Deep Learning

شهادة احترافية
Offered By : IBM

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 skills required to become successful AI practitioners and start a career in applied Deep Learning.

متوسط

الإنجليزية

6 مقررات.

Instructors

Alex Aklson

Alex Aklson

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-centred, data-driven approach. Alex received his Ph.D. in Biomedical Engineering from the University of Toronto.

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Aije Egwaikhide

Aije Egwaikhide

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. She is currently pursuing her Masters's in Management Analytics at Queens University. She is a current employee of IBM where she started as a Junior Data Scientist at the Global Business Services (GBS) in 2018. Her main role was making meaning out of data for their Oil and Gas clients through basic statistics and advanced Machine Learning algorithms. The highlight of her time in GBS was creating a customized end-to-end Machine learning and Statistics solution on optimizing operations in the Oil and Gas wells. She is part of the IBM Developer Skills Network group where she brings her real-world experience to the courses she creates. Aije uses her voice through social media to empower young women and directly/indirectly mentoring them on career paths in the STEM field.

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Joseph Santarcangelo

Joseph Santarcangelo

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.

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Saeed Aghabozorgi

Saeed Aghabozorgi

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 knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.

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Romeo Kienzler

Romeo Kienzler

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 Enineering, Database Administration and Information Integration. Since 2012 he works as a Data Scientist for IBM. He published several works in the field with international publishers and on conferences. His current research focus is on massive parallel data processing architectures. Romeo also contributes to various open source projects.

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Samaya Madhavan

Samaya Madhavan

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, experienced in offering AI based solutions within the healthcare domain. Samaya has her Bachelor of Engineering in Computer Science from College of Engineering, Guindy and her Master of Science in Computer Science from University of Texas at Arlington. She is an ardent learner and a very passionate algorithm solver.

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