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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About this Course
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Shareable Certificate
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Course 6 of 6 in the
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Approx. 16 hours to complete
English
Subtitles: English
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: English
Offered by
IBM
IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame.
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)
4 videos
Choose One Library Only (PyTorch or Keras)41s
1.0 Loading data.1m
Keras Stream3m
3 practice exercises
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Download Data10m
Loading Data with Keras30m
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)
1 video
2 practice exercises
Data loader PyTorch15m
Data Preparation with Keras30m
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)
1 video
2 practice exercises
Linear Classifier PyTorch10m
Building a Classifier with Pre-Trained Models30m
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), 1 reading, 2 quizzes
1 reading
Peer Review Instruction10m
Reviews
TOP REVIEWS FROM AI CAPSTONE PROJECT WITH DEEP LEARNING
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.
by SKMay 6, 2020
The Course is good, The labs were crashing which were causing lot of issues in completing the course
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.
You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.
Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.
In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
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What will I get if I subscribe to this Certificate?
When you enroll in the course, you get access to all of the courses in the Certificate, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Will I earn university credit for completing the Course?
This Course doesn't carry university credit, but some universities may choose to accept Course Certificates for credit. Check with your institution to learn more. Online Degrees and Mastertrack™ Certificates on Coursera provide the opportunity to earn university credit.
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