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IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate
  • About
  • Outcomes
  • Courses
  • Testimonials
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  2. Data Science
  3. Machine Learning
IBM

IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate

Fast-track your deep learning engineering career. Build the deep learning expertise employers are looking for in just 3 months

Wojciech 'Victor' Fulmyk
Ricky Shi
Romeo Kienzler

Instructors: Wojciech 'Victor' Fulmyk

Instructors

Wojciech 'Victor' Fulmyk
Wojciech 'Victor' Fulmyk
IBM
7 Courses•63,398 learners
Ricky Shi
Ricky Shi
IBM
1 Course•42,867 learners
Romeo Kienzler
Romeo Kienzler
IBM
10 Courses•767,303 learners
Joseph Santarcangelo
Joseph Santarcangelo
IBM
35 Courses•2,029,723 learners
Alex Aklson
Alex Aklson
IBM
22 Courses•1,293,911 learners
Samaya Madhavan
Samaya Madhavan
IBM
1 Course•42,867 learners
JEREMY NILMEIER
JEREMY NILMEIER
IBM
1 Course•42,867 learners

5,258 already enrolled

Included with Coursera Plus

•Learn more
5 course series
Earn a career credential that demonstrates your expertise
4.4

(127 reviews)

Intermediate level

Recommended experience

Recommended experience

Intermediate level

Basic Python knowledge is required, plus knowledge of foundationalPyTorch, machine learning, and neural networks.

2 months
at 10 hours a week
Flexible schedule
Learn at your own pace

5 course series
Earn a career credential that demonstrates your expertise
4.4

(127 reviews)

Intermediate level

Recommended experience

Recommended experience

Intermediate level

Basic Python knowledge is required, plus knowledge of foundationalPyTorch, machine learning, and neural networks.

2 months
at 10 hours a week
Flexible schedule
Learn at your own pace
  • About
  • Outcomes
  • Courses
  • Testimonials

What you'll learn

  • Job-ready deep learning skills using PyTorch, Keras, and TensorFlow employers are looking for - in just 3 months!

  • How to create shareable projects, deep learning models, and neural networks using Keras and PyTorch.

  • How to train linear and logistic regression models, optimize with gradient descent using PyTorch, and create custom models with Keras.

  • How to build advanced CNNs and transformer models and build CNNs with effective layers and activations… and more.

Skills you'll gain

  • Reinforcement Learning
  • Scientific Visualization
  • Verification And Validation
  • Machine Learning Methods
  • Generative AI
  • Computer Vision
  • Artificial Intelligence
  • Tensorflow
  • Predictive Modeling
  • Applied Machine Learning
  • Unsupervised Learning
  • PyTorch (Machine Learning Library)

Details to know

Shareable certificate

Add to your LinkedIn profile

Taught in English

See how employees at top companies are mastering in-demand skills

Learn more about Coursera for Business
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Advance your career with in-demand skills

  • Receive professional-level training from IBM
  • Demonstrate your technical proficiency
  • Earn an employer-recognized certificate from IBM

Professional Certificate - 5 course series

The global deep learning market is set to grow 23% annually to 2030 (Grand View Research). This IBM Deep Learning with PyTorch, Keras and TensorFlow Professional Certificate builds the job-ready skills and practical experience AI techies need to catch the eye of employers.

Deep learning is a branch of machine learning powering the generative AI revolution. It uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain.

During the program, you’ll learn to build, train, and deploy deep learning models. You’ll master fundamental concepts of machine learning and deep learning, including supervisedlearning, using Python. You’ll learn to develop transformer models for sequential data and time series predictions and apply unsupervised learning and reinforcement learning. Plus, you’ll apply popular libraries such as Keras, PyTorch, and TensorFlow to industry problems using object recognition,image and natural language processing. You’ll also gain valuable hands-on experience in labs and projects using PyTorch with deep learning models, creating custom layers and models using Keras, integrating Keras with TensorFlow 2, and developing advanced convolutional neural networks (CNNs). If you’re looking to take the next step in your AI or data science career, this IBM Professional Certificate will give you job-ready skills and practical experience employers are looking for, so ENROLL TODAY!

Applied Learning Project

Throughout this program, you’ll build a portfolio of projects that demonstrate your mastery of deep learning. During the hands-on projects, you’ll gain practical working skills using machine learning libraries and deep learning frameworks, including Keras, PyTorch, and TensorFlow.

