In this capstone, learners will apply their deep learning knowledge and expertise to a real world challenge. They will use a library of their choice to develop and test a deep learning model. They will load and pre-process data for a real problem, build the model and validate it. Learners will then present a project report to demonstrate the validity of their model and their proficiency in the field of Deep Learning.
This course is part of the IBM AI Engineering Professional Certificate
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Course 6 of 6 in the
Flexible deadlines
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Advanced Level
Approx. 16 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Course 6 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 16 hours to complete
English
Subtitles: French, Portuguese (European), Russian, English, Spanish
Offered by
IBM
IBM is the global leader in business transformation through an open hybrid cloud platform and AI, serving clients in more than 170 countries around the world. Today 47 of the Fortune 50 Companies rely on the IBM Cloud to run their business, and IBM Watson enterprise AI is hard at work in more than 30,000 engagements. IBM is also one of the world’s most vital corporate research organizations, with 28 consecutive years of patent leadership. Above all, guided by principles for trust and transparency and support for a more inclusive society, IBM is committed to being a responsible technology innovator and a force for good in the world.
Syllabus - What you will learn from this course
3 hours to complete
Module 1 - Loading Data
In this module, you will get introduced to the problem that we will try to solve throughout the course. You will also learn how to load the image dataset, manipulate images, and visualize them.
3 hours to complete
4 videos (Total 8 min)
3 hours to complete
Module 2
In this Module, you will mainly learn how to process image data and prepare it to build a classifier using pre-trained models.
3 hours to complete
1 video (Total 2 min)
3 hours to complete
Module 3
In this Module, in the PyTorch part, you will learn how to build a linear classifier. In the Keras part, you will learn how to build an image classifier using the ResNet50 pre-trained model.
3 hours to complete
1 video (Total 2 min)
7 hours to complete
Module 4
In this Module, in the PyTorch part, you will complete a peer review assessment where you will be asked to build an image classifier using the ResNet18 pre-trained model. In the Keras part, for the peer review assessment, you will be asked to build an image classifier using the VGG16 pre-trained model and compare its performance with the model that we built in the previous Module using the ResNet50 pre-trained model.
7 hours to complete
1 video (Total 3 min), 2 readings, 2 quizzes
Reviews
TOP REVIEWS FROM AI CAPSTONE PROJECT WITH DEEP LEARNING
by TGJan 27, 2021
Great course content! One thing that can be improved is the Skills Network portal. It's incredibly difficult to train any of the pre-trained models there.
by RKJul 30, 2020
The capstone of the project was really good it helped me to understand the deep learning concepts clearly for providing the solution.
by DOMay 26, 2020
I like the flexibility to pick our framework for the project i wish the kers one were a little bit more challenging
by RBMay 22, 2020
A very nice project based course to get hands on experience with deep learning and transfer learning.
About the IBM AI Engineering Professional Certificate
Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.
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