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.

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.

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. 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.

IBM AI Engineering with Python, PyTorch & TensorFlow Professional Certificate

IBM AI Engineering with Python, PyTorch & TensorFlow Professional Certificate

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

Instructors: Wojciech 'Victor' Fulmyk

Included with Coursera Plus

Learn more

6 course series

Earn a career credential that demonstrates your expertise

4.5

(7,986 reviews)

Intermediate level

Some related experience required

3 months at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

6 course series

Earn a career credential that demonstrates your expertise

4.5

(7,986 reviews)

Intermediate level

Some related experience required

3 months at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll 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

Overview

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.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

68 practice exercises

Professional Certificate - 6 course series

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.

Machine Learning with Python

Course 120 hours4.7 (17,918 ratings)View course

What you'll learn

Explain key concepts, tools, and roles involved in machine learning, including supervised and unsupervised learning techniques.
Apply core machine learning algorithms such as regression, classification, clustering, and dimensionality reduction using Python and scikit-learn.
Evaluate model performance using appropriate metrics, validation strategies, and optimization techniques.
Build and assess end-to-end machine learning solutions on real-world datasets through hands-on labs, projects, and practical evaluations.

Skills you'll gain

Machine Learning, Supervised Learning, Unsupervised Learning, Regression Analysis, Dimensionality Reduction, Decision Tree Learning, Predictive Modeling, Applied Machine Learning, Scikit Learn (Machine Learning Library), Classification And Regression Tree (CART), Feature Engineering, and Statistical Modeling

Introduction to Deep Learning & Neural Networks with Keras

Course 210 hours4.7 (2,001 ratings)View course

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

Deep Learning, Artificial Neural Networks, Keras (Neural Network Library), Network Architecture, PyTorch (Machine Learning Library), Image Analysis, Tensorflow, Regression Analysis, Computer Vision, Machine Learning, Natural Language Processing, and Machine Learning Algorithms

Introduction to Computer Vision and Image Processing

Course 321 hours4.3 (1,398 ratings)View course

What you'll learn

Describe the applications of computer vision across different industries.
Apply image processing and analysis techniques to computer vision problems.
Utilize Python, Pillow, and OpenCV for basic image processing and perform image classification and object detection.
Create an image classifier using Supervised learning techniques.

Skills you'll gain

Image Analysis, Computer Vision, Deep Learning, Algorithms, Machine Learning, Artificial Neural Networks, Machine Learning Algorithms, Artificial Intelligence and Machine Learning (AI/ML), Cloud Applications, Visualization (Computer Graphics), Computer Programming, Application Deployment, Supervised Learning, Jupyter, Applied Machine Learning, Cloud Development, and Data Processing

Introduction to Neural Networks and PyTorch

Course 416 hours4.4 (1,870 ratings)View course

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

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

Deep Learning with Keras and Tensorflow

Course 523 hours4.4 (999 ratings)View course

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

Keras (Neural Network Library), Tensorflow, Deep Learning, Performance Tuning, Reinforcement Learning, Unsupervised Learning, Natural Language Processing, Artificial Intelligence, Generative AI, Machine Learning Methods, Artificial Intelligence and Machine Learning (AI/ML), and Artificial Neural Networks

AI Capstone Project with Deep Learning

Course 614 hours4.5 (670 ratings)View course

What you'll learn

Demonstrate your hands-on skills in building deep learning models using Keras and PyTorch to solve real-world image classification problems
Showcase your expertise in designing and implementing a complete deep learning pipeline, including data loading, augmentation, and model validation
Highlight your practical skills in applying CNNs and vision transformers to domain-specific challenges like geospatial land classification
Communicate your project outcomes effectively through a model evaluation

Skills you'll gain

PyTorch (Machine Learning Library), Keras (Neural Network Library), Deep Learning, Data Pipelines, Computer Vision, Python Programming, Geospatial Information and Technology, Machine Learning Methods, Artificial Intelligence, and Machine Learning

Earn a career certificate

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

Build toward a degree

When you complete this Professional Certificate, you may be able to have your learning recognized for credit if you are admitted and enroll in one of the following online degree programs.¹

Instructors

Wojciech 'Victor' Fulmyk

IBM

8 Courses86,979 learners

Ricky Shi

IBM

2 Courses53,146 learners

Aman Aggarwal

IBM

1 Course37,704 learners

Tenzin Migmar

IBM

2 Courses41,186 learners

Romeo Kienzler

IBM

10 Courses794,744 learners

Offered by

IBM

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Frequently asked questions

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

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

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.

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:

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

IBM AI Engineering with Python, PyTorch & TensorFlow Professional Certificate

What you'll learn

Overview

Skills you'll gain

Tools you'll learn

What’s included