You’ll also complete an in-depth capstone project, where you’ll apply your AI and neural network skills to build image classifiers, analyze their performance and present your findings.

Introduction to Deep Learning & Neural Networks with Keras

Introduction to Deep Learning & Neural Networks with Keras

Course 1•10 hours

What you'll learn

  • Describe the foundational concepts of deep learning, neurons, and artificial neural networks to solve real-world problems

  • Explain the core concepts and components of neural networks and the challenges of training deep networks

  • Build deep learning models for regression and classification using the Keras library, interpreting model performance metrics effectively.

  • Design advanced architectures, such as CNNs, RNNs, and transformers, for solving specific problems like image classification and language modeling

Skills you'll gain

Category: Deep Learning
Deep Learning
Category: Keras (Neural Network Library)
Keras (Neural Network Library)
Category: Artificial Neural Networks
Artificial Neural Networks
Category: Network Architecture
Network Architecture
Category: Computer Vision
Computer Vision
Category: Network Model
Network Model
Category: Machine Learning Methods
Machine Learning Methods
Category: Regression Analysis
Regression Analysis
Category: Machine Learning
Machine Learning
Category: Image Analysis
Image Analysis
Category: Natural Language Processing
Natural Language Processing
Deep Learning with Keras and Tensorflow

Deep Learning with Keras and Tensorflow

Course 2•23 hours

What you'll learn

  • Create custom layers and models in Keras and integrate Keras with TensorFlow 2.x

  • Develop advanced convolutional neural networks (CNNs) using Keras

  • Develop Transformer models for sequential data and time series prediction

  • Explain key concepts of Unsupervised learning in Keras, Deep Q-networks (DQNs), and reinforcement learning

Skills you'll gain

Category: Tensorflow
Tensorflow
Category: Keras (Neural Network Library)
Keras (Neural Network Library)
Category: Deep Learning
Deep Learning
Category: Reinforcement Learning
Reinforcement Learning
Category: Generative AI
Generative AI
Category: Unsupervised Learning
Unsupervised Learning
Category: Time Series Analysis and Forecasting
Time Series Analysis and Forecasting
Category: Image Analysis
Image Analysis
Category: Performance Tuning
Performance Tuning
Category: Natural Language Processing
Natural Language Processing
Category: Artificial Intelligence and Machine Learning (AI/ML)
Artificial Intelligence and Machine Learning (AI/ML)
Category: Artificial Intelligence
Artificial Intelligence
Category: Artificial Neural Networks
Artificial Neural Networks
Introduction to Neural Networks and PyTorch

Introduction to Neural Networks and PyTorch

Course 3•17 hours

What you'll learn

  • Job-ready PyTorch skills employers need in just 6 weeks

  • How to implement and train linear regression models from scratch using PyTorch’s functionalities

  • Key concepts of logistic regression and how to apply them to classification problems

  • How to handle data and train models using gradient descent for optimization 

Skills you'll gain

Category: PyTorch (Machine Learning Library)
PyTorch (Machine Learning Library)
Category: Regression Analysis
Regression Analysis
Category: Machine Learning
Machine Learning
Category: Predictive Modeling
Predictive Modeling
Category: Data Manipulation
Data Manipulation
Category: Artificial Neural Networks
Artificial Neural Networks
Category: Probability & Statistics
Probability & Statistics
Category: Tensorflow
Tensorflow
Category: Deep Learning
Deep Learning
Deep Learning with PyTorch

Deep Learning with PyTorch

Course 4•20 hours

What you'll learn

  • Key concepts on Softmax regression and understand its application in multi-class classification problems.

  • How to develop and train shallow neural networks with various architectures.

  • Key concepts of deep neural networks, including techniques like dropout, weight initialization, and batch normalization.

  • How to develop convolutional neural networks, apply layers and activation functions.

Skills you'll gain

Category: Deep Learning
Deep Learning
Category: PyTorch (Machine Learning Library)
PyTorch (Machine Learning Library)
Category: Artificial Neural Networks
Artificial Neural Networks
Category: Regression Analysis
Regression Analysis
Category: Artificial Intelligence and Machine Learning (AI/ML)
Artificial Intelligence and Machine Learning (AI/ML)
Category: Supervised Learning
Supervised Learning
Category: Applied Machine Learning
Applied Machine Learning
Category: Computer Vision
Computer Vision
AI Capstone Project with Deep Learning

AI Capstone Project with Deep Learning

Course 5•16 hours

What you'll learn

  • Build a deep learning model to solve a real problem.

