Professional Courses in AI and

Machine Learning

Build a strong internal team of AI experts with our courses in machine learning, AI, data science, and predictive analytics.

General Information

We provide professional courses in many areas of artificial intelligence, machine learning, data mining, and data science. Our schedule is flexible and adapts to the client’s needs. Courses combine conceptual and practical sessions to ensure participants are ready to use modern data analysis technologies in realistic scenarios.

Courses are offered in real-time (no pre-recorded lectures) and are either face-to-face or conducted as webinars. Each course is fourteen hours long and can be provided on two full days or four half days. The minimum number of participants per course is four. If you are interested, please get in touch with us, and a representative will respond shortly.

Introduction to Machine Learning

Introduction to Artificial Intelligence

Objectives

The goal is to ensure a good understanding of basic concepts in machine learning and data analytics to do both supervised and unsupervised learning, in addition to all steps involved in data science projects (data preparation, cleansing, and interpretation).

Topics

Decision trees, support vector machines, neural networks, probabilistic learning, ensemble learning (random forests, boosting), and clustering.

Objectives

The goal is to provide a comprehensive study of multiple techniques in artificial intelligence, aiming to build software systems that exhibit intelligent behavior. The central idea is to create rational agents that can solve complex tasks in real applications.

Topics

Search techniques, genetic algorithms, logic, reasoning, planning, reinforcement learning, maximizing utility, vision, and robotics.

Advanced Machine Learning

Objectives

The goal is to go beyond an introductory course in machine learning to cover advanced topics that can enhance the efficiency and accuracy of induced models. The course assumes a good understanding of basic machine learning techniques.

Topics

Meta-learning, self-adaptive learning, transfer learning, domain adaptation, semi-supervised learning, active learning, kernel methods, and Gaussian processes.

Deep Learning

Objectives

The goal is to offer an understanding of the mechanisms behind modern deep neural network architectures. The course assumes a good background in basic machine learning.

Topics

Deep feedforward networks, convolutional networks, recurrent networks, deep reinforcement learning, autoencoders, deep generative models, and adversarial models.