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 for Machine Learning 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...

Fundamentals of Data Science 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...

Probability and Statistics for Deep Learning 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...

Practicum for Deep Neural Networks 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....

Deep Learning Using TensorFlow Deep Learning is a branch of Artificial Intelligence (AI) that relies on the architecture of Neural Networks. When the number of hidden layers in a neural network is increased, it transforms into a ‘Deep Learning’ Neural Network. This course begins by explaining the concepts of Neural Networks and Deep Learning. Subsequently, it delves into the basics of the Python language and TensorFlow. The process of installing TensorFlow, Keras, and other interfaces is covered. Following that, it explores various Machine Learning modeling techniques for both estimation...

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...

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,...

Artificial Intelligence for Finance 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...

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 programs 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,...

The main goal of Natural Language Processing (NLP) is to comprehend the meaning of text semantics. Currently, there are two distinct approaches to NLP. In the first approach, the fundamental mathematical analysis of NLP will be covered. Students will write Python code to access NLTK, TextBlob software packages, breaking down text into tokens. The tokenization process will be covered using Regular Expressions, NLTK and TextBlob software. Text tokens will be converted into vectors and vectorization process, including count vectorizer, cosine similarity computation and TF-IDF (Term Frequency...

Intuitive Learning of Quantum Computing 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...