Artificial Intelligence & Data Science
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Professional Certificate in Deep Learning

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Course Features

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Duration

7 months

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Delivery Method

Online

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Available on

Limited Access

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Accessibility

Desktop, Laptop

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Language

English

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Subtitles

English

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Level

Beginner

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Effort

4 hours per week

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Teaching Type

Self Paced

Course Description

AI is changing the way we communicate, live, and work. Deep Learning is at the core of AI. Deep Learning was once a niche for researchers and PhDs. However, it has become mainstream due to its practical applications and availability of affordable technology. Deep Learning professionals and Data Scientists are in high demand. This is far more than the supply. AI is becoming a part of the fabric of the industry. As AI becomes more widespread in society, the demand for Deep Learning skills and the salaries of Deep Learning professionals will only increase. Deep Learning is a career that's future-proof.

This series of courses will introduce you to Deep Learning concepts and their applications, as well as various types of Neural Networks that can be used for both supervised and unsupervised learning. The next step is to delve deeper into Deep Learning and build models and algorithms with libraries such as Keras and PyTorch. Deep Learning will be possible with GPU-accelerated hardware. This includes image and video processing as well as object recognition in Computer Vision.

Through this program, you will be able to practice your Deep Learning skills by engaging in hands-on labs, assignments, as well as projects that are inspired by real problems and data sets from industry. The program will also include a capstone project in Deep Learning that will show potential employers your applied skills.

This program is designed to prepare and equip learners with the skills necessary to be successful AI practitioners and begin a career in applied Deep Learning.

Course Overview

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International Faculty

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Post Course Interactions

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Instructor-Moderated Discussions

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Case Studies, Captstone Projects

Skills You Will Gain

What You Will Learn

Fundamental concepts of Deep Learning, including various Neural Networks for supervised and unsupervised learning

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

Use of popular Deep Learning libraries such as Keras, PyTorch, and Tensorflow applied to industry problems

Course Instructors

Aije Egwaikhide

Senior Data Scientist

Aije Egwaikhide is a Data Scientist at IBM who holds a degree in Economics and Statistics from the University of Manitoba and a Post-grad in Business Analytics from St. Lawrence College, Kingston. Sh...

Romeo Kienzler

Chief Data Scientist

Romeo Kienzler holds a M. Sc. (ETH) in Information Systems, Bioinformatics & Applied Statistics (Swiss Federal Institute of Technology). He has nearly two decades of experience in Software Eninee...

Samaya Madhavan

Advisory Software Engineer

Samaya Madhavan is an Advisory Software Engineer with IBM where she currently publishes content that are related to machine learning and deep learning. She is also a full stack software developer, ex...

Saeed Aghabozorgi

PhD, Sr. Data Scientist

Saeed Aghabozorgi, PhD is a Sr. Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients' ability to turn data into actionable knowle...
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