AI Training Program for Engineers

We provide the materials and resources that your engineers need to be retrained in AI. Our blended online and onsite program was designed for engineers who have the necessary math background to learn AI, as well as for those who don’t. Instead of spending hundreds of thousands of dollars trying to recruit fresh graduates from top AI programs, retrain your own in-house talent instead.

Interested in a customized learning experience?

How We’ve Helped Engineers

“I’d recommend the Fusemachines educational program to anyone who has an interest in AI and wants to pursue it as a career option.”

Onel Harrison

Software Engineer, Listenfirst

Our engineers have worked with companies such as:

  • Apple
  • IBM
  • Metlife
  • Buildcenter

Learning Path

Engineers first take an Eligibility Test. The results dictate whether the professional should first take either the Foundations in AI or MicrodegreeTM in AI program. Upon completion of the Foundations program, professionals are eligible for the MicrodegreeTM course. Those who complete the MicrodegreeTM are eligible to apply for either or both of our specialized courses.
Learning Path

Key Features

Our proprietary coding platform allows engineers to work and submit their assignments online. Codehub also provides access to dedicated GPU servers.
Engineers Progress Tracking
Our platform includes a built-in dashboard. Managers and Executives can track the progress of each engineer in the program and within each module.
Blended Onsite and Online Program
Typical online-only programs have an average completion rate of 5%.
Our blended program has an average completion rate of 85%!

Foundations in AI

The Foundation in AI program enables engineers to launch their AI education for today’s industry. The course provides a strong foundation in the mathematical concepts of linear algebra, calculus, statistics, and Python. These are the core prerequisites to mastering Machine Learning.


Introduction to Computer Science for AI
Become acquainted with the basics of computer science required for AI.
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  1. Introduction to the Course
  2. Basics of Computer Systems
  3. Introduction to Python Programming
  4. Data Structures and Algorithms Analysis
  5. Database
  6. Building Applications
Introduction to Mathematics for AI
Relate basic concepts of mathematics such as linear algebra, probability, and calculus to Machine Learning.
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  1. Introduction to the Course
  2. Linear Algebra
  3. Calculus and Optimisation
  4. Probability and Statistics
  5. Information Theory
  6. Numerical Computation

MicrodegreeTM in AI

The MicrodegreeTM program was designed by a team of leading US University faculty and AI industry experts. The core goal of the Machine Learning and Deep Learning courses is to upskill an engineer in AI. We also offer additional specialized courses for engineers who want to lead their teams in the areas of Computer Vision and/or Natural Language Processing.


Machine Learning
Become acquainted with the basics of computer science required for AI.
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  1. Introduction to the Course
  2. Sklearn Building Blocks
  3. Supervised Machine Learning
  4. Unsupervised Machine Learning
  5. Time Series Analysis
  6. Computer Vision with OpenCV
  7. Reinforcement Learning
Deep Learning
Learn to implement concepts such as multilayer perceptrons, backpropagation, optimization methods, and neural networks into a production-grade system.
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  1. Introduction to the Course
  2. Introduction to Deep Learning
  3. Components of Deep Learning
  4. Convolutional Neural Network
  5. Recurrent Neural Networks
  6. Attention and Neural Computers
  7. Generative Modules
  8. Deep Reinforcement Learning
  9. DL in Production
  10. Deep Learning Applications
Microdegree Specialization

MicrodegreeTM Specialization

These specialized courses are ideal for engineers who want to lead their teams in Computer Vision and Natural Language Processing. Engineers can choose to take one or both of the courses, upon completion a certificate will be awarded.


Computer Vision
Learn Computer Vision concepts such as image processing, feature detection, image classification, and object recognition.
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  1. Introduction to Computer Vision
  2. Image Processing and Feature Detection
  3. Image Classification and Object Recognition
  4. Segmentation
  5. 3D Vision
  6. Motion and Video
  7. Generating Synthetic Images
  8. Scene Understanding and Image Retrieval
  9. Computer Vision Applications
Natural Language Processing
Learn concepts such as text normalization, classification, and clustering; language modeling, parsing, and information extraction; and machine translation.
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  1. Introduction to the Course
  2. Fundamentals of NLP
  3. Language Models
  4. Markov Models
  5. Syntax and Parsing
  6. Semantic Role Labelling
  7. Semantics
  8. Information Extraction
  9. Machine Translation
  10. Neural Network based NLP
  11. Reinforcement Learning for NLP
  12. Applications

Interested in upskilling your engineers?