Building Autonomous AI (Coursera)

Building Autonomous AI (Coursera)

Practice makes perfect. It’s true for people learning to master a new skill, and it’s also true for your AI brain. Just as you need the right environment to practice, get feedback and try again, so does your AI brain. In this course, you’ll solve industrial engineering problems inspired by real problems your instructors have worked on in industry.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

You’ll learn how to build, test and deploy an AI brain using Microsoft Bonsai, a cloud-based, low-code platform. We’ll walk through the entire Bonsai platform from setup to deployment. Along the way, you’ll use Bonsai to conduct machine teaching experimentation to train a brain and assess its progress. Because you’ll be teaching the brain a relatively complex task, you’ll run multiple simulations until you’re satisfied with the results. You’ll then prep the brain for graduation into the real world — deploying it into a machinery control system or other live environment.
At the end of this course, you’ll be able to:
• Build an autonomous AI that combines reinforcement learning with machine learning, expert rules and other methods that you’ve used in the first two courses of the specialization
• Establish requirements for a simulated environment for your brain to practice a task
• Validate and assess your brain’s performance of a task and make improvements to your brain design
• Evaluate whether a simulator is a good practice environment
• Deploy a brain on a real piece of hardware
This course requires an Azure subscription.
This course is part of a specialization called Autonomous AI for Industry.
Course 3 of 3 in the Autonomous AI for Industry Specialization.

What You Will Learn
You’ll build an industrial strength AI brain using complex features and simulations to solve real-world problems.

Syllabus

WEEK 1
Build Your First Brain
You'll begin this course by implementing a monolithic (one concept) brain.
This week you'll setup your Bonsai workplace and train a brain. By the end of this week you'll be familiar with the Bonsai user interface, and learn how to read the assessment graphs that tell you how the brain is performing.

WEEK 2
Industrial Strength Brains!
This week you're going to train a much more complex brain, something more like the kind of brains that you train for real industrial processes. These brains require decomposition of the task into skills, training or programming multiple skills, and experimentation to get the orchestration and the training right. Juan Vergera and John Alexander will guide you through the steps. You'll practice, and then you'll take a quiz that helps you determine whether you have a good understanding of the steps that you completed so you can do it again on other projects. 

WEEK 3
Machine Teaching experimentation
Brain designs are like lesson plans that guide students in their learning. After developing a Lesson plan and teaching it to a variety of students, a teacher will track how well different types of students learned different skills and concepts under different conditions and then modify that Lesson plan for the next set of students.
Machine teachers design AI brains based on subject matter expertise that will guide your students’ learning. This is your original Lesson plan. But then based on how the brain learns, you'll modify that Lesson plan. You'll modify the brain design, so that it can learn even better next time. We call this machine teaching experimentation and that's the primary focus of this week.  Learning in autonomous AI brains takes place in layers. There's a teaching layer and a learning layer.  The teaching layer sets the structure of which skills the brain needs to learn. That's your brain design.  The learning layer is algorithms that learn by practice, neural networks, programming and math. Through this trial and error, the brain is built.

WEEK 4
Simulations: Creating the Classroom for your brain
This week we're going to discuss the ins and outs of simulators. Why do we use simulators for autonomous AI to practice? What kinds of simulators do you need to understand and be familiar with in order to train autonomous AI? At the end of this course you will actually practice connecting a simulator to the project bonsai platform for training.

WEEK 5
Graduating to the Real World
This week you'll practice mining training logs, learning what's working in your training, what's not working in your training, and you're gonna use that information to update your training plans. 
Your assessments will get more complex, too, from the automatic assessments in the platform to custom assessments that will analyze specific scenarios, even rare scenarios that are keeping your brain from succeeding well under all conditions.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Code Free Data Science (Coursera) Coursera
University of California, San Diego

Code Free Data Science (Coursera)

The Code Free Data Science class is designed for learners seeking to gain or expand their knowledge in the area of Data Science. Participants will receive the basic training in effective predictive analytic approaches accompanying the growing discipline of Data Science without any programming requirements. Machine Learning methods will be presented by utilizing the KNIME Analytics Platform to discover patterns and relationships in data.

Sep 28th 2026
4 Weeks
Information Extraction from Free Text Data in Health (Coursera) Coursera
University of Michigan

Information Extraction from Free Text Data in Health (Coursera)

In this MOOC, you will be introduced to advanced machine learning and natural language processing techniques to parse and extract information from unstructured text documents in healthcare, such as clinical notes, radiology reports, and discharge summaries. Whether you are an aspiring data scientist or an early or mid-career professional in data science or information technology in healthcare, it is critical that you keep up-to-date your skills in information extraction and analysis.

