IBM
IBM AI Engineering Professional Certificate
IBM

IBM AI Engineering 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.

IBM Skills Network Team
Ricky Shi
Romeo Kienzler

Instructors: IBM Skills Network Team

101,482 already enrolled

Included with Coursera Plus

Earn a career credential that demonstrates your expertise
4.5

(6,498 reviews)

Intermediate level
Some related experience required
Flexible schedule
2 months, 10 hours a week
Learn at your own pace
Build toward a degree
Earn a career credential that demonstrates your expertise
4.5

(6,498 reviews)

Intermediate level
Some related experience required
Flexible schedule
2 months, 10 hours a week
Learn at your own pace
Build toward a degree

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 

Details to know

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Taught in English

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

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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
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Get exclusive access to career resources upon completion

  • Resume review

    Improve your resume and LinkedIn with personalized feedback

  • Interview prep

    Practice your skills with interactive tools and mock interviews

  • Career support

    Plan your career move with Coursera's job search guide

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

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Professional Certificate - 6 course series

Machine Learning with Python

Course 113 hours4.7 (16,038 ratings)

What you'll learn

  • Describe the various types of Machine Learning algorithms and when to use them 

  • Compare and contrast linear classification methods including multiclass prediction, support vector machines, and logistic regression 

  • Write Python code that implements various classification techniques including K-Nearest neighbors (KNN), decision trees, and regression trees 

  • Evaluate the results from simple linear, non-linear, and multiple regression on a data set using evaluation metrics 

Skills you'll gain

Category: Reinforcement Learning
Category: Transformers
Category: Convolutional Neural networks CNN
Category: TensorFlow Keras
Category: Generative Adversarial Networks (GANs)

Introduction to Deep Learning & Neural Networks with Keras

Course 28 hours4.7 (1,579 ratings)

What you'll learn

Skills you'll gain

Category: Artificial Intelligence (AI)
Category: Artificial Neural Network
Category: Machine Learning
Category: Deep Learning
Category: keras

Introduction to Computer Vision and Image Processing

Course 321 hours4.3 (1,211 ratings)

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

Category: Machine Learning
Category: regression
Category: Hierarchical Clustering
Category: classification
Category: SciPy and scikit-learn

Deep Neural Networks with PyTorch

Course 431 hours4.4 (1,683 ratings)

What you'll learn

  • Demonstrate your comprehension of deep learning algorithims and implement them using Pytorch.

  • Explain and apply knowledge of Deep Neural Networks and related machine learning methods.

  • Describe how to use Python libraries such as PyTorch for Deep Learning applications.

  • Build Deep Neural Networks using PyTorch.

Skills you'll gain

Category: Image Processing
Category: Artificial Intelligence (AI)
Category: Opencv
Category: Computer Vision
Category: Deep Learning

Building Deep Learning Models with TensorFlow

Course 57 hours4.4 (844 ratings)

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

AI Capstone Project with Deep Learning

Course 616 hours4.5 (561 ratings)

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.

Instructors

IBM Skills Network Team
IBM
57 Courses978,324 learners
Ricky Shi
IBM
1 Course31,644 learners
Romeo Kienzler
IBM
10 Courses688,382 learners

Offered by

IBM

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

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