Launch your career as an AI engineer. Learn how to provide business insights from big data using machine learning and deep learning techniques.

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Credit eligible towards Bachelor of Science in Computer Science degree

University of London

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IBM AI Engineering Professional CertificateIBM

What you will learn

Describe machine learning, deep learning, neural networks, and ML algorithms like classification, regression, clustering, and dimensional reduction

Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn

Deploy machine learning algorithms and pipelines on Apache Spark

Build deep learning models and neural networks using Keras, PyTorch, and TensorFlow

Skills you will gain

Python Libraries
Machine Learning
regression
Hierarchical Clustering
K-Means Clustering
Deep Learning
Artificial Neural Network
Artificial Intelligence (AI)
keras
Opencv
Image Processing
Computer Vision

About this Professional Certificate

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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.

Applied Learning Project

Throughout the program, you will build a portfolio of projects demonstrating your mastery of course topics. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning frameworks such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow. You will also complete an in-depth Capstone Project, where you’ll apply your AI and Neural Network skills to a real-world challenge and demonstrate your ability to communicate project outcomes.

Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Some related experience required.

Approximately 9 months to complete

Suggested pace of 3 hours/week

English

Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, Spanish

Shareable Certificate

Earn a Certificate upon completion

100% online courses

Start instantly and learn at your own schedule.

Flexible Schedule

Set and maintain flexible deadlines.

Intermediate Level

Some related experience required.

Approximately 9 months to complete

Suggested pace of 3 hours/week

English

Subtitles: English, Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, Spanish

What is a Professional Certificate?

Build the Skills to Get Job Ready

Whether you’re looking to start a new career, or change your current one, Professional Certificates on Coursera help you become job ready. Learn at your own pace, whenever and wherever it’s most convenient for you. Enroll today and explore a new career path with a 7 day free trial. You can pause your learning or end your subscription at any time.

Hands-On Projects

Apply your skills with hands-on projects and build a portfolio that showcases your job readiness to potential employers. You'll need to successfully finish the project(s) to earn your Certificate.

Earn a Career Credential

When you complete all of the courses in the program, you'll earn a Certificate to share with your professional network as well as unlock access to career support resources to help you kickstart your new career. Many Professional Certificates have hiring partners that recognize the Professional Certificate credential and others can help prepare you for a certification exam. You can find more information on individual Professional Certificate pages where it applies.

There are 6 Courses in this Professional Certificate

Course1

Course 1

Machine Learning with Python

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2,162 reviews

This course dives into the basics of machine learning using an approachable, and well-known programming language, Python.

In this course, we will be reviewing two main components: First, you will be learning about the purpose of Machine Learning and where it applies to the real world. Second, you will get a general overview of Machine Learning topics such as supervised vs unsupervised learning, model evaluation, and Machine Learning algorithms. In this course, you practice with real-life examples of Machine learning and see how it affects society in ways you may not have guessed! By just putting in a few hours a week for the next few weeks, this is what you’ll get. 1) New skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy 2) New projects that you can add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines, and many more. 3) And a certificate in machine learning to prove your competency, and share it anywhere you like online or offline, such as LinkedIn profiles and social media. If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge upon successful completion of the course.

Course2

Course 2

Introduction to Deep Learning & Neural Networks with Keras

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stars

1,055 ratings

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209 reviews

Looking to start a career in Deep Learning? Look no further. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks? You will learn about the different deep learning models and build your first deep learning model using the Keras library.

After completing this course, learners will be able to: • Describe what a neural network is, what a deep learning model is, and the difference between them. • Demonstrate an understanding of unsupervised deep learning models such as autoencoders and restricted Boltzmann machines. • Demonstrate an understanding of supervised deep learning models such as convolutional neural networks and recurrent networks. • Build deep learning models and networks using the Keras library.