  • Execute 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.

Skills you'll gain

Category: Keras (Neural Network Library)
Keras (Neural Network Library)
Category: Image Analysis
Image Analysis
Category: PyTorch (Machine Learning Library)
PyTorch (Machine Learning Library)
Category: Data Processing
Data Processing
Category: Deep Learning
Deep Learning
Category: Applied Machine Learning
Applied Machine Learning
Category: Predictive Modeling
Predictive Modeling
Category: Computer Vision
Computer Vision
Category: Artificial Neural Networks
Artificial Neural Networks
Category: Data Manipulation
Data Manipulation
Category: Scientific Visualization
Scientific Visualization
Category: Verification And Validation
Verification And Validation

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructors

Wojciech 'Victor' Fulmyk
Wojciech 'Victor' Fulmyk
IBM
7 Courses•63,398 learners
Ricky Shi
Ricky Shi
IBM
1 Course•42,867 learners

Instructors

Wojciech 'Victor' Fulmyk
Wojciech 'Victor' Fulmyk
IBM
7 Courses•63,398 learners
Ricky Shi
Ricky Shi
IBM
1 Course•42,867 learners
Romeo Kienzler
Romeo Kienzler
IBM
10 Courses•767,303 learners
Joseph Santarcangelo
Joseph Santarcangelo
IBM
35 Courses•2,029,723 learners
Alex Aklson
Alex Aklson
IBM
22 Courses•1,293,911 learners
Samaya Madhavan
Samaya Madhavan
IBM
1 Course•42,867 learners
JEREMY NILMEIER
JEREMY NILMEIER
IBM
1 Course•42,867 learners

Offered by

IBM

Offered by

IBM

At IBM, we know how rapidly tech evolves and recognize the crucial need for businesses and professionals to build job-ready, hands-on skills quickly. As a market-leading tech innovator, we’re committed to helping you thrive in this dynamic landscape. Through IBM Skills Network, our expertly designed training programs in AI, software development, cybersecurity, data science, business management, and more, provide the essential skills you need to secure your first job, advance your career, or drive business success. Whether you’re upskilling yourself or your team, our courses, Specializations, and Professional Certificates build the technical expertise that ensures you, and your organization, excel in a competitive world.

Why people choose Coursera for their career

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"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
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Frequently asked questions

This program is for anyone interested in mastering Deep Learning.  This program is ideal for professionals already in related roles, such as data scientists, software engineers, machine learning engineers, data engineers, and Python developers, who want to transition to a rewarding AI engineering career.

To take this program, you need to have basic computer literacy, knowledge of machine learning and neural network fundamentals, and a working knowledge of Python.

This course is completely online, so there’s no need to show up to a classroom in person. You can access the lectures, readings, and assignments anytime and anywhere via your desktop or your mobile device. 

This program requires working knowledge of Python to process and analyze data and build, train, and test machine learning models, as well as high school level math.

Yes, it is highly recommended that you take courses in the order they are listed because each course progressively builds on concepts taught in previous courses. 

Deep learning engineers design and train neural network models to analyze large datasets and recognize patterns in images, audio, or text. They handle data preparation, model tuning, evaluation, and deployment to ensure models perform effectively in real-world applications. Their work powers innovations in AI, such as self-driving cars, virtual assistants, and medical diagnostics.

Deep learning engineers need strong programming skills, especially in Python and frameworks like TensorFlow or PyTorch, along with expertise in mathematics, data handling, and machine learning concepts. Deep learning engineers need analytical thinking and continuous learning to optimize model performance and stay current with technical advancements.This IBM Professional Certificate provides hands-on training, preparing you for your first deep learning engineering role.

Absolutely, yes!The U.S. Bureau of Labor Statistics forecasts a 23% growth rate for machine learning engineering jobs (which includes deep learning) from 2022 to 2032.The top industries hiring deep learning engineers include technology and software, healthcare, finance and insurance, autonomous vehicles and robotics, and e-commerce and retail. These industries are using deep learning for various applications, from natural language processing and computer vision to medical diagnostics, financial risk assessment, self-driving cars, and personalized recommendations, reflecting the widespread adoption and growing importance of AI technologies across diverse sectors.

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 policyOpens in a new tab.

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.

More questions

Visit the learner help center

Financial aid available,

¹Based on Coursera learner outcome survey responses, United States, 2021.

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