14 Online Machine Learning Preparatory Programs

Upgrade your data skills and develop a technical understanding of the business applications of machine learning.

This Summer School aim to provide an overview of the main Machine Learning and Deep Learning methods and their application to geophysical, geological, environmental and geographical data, keeping a more practical flavour.

The goal of this course is to provide you with a quick but solid introduction to a theoretical foundations of machine learning.

Enhance your machine learning expertise by delving into more complex algorithms and real-world applications. This short course is aimed at professionals who are seeking to understand the concepts and technologies that underpin modern machine learning.

Gain foundational knowledge in machine learning, covering essential concepts and tools for building predictive models. Machine learning workflows are key to effective data science. This short course is focused on using Python packages to perform end-to-end data-driven analyses.

Master advanced machine learning techniques, including deep learning and neural networks, for sophisticated data analysis. This short course will give you the tools to understand the concepts and technologies that underpin modern deep learning using artificial neural networks (ANNs).

This programme provides you with a strong foundation in machine learning and AI, combined with practical skills in Python programming. You will learn to analyse data, build models and interpret results - skills that will position you as a key player in a rapidly growing industry.

An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. -- Part of the MITx MicroMasters program in Statistics and Data Science.

In our age of burgeoning smart technology and automation, we are already seeing the transformative potential of Artificial Intelligence and Machine Learning in fields as diverse as finance, medicine, and manufacturing. This course offers a hands-on introduction to this future-focused area of research.

The Artificial Intelligence and Machine Learning (AIML) program is designed to put you on the path to an exciting, evolving career that is predicted to grow sharply into 2025 and beyond. Artificial intelligence and Machine Learning will impact all segments of daily life by 2025, with applications in a wide range of industries such as healthcare, transportation, insurance, transport and logistics and even customer service.

Machine learning (ML) and artificial intelligence (AI) are transforming the way organizations do business and how consumers live. That's why IT professionals with the specialized knowledge and skills to develop the next generation of ML/AI technology innovations are in immediate demand globally and across industries. So how can you kick-start your career in this exciting, in-demand field? The Professional Certificate in Machine Learning and Artificial Intelligence from UC Berkeley (ranked the #1 university in the world by Forbes magazine) is built in collaboration with the College of Engineering and the Haas School of Business.

Build your understanding of how processes and production environments can be improved with DeVry University’s Associate in Engineering Technology with a specialization in Machine Learning and Design Techniques. Anchored by our Tech Core curriculum, this specialization offers the interdisciplinary fundamentals you need to analyze processes in the modern Internet of Things (IoT) economy.

Built for working professionals, this graduate-level Certificate in Artificial Intelligence and Machine Learning will help you to gain a competitive edge. Whether you're just starting out or a seasoned pro, this program provides essential skills to harness the full potential of AI and ML in your field.

Led by Aalto University experts, the Digital Twins master class will dive deeply into the different roles and uses of digital twins and explain how they enable asset-centric data management and cross-company data sharing. We will review the standardization landscape of digital twins and recognize that it is the right time to act in order to have an influence.