Jeremy Nilmeier is a Data Scientist and Developer Advocate at IBM, where he teaches the course "Building Deep Learning Models with TensorFlow." This course provides a comprehensive introduction to deep learning using TensorFlow, covering topics such as neural network architectures, optimization techniques, and best practices for model development and deployment. Participants will gain hands-on experience building and training deep learning models for various applications, including computer vision and natural language processing.

Dr. Alex Aklson is a data scientist in IBM Canada’s Digital Business Group, where he has contributed to innovative projects, including the development of a smart system to detect early signs of dementia by analyzing walking speed and home activity patterns in older adults. Prior to IBM, Alex worked at Datascope Analytics in Chicago, where he crafted data-driven solutions using a human-centered approach. He holds a Ph.D. in Biomedical Engineering from the University of Toronto.

Dr. Joseph Santarcangelo, a Data Scientist at IBM, brings a unique blend of academic excellence and practical expertise to the field of data science and artificial intelligence. With a Ph.D. in Electrical Engineering, his groundbreaking research focused on the intersection of machine learning, signal processing, and computer vision to understand how video content influences human cognitive processes. At IBM, he has established himself as a prominent educator and course developer, creating comprehensive learning materials that have reached hundreds of thousands of students worldwide. His teaching portfolio encompasses a wide range of technical subjects, from foundational Python programming to advanced topics in artificial intelligence, machine learning, and computer vision. Santarcangelo's ability to translate complex technical concepts into accessible learning experiences has made him an influential figure in data science education, maintaining consistently high ratings from learners while continuing to push the boundaries of applied machine learning and artificial intelligence research.

Romeo Kienzler is the Chief Data Scientist and Course Lead at IBM, where he leverages nearly two decades of experience in software engineering, database administration, and information integration. He holds a Master of Science from the Swiss Federal Institute of Technology (ETH) in Information Systems, Bioinformatics, and Applied Statistics. Since joining IBM in 2012, Romeo has focused his research on massive parallel data processing architectures and has published numerous works in the field through international publishers and conferences. In addition to his professional contributions, he is actively involved in various open-source projects. On Coursera, he teaches several courses, including Deep Learning with Keras and TensorFlow, Introduction to Big Data with Spark and Hadoop, Scalable Machine Learning on Big Data using Apache Spark, and Tools for Data Science, all designed to equip learners with essential skills in data science and machine learning

Wojciech 'Victor' Fulmyk is an accomplished instructor at IBM, specializing in artificial intelligence and data science education. He teaches several online courses on Coursera, including Generative AI Advance Fine-Tuning for LLMs and Deep Learning with Keras and TensorFlow, where he imparts his extensive knowledge of machine learning and neural networks to a global audience. With a strong academic background as a Ph.D. candidate in Economics at the University of Calgary, Victor combines theoretical insights with practical applications in his teaching. His expertise is further demonstrated through his competitive success on platforms like Kaggle, where he has achieved top rankings in challenging data science competitions. Victor's commitment to advancing the field of AI education is evident in his innovative course offerings that prepare learners for the rapidly evolving tech landscape, making him a valuable asset to both IBM and the broader educational community.