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.

Romeo Kienzler
Joseph Santarcangelo
Alex Aklson

Instructors: Romeo Kienzler

55,082 already enrolled

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4.5

(6,171 reviews)

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

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

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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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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 (15,531 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 

Introduction to Deep Learning & Neural Networks with Keras

Course 28 hours4.7 (1,502 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 (1,127 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 430 hours4.4 (1,607 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 (811 ratings)

What you'll learn

  • Explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.

  • Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

  • Analyze different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

  • Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

AI Capstone Project with Deep Learning

Course 615 hours4.5 (535 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

Romeo Kienzler
IBM
10 Courses653,459 learners
Joseph Santarcangelo
IBM
28 Courses1,426,986 learners
Alex Aklson
IBM
22 Courses1,061,070 learners

Offered by

IBM

Get a head start on your degree

When you complete this Professional Certificate, you can earn college credit if you are admitted and enroll in one of the following online degree programs.¹

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