Intuitive & graphic explanation of quantum phenomena, including wave-particle duality, superposition,...

The course aims at helping students to understand Fintech and solve practical AI problems they may encounter in field of finance. The course is designed for practitioners working at financial institutions such as banks, asset management firms or hedge funds, individuals interested in applications of AI for personal trading/portfolio management and current students pursuing a degree in Finance, Statistics, Computer Science, Mathematics or other related disciplines who want to learn practical applications of AI in Finance. A learner with some or no previous knowledge of AI/Machine Learning...

Unlock the power of AI and ML without coding – Empowering you to shape the future, ethically and innovatively. Explore the foundational principles of artificial intelligence (AI) and machine learning (ML) in this introductory course, designed to be accessible to all, regardless of coding experience. Discover the vast potential of AI/ML in addressing real-world challenges across diverse industries. Through hands-on exercises on no-code platforms, participants will master the art of constructing, training, and deploying AI/ML models. Gain insights into essential AI terminology and delve into...

This course offers an intuitive introduction to Quantum Machine Learning (QML) algorithms, combined with hands-on experience using IBM Qiskit and Pennylane platforms. Designed for those with an interest in AI and ML, the course does not require advanced knowledge of quantum mechanics, making it accessible to students from various backgrounds. It focuses on how quantum computing can enhance classical AI and ML computations, providing practical tools to leverage the power of quantum technologies. Course Learning Outcomes: By the end of this course students will Gain knowledge in foundation of...