Course3

Course 3

Introduction to Computer Vision and Image Processing

4.4

stars

778 ratings

•

158 reviews

Computer Vision is one of the most exciting fields in Machine Learning and AI. It has applications in many industries, such as self-driving cars, robotics, augmented reality, and much more. In this beginner-friendly course, you will understand computer vision and learn about its various applications across many industries.

As part of this course, you will utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection. This is a hands-on course and involves several labs and exercises. Labs will combine Jupyter Labs and Computer Vision Learning Studio (CV Studio), a free learning tool for computer vision. CV Studio allows you to upload, train, and test your own custom image classifier and detection models. At the end of the course, you will create your own computer vision web app and deploy it to the Cloud. This course does not require any prior Machine Learning or Computer Vision experience. However, some knowledge of the Python programming language and high school math is necessary.

Course4

Course 4

Deep Neural Networks with PyTorch

4.4

stars

1,098 ratings

•

245 reviews

The course will teach you how to develop deep learning models using Pytorch. The course will start with Pytorch's tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered.

Instructors

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.

Earn credit towards your degree

Upon completing this certificate, you can earn college credit when you’re accepted into Bachelor of Science in Computer Science from University of London.

Bachelor of Science in Computer Science

University of London

Frequently Asked Questions

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.
Can I just enroll in a single course?
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.
Is this course really 100% online? Do I need to attend any classes in person?
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
How can I earn my IBM Badge?
Upon completion of the program, you will receive an email from Acclaim with your IBM Badge recognising your expertise in the field. Some badges are issues almost immediately after completion of the badge activities, while others may take 1-2 weeks before they are issued. Once issued, you will receive a notification email from admin@youracclaim.com with instructions for claiming the badge. Learn more about IBM Badges
What are some examples of careers in artificial intelligence?
An understanding of artificial intelligence can be used to support many careers, but some careers specifically require a background in AI. Some examples of careers in AI include:
- AI Developer
- Data Analyst
- Data Engineer
- Data Scientist
- Machine Learning Engineer
- Marketing Analyst
- Operations Analyst
- Quantitative Analyst
- Software Analyst
- Software Developer
- Software Engineer
- User Experience Engineer
How long does it take to complete the Professional Certificate?
This Professional Certificate consists of 6 self-paced courses. Each course takes 4-5 weeks to complete if you spend 2-4 hours working through the course per week. At this rate, the entire Professional Certificate can be completed in 3-6 months. However, you are welcome to complete the program more quickly or more slowly, depending on your preference.
What background knowledge is necessary?
This Professional Certificate's pre-requisites includes the following skills:
Working knowledge of Python and Jupyter Notebooks (Don't have these skills? Try taking the Python for Data Science and course)
High school mathematics or math for machine learning
It is highly recommended that you complete either or both of the following Professional Certificates before starting this one:
IBM Data Science Professional Certificate
IBM Applied AI Professional Certificate
Do I need to take the courses in a specific order?
It is highly recommended to complete the courses in the suggested order.
Will I earn university credit for completing the Professional Certificate?
At this time there is no university credit for completing courses in this program.
What will I be able to do upon completing the Professional Certificate?
Upon completing this Professional Certificate you will be able to:
Describe what machine learning (ML), deep learning (DL), and neural networks are
Explain ML algorithms including classification, regression, clustering, and dimensional reduction
Implement supervised and unsupervised ML models using Scipy and Scikitlearn
Express how Apache Spark works and how to perform machine learning on big data
Deploy ML algorithms and pipelines on Apache Spark
Demonstrate an understanding of deep learning models such as autoencoders, restricted Boltzmann machines, convolutional networks, recursive neural networks, and recurrent networks
Build deep learning models and neural networks using the Keras library
Utilize the PyTorch library for deep learning applications and build deep neural networks
Explain foundational TensorFlow concepts like main functions, operations & execution pipelines
Apply deep learning using TensorFlow and perform back propagation to tune the weights and biases
Determine what kind of deep learning method to use in which situation and build a deep learning model to solve a real problem
Demonstrate ability to present and communicate outcomes of deep learning projects

More questions? Visit the Learner Help Center.