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
AI Capstone Project with Deep Learning
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About this Course
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Advanced Level
Approx. 11 hours to complete
Suggested: 20 hours/week
English
Subtitles: English
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
4.3
13 ReviewsTop reviews from AI Capstone Project with Deep Learning
But need to study extra as these topics are not taught like Transfer Learning
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.
About the IBM AI Engineering Professional Certificate
The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.
You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.
You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.
By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
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?
More questions? Visit the Learner Help Center.
