IBM AI Engineering Professional Certificate

Become an IBM AI machine learning engineer, using cutting-edge methods to provide data driven actionable intelligence to any organization. Acquire the tools you need to succeed as an AI or ML engineer.

With this IBM AI Professional Certification, you’ll master fundamental concepts of machine learning and deep learning including supervised and unsupervised learning. Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data.

Deploy Machine Learning

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

Build Deep Learning

You will learn to build deep learning modeule and neural networks using Keras, PyTorch and TensorFlow

Models

Implement supervised and unsupervised machine learning models using SciPy and ScikitLearn

Professional Certificate Programs enable you to become empowered and successful in every phase of your job!

Dana Baker

Dana Baker, Executive Director of Regional Campuses

"We are committed to developing current and relevant coursework to help transform our next generation of leaders."

IBM AI Engineering Professional

100% Online

Learn on your own schedule

Flexible Schedule

Set and maintain flexible deadlines

Entry Level

No previous experience required

6-Months to Complete

Suggested pace of 10 hours/week; 6 Courses

IBM AI Engineering Professional Certificate Courses

Machine Learning with Python

This course explores the basics of machine learning using an approachable and well-known programming language, Python. Two main concepts will be reviewed:

  1. the purpose of machine learning and where it applies in the real world.
  2. A general overview of machine learning topics such as supervised vs unsupervised learning, model evaluation and machine learning algorithms.

By the end of this course, you will:

  • Practice with real-life examples of machine learning and see how it affects society in ways you may not have guessed.
  • Gain new skills to add to your resume, such as regression, classification, clustering, sci-kit learn and SciPy.
  • Produce new projects to add to your portfolio, including cancer detection, predicting economic trends, predicting customer churn, recommendation engines and many more.

Scalable Machine Learning on Big Data using Apache Sparkn

This course teaches the skills to scale data science and machine learning (ML) tasks on Big Data sets using Apache Spark. You will practice running machine learning tasks hands on an Apache Spark cluster provided by IBM at no charge during the course which you can continue to use afterwards.

Apache Spark is an open-source framework that leverages cluster computing and distributed storage to process extremely large data sets in an efficient and cost-effective manner. An applied knowledge of working with Apache Spark is a great asset and potential differentiator for a machine learning engineer.

After completing this course, you will be able to:

  • Understand Apache Spark and apply it to solve machine learning problems involving both small and big data.
  • Understand how parallel code is written, capable of running on thousands of CPUs.
  • Use large scale compute clusters to apply machine learning algorithms on Petabytes of data using Apache SparkML Pipelines.
  • Eliminate out-of-memory errors generated by traditional machine learning frameworks when data doesn’t fit in a computer’s main memory.
  • Test thousands of different ML models in parallel to find the best performing one – a technique used by many successful Kagglers
  • (Optional) run SQL statements on very large data sets using Apache SparkSQL and the Apache Spark DataFrame API.

Introduction to Deep Learning and Neural Networks with Keras

This course is an introduction to the field of deep learning and explores how 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, you 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.

Deep Neural Networks with PyTorch

This course will teach you to develop deep learning models using Pytorch, starting with Pytorch’s tensors and automatic differentiation package. Each section will cover different models starting with fundamentals such as linear regression and logistic/softmax regression, followed by feedforward deep neural networks, convolutional neural networks and transfer learning.

After completing this course, you will be able to:

  • Explain and apply knowledge of Deep Neural Networks and related machine learning methods.
  • Use Python libraries such as PyTorch for deep learning applications.
  • Build deep neural networks using PyTorch.

Building Deep Learning Models with TensorFlow

Most of the world’s data is unlabeled and unstructured. Shallow neural networks cannot easily capture relevant structure in images, sound and textual data. Deep networks can discover hidden structures within this type of data. In this course you will use the TensorFlow library to apply deep learning to different data types to solve real world problems

After completing this course, you will be able to:

  • 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.
  • understand 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

In this capstone, you will apply deep learning knowledge and expertise to a real-world challenge by using a library of choice to develop and test a deep learning model. You will load and pre-process data for a real problem, build the model, validate it and then present a project report to demonstrate the validity of you model and proficiency in the field of deep learning.

After completing this course, you will be able to:

  • Determine the kind of deep learning method to use appropriate to the situation.
  • Build a deep learning model to solve a real problem.
  • Master 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 will gain: