AI Program for Engineers

Retraining your in-house talent is a great alternative to spending hundreds of thousands of dollars trying to recruit fresh graduates from top AI programs.
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Explore our programs for your engineers
AI2Go Workshop

AI2Go Workshop

The workshop is specially designed for Non-Technical Professionals to begin a career in AI

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Foundations in Artificial Intelligence

Foundations in Artificial Intelligence

Build strong programming and mathematical foundation to take advanced AI courses

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MicrodegreeTM in Artificial Intelligence

MicrodegreeTM in Artificial Intelligence

Designed to upskill an engineer with AI expertise

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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
Onel Harrison
Software Engineer, Listenfirst
Our Alumni have worked with companies such as
  • Apple
  • IBM
  • Metlife
  • Enatch
  • Buildcenter
Learning Path
AI Learning Path Graph
Key Features
View of Codehub Coding platform
Online Live Lectures
Offer live online classes in a matter of minutes with our Zoom-integrated platform that is equipped with all the tools instructors need.
Engineer tracking dashboard
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.
Fusemachine AI Classroom
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%!

Fusemachines AI2Go Workshop

The workshop is specially designed for Non-Technical Professionals to begin a career in AI.

*No programming experience needed
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online-lectures

Live Online Lectures

certificate

Professional Certificates

You’ll walk away with
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Sound knowledge of concepts like machine learning and artificial intelligence

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A basic working knowledge of python and how to create machine learning models

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A basic understanding of the machine learning workflow and how to apply it

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Enough knowledge to create a dataset and AI model to turn your ideas into reality

Microdegree in AI
Session 1 : Intro to AI and Programming arrow-down

What is AI, Applications, benefits and importance of AI in different sectors of modern society, and why you should be getting into the field of AI now. Do we really need to learn programming to learn AI ? No! It is not necessary to know about programming to learn about AI. You can create simple AI models with no code, however- learning some programming basics can help you build a stronger foundation.

  • Introduction to AI
    • What is AI, ML & DL & its type
    • Project: Create ML model without coding
  • Introduction to Python Programming
    • Basic Python Programming Syntax
    • Python Libraries: NumPy, Pandas, Matplotlib
    • Project: Python Data Visualization
Session 2: Intro to Machine Learning arrow-down

The course includes the basics of how machines actually learn to find patterns in data, and are able to predict even better than humans. You will learn to build a Regression model to predict housing prices.

  • 7 steps of Machine Learning Workflow
  • Linear Regression
    • Scikit-Learn for Machine Learning
    • Project: House Price Prediction
  • Logistic Regression
    • Project: Iris Classification
Session 3: Intro to Natural Language Processing (NLP) arrow-down

Language is a cornerstone of human cognition. You will learn how to understand the state of NLP, and build a sentiment analysis model to recognize the sentiment of reviews and create a chatbot yourself.

  • Introduction to NLP
    • Why is Natural Language Hard ?
    • Applications of NLP
  • NLP Pipeline
    • Converting Text to Vectors
    • NLP workflow
    • Project: Sentiment Analysis
    • Project: Building a Chatbot
Session 4: Introduction to Deep Learning (DL) arrow-down

By modeling neurons in human brains into artificial neurons to develop neural networks, scientists have achieved state-of-the-art results in many domains of AI. You will learn how to build simple neural networks.

  • Deep Learning
    • Introduction & Application of Deep Learning
  • Artificial Neural Network
    • Biological and Artificial Neurons
    • Multi-layer Neural Network
    • TensorFlow for Deep Learning
    • Project: Numeric Character Recognition
Session 5: Introduction to Computer Vision (CV) arrow-down

Vision is a crucial sense but hard for computers to learn, yet they can recognize objects even better than humans, and learn to build their own image classifiers that can separate one image from the other.

  • Introduction to Computer Vision (CV)
    • Why is Computer Vision hard?
    • Application of Computer Vision
  • Convolutional Neural Networks (CNN)
    • Extracting features from images
    • Project: Image Classifier
Session 6: Impact of AI on Society arrow-down

AI is deeply integrated into our everyday lives, in almost all major aspects. You will learn how AI impacts society, and recognize the negative impacts as well. We will also discuss how we can spot AI opportunities in business, and how to implement them.

  • Impacts of AI on different sectors: Agriculture, Healthcare, Retail, Finance
  • Methods of AI implementation in business
    • Spotting AI Opportunities
    • Issues with AI Implementation
    • Implementation Strategies
  • Negative Impact of AI: Ethics, Bias, Adversaries
Session 7: Guest Lectures arrow-down

Learn from, and interact with, esteemed Guest Lecturers from different renowned universities who possess many years of experience in the domain and have absolute expertise in the field.

  • Optional TA Sessions to support participants in their project works
Session 8: Demo day arrow-down

The best learning process is acknowledging mistakes and improving upon them, constantly acquiring better understandings. On this day, participants can demonstrate their learned AI skills and knowledge by implementing their proficiency in a domain of their interest.

  • Choose a particular problem that can be solved with AI, which can have some impact in your chosen domain
  • Develop AI models to solve the problem
  • TAs will be available to support you, there will be optional TA sessions before the Demo Day
Foundation in AI

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.
  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.
  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.
  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.
  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 in AI
Students attending Microdegree Specialization course

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.

MicrodegreeTM Specialization in Computer Vision

Learn Computer Vision concepts such as image processing, feature detection, image classification, and object recognition.
  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

MicrodegreeTM Specialization in Natural Language Processing

Learn concepts such as text normalization, classification, and clustering; language modeling, parsing, and information extraction; and machine translation.
  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
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