What you will learn
- Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning.
- Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems.
- Build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
- Application of Deep Learning to real-world scenarios such as object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
- Master Deep Learning at scale with accelerated hardware and GPUs.
AI is revolutionizing the way we live, work, and communicate. At the heart of AI is Deep Learning. Once a domain of researchers and PhDs only, Deep Learning has now gone mainstream thanks to its practical applications and availability in terms of consumable technology and affordable hardware.
The demand for Data Scientists and Deep Learning professionals is booming, far exceeding the supply of personnel skilled in this field. The industry is clearly embracing AI, embedding it within its fabric. The demand for Deep Learning skills by employers — and the job salaries of Deep Learning practitioners — are only bound to increase over time, as AI becomes more pervasive in society. Deep Learning is a future-proof career.
Within this series of courses, you’ll be introduced to concepts and applications in Deep Learning, including various kinds of Neural Networks for supervised and unsupervised learning. You’ll then delve deeper and apply Deep Learning by building models and algorithms using libraries like Keras, PyTorch, and Tensorflow. You’ll also master Deep Learning at scale by leveraging GPU accelerated hardware for image and video processing, as well as object recognition in Computer Vision.
Throughout this program, you will practice your Deep Learning skills through a series of hands-on labs, assignments, and projects inspired by real-world problems and data sets from the industry. You’ll also complete the program by preparing a Deep Learning capstone project that will showcase your applied skills to prospective employers.
This program is intended to prepare learners and equip them with the skills required to become successful AI practitioners and start a career in applied Deep Learning.
IBM’s Deep Learning Professional Certificate
IBM is a cognitive solutions and cloud platform company headquartered in Armonk, NY. It is the largest technology and consulting employer in the world, serving clients in more than 170 countries. With 25 consecutive years of patent leadership, IBM Research is the world’s largest corporate research organization with more than 3,000 researchers in 12 labs located across six continents. For more information, visit www.ibm.com.
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Meet your instructors
Joseph Santarcangelo is currently working as a Data Scientist at IBM. Joseph has a Ph.D. in Electrical Engineering. His research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition.
Alex Aklson, Ph.D., is a data scientist in the Digital Business Group at IBM Canada. Alex has been intensively involved in many exciting data science projects such as designing a smart system that could detect the onset of dementia in older adults using longitudinal trajectories of walking speed and home activity. Before joining IBM, Alex worked as a data scientist at Datascope Analytics, a data science consulting firm in Chicago, IL, where he designed solutions and products using a human-centered, data-driven approach. Alex received his Ph.D. in Biomedical Engineering from the University of Toronto.
Saeed Aghabozorgi, PhD is a Sr. Data Scientist in IBM with a track record of developing enterprise-level applications that substantially increase clients’ ability to turn data into actionable knowledge. He is a researcher in the data mining field and expert in developing advanced analytic methods like machine learning and statistical modeling on large datasets.
- Lectures 5
- Quizzes 0
- Duration 28 weeks
- Skill level All levels
- Students 0
- Certificate No
- Assessments Yes