Sep 28th 2026
4 Weeks
Cloud: Platform as a Service - Master's (Coursera) Coursera
Illinois Tech

Cloud: Platform as a Service - Master's (Coursera)

This course is aimed at preparing individuals to gain knowledge, skills, and abilities to demonstrate the knowledge for managing Platform as a Service (PaaS) in the Cloud. Students will learn to deploy, operate, and maintain cloud platforms for storing, processing, and transferring information with architecture design principles and a structured approach. Students will also learn the shared responsibility model and cloud security best practices to secure PaaS platforms for the application-hosting environments.

Sep 28th 2026
5-12 Weeks
Foundations of Data Science: K-Means Clustering in Python (Coursera) Coursera
University of London,Goldsmiths, University of London

Foundations of Data Science: K-Means Clustering in Python (Coursera)

This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks.

Oct 5th 2026
5-12 Weeks
Getting Started with Machine Learning at the Edge on Arm (Coursera) Coursera
Arm

Getting Started with Machine Learning at the Edge on Arm (Coursera)

The age of machine learning has arrived! Arm technology is powering a new generation of connected devices with sophisticated sensors that can collect a vast range of environmental, spatial and audio/visual data. Typically this data is processed in the cloud using advanced machine learning tools that are enabling new applications reshaping the way we work, travel, live and play.

Sep 28th 2026
5-12 Weeks
Introduction to Vertex AI (Coursera) Coursera
Fractal Analytics

Introduction to Vertex AI (Coursera)

Welcome to "Introduction to Vertex AI"! In this concise yet impactful microlearning course spanning around 4 hours, we're diving into the world of Vertex AI to equip you with fundamental insights and practical skills. We'll unravel the essentials of Vertex AI, guiding you through the interface to empower you to navigate this powerful platform seamlessly. Get ready to grasp strategic insights that will enable you to effectively harness the capabilities of Vertex AI in your projects.

Sep 28th 2026
2 Weeks
Google Cloud Product Fundamentals em Português Brasileiro (Coursera) Coursera
Google Cloud

Google Cloud Product Fundamentals em Português Brasileiro (Coursera)

Este curso é uma continuação do "Business Transformation with Google Cloud" e guiará você pela jornada de transformação de uma organização do ponto de vista tecnológico. Explicaremos como as organizações podem fazer a transformação digital usando a tecnologia do Google Cloud nestas categorias: modernização da infraestrutura de TI; melhorias no processo de desenvolvimento dos aplicativos da empresa; uso do machine learning e da inteligência artificial para criar novo valor; a importância de ferramentas de produtividade como o G Suite na realização do trabalho; e compreender as oportunidades e os desafios da gestão do custo que uma infraestrutura de TI na nuvem traz.

Sep 28th 2026
5-12 Weeks
Convolutional Neural Networks (Coursera) Coursera
DeepLearning.AI

Convolutional Neural Networks (Coursera)

This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images.

Sep 21st 2026
4 Weeks
Computer Vision with Embedded Machine Learning (Coursera) Coursera
Edge Impulse

Computer Vision with Embedded Machine Learning (Coursera)

Computer vision (CV) is a fascinating field of study that attempts to automate the process of assigning meaning to digital images or videos. In other words, we are helping computers see and understand the world around us! A number of machine learning (ML) algorithms and techniques can be used to accomplish CV tasks, and as ML becomes faster and more efficient, we can deploy these techniques to embedded systems.

Sep 28th 2026
3 Weeks
Probabilistic Graphical Models 1: Representation (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 1: Representation (Coursera)

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more. They are the basis for the state-of-the-art methods in a wide variety of applications, such as medical diagnosis, image understanding, speech recognition, natural language processing, and many, many more. They are also a foundational tool in formulating many machine learning problems.

Sep 28th 2026
5-12 Weeks
Alibaba Cloud Native Solutions and Container Service (Coursera) Coursera
Alibaba Cloud Academy

Alibaba Cloud Native Solutions and Container Service (Coursera)

This course demonstrates how to use Alibaba Cloud Container Service and Container Registry Service to design and develop architectures related to cloud native applications, services, and security solutions. This course helps you understand the basic concepts of cloud native, the commercial implementation of container technology, and Kubernetes technology as well as extra benefits provided by Alibaba Cloud. This course is intended to prepare users to take the Alibaba Cloud Native ACA certification exam.

Oct 5th 2026
5-12 Weeks