This course is part of the IBM AI Engineering Professional Certificate

Alex Aklson +1 more instructor

Offered By

IBM AI Engineering Professional CertificateIBM

About this Course

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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.
Learning Outcomes:
•	determine what kind of deep learning method to use in which situation
•	know how to build a deep learning model to solve a real problem 
•	master the process of creating  a deep learning pipeline 
•	apply knowledge of deep learning to improve models using real data
•	demonstrate ability to present and communicate outcomes of deep learning projects

Shareable Certificate

Earn a Certificate upon completion

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Start instantly and learn at your own schedule.

Course 6 of 6 in the

IBM AI Engineering

Flexible deadlines

Reset deadlines in accordance to your schedule.

Advanced Level

Approx. 16 hours to complete

English

Subtitles: English

Instructors

Instructor rating

4.55/5 (23 Ratings)

Alex Aklson

Ph.D., Data Scientist

420,820 Learners

11 Courses

Joseph Santarcangelo

Ph.D., Data Scientist at IBM

IBM Developer Skills Network

395,849 Learners

8 Courses

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 6 of 6 in the

IBM AI Engineering

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

Week

Week 1

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

Introduction2m

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

Week

Week 2

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

2.0 Data Preparation with PyTorch1m

2 practice exercises

Data loader PyTorch15m

Data Preparation with Keras30m

Week

Week 3

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

Linear Classifier PyTorch Review1m

2 practice exercises

Linear Classifier PyTorch10m

Building a Classifier with Pre-Trained Models30m

Week

Week 4

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

1 video

Pre-trained models with Resnet-18 Review PyTorch2m

2 readings

Peer Review Instruction10m

Setup Watson Studio10m

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:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
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.

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AI Capstone Project with Deep Learning