Preparing for AI-900: Microsoft Azure AI Fundamentals exam (Coursera)

Offered by Microsoft,
Preparing for AI-900: Microsoft Azure AI Fundamentals exam (Coursera)

Microsoft certifications give you a professional advantage by providing globally recognized and industry-endorsed evidence of mastering skills in digital and cloud businesses. In this course, you will prepare to take the AI-900 Microsoft Azure AI Fundamentals certification exam. You will refresh your knowledge of fundamental principles of machine learning on Microsoft Azure. You will go back over the main consideration of AI workloads and the features of computer vision, Natural Language Processing (NLP), and conversational AI workloads on Azure. In short, you will recap all the core concepts and skills that are measured by the exam.

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

You will test your knowledge in a series of practice exams mapped to all the main topics covered in the AI-900 exam, ensuring you’re well prepared for certification success. You will prepare to pass the certification exam by taking practice tests with similar formats and content.
You will also get a more detailed overview of the Microsoft certification program and where you can go next in your career. You’ll also get tips and tricks, testing strategies, useful resources, and information on how to sign up for the AI-900 proctored exam. By the end of this course, you will be ready to sign-up for and take the AZ-900 exam.
This course is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience is not required; however, some general programming knowledge or experience would be beneficial. To be successful in this course, you need to have basic computer literacy and proficiency in the English language. You should be familiar with basic computing concepts and terminology, general technology concepts, including concepts of machine learning and artificial intelligence.

Course 5 of 5 in the Microsoft Azure AI Fundamentals AI-900 Exam Prep Specialization

Syllabus

WEEK 1
Prepare for the AI-900: Microsoft Azure AI Fundamentals exam
In this module, you’ll also have access to resources that will help you to prepare for the proctored exam, enhance your study techniques, and help develop successful exam strategies. You will have an opportunity to explore some other Microsoft certifications paths that can help to advance your career.

WEEK 2
Exam Prep 1
In this module, you will have an opportunity to recap some of the key points from Courses 1 & 2 of the Microsoft AI-900 AI Fundamentals Specialization and take a practice exam that covers the related skills measured in the AI-900 certification exam.

WEEK 3
Exam prep 2
In this module, you will have an opportunity to recap some of the key points from Courses 3 & 4 of the Microsoft AI-900 AI Fundamentals Specialization and take a practice exam that covers the related skills measured in the AI-900 certification exam.

WEEK 4
Exam prep 3
In this module, you will take a practice exam that covers the skills measured in the Exam AI-900: Microsoft Azure AI Fundamentals.

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

Related Courses

Programming with Cloud IoT Platforms (Coursera) Coursera
Pohang University of Science and Technology - POSTECH

Programming with Cloud IoT Platforms (Coursera)

Internet of Things (IoT) is an emerging area of information and communications technology (ICT) involving many disciplines of computer science and engineering including sensors/actuators, communications networking, server platforms, data analytics and smart applications. IoT is considered to be an essential part of the 4th Industrial Revolution along with AI and Big Data.

Nov 16th 2026
5-12 Weeks
Introduction to Machine Learning (Coursera) Coursera
Duke University

Introduction to Machine Learning (Coursera)

This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction.

Nov 16th 2026
5-12 Weeks
Remote Sensing Image Acquisition, Analysis and Applications (Coursera) Coursera
UNSW Sydney - University of New South Wales

Remote Sensing Image Acquisition, Analysis and Applications (Coursera)

Welcome to Remote Sensing Image Acquisition, Analysis and Applications, in which we explore the nature of imaging the earth's surface from space or from airborne vehicles. This course covers the fundamental nature of remote sensing and the platforms and sensor types used. It also provides an in-depth treatment of the computational algorithms employed in image understanding, ranging from the earliest historically important techniques to more recent approaches based on deep learning.

Nov 30th 2026
13-24 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.

Nov 23rd 2026
5-12 Weeks
Transformer Models and BERT Model (Coursera) Coursera
Google Cloud

Transformer Models and BERT Model (Coursera)

This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.

Nov 16th 2026
1 Week
Cloud: Platform as a Service - Bachelor's (Coursera) Coursera
Illinois Tech

Cloud: Platform as a Service - Bachelor'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.

Nov 23rd 2026
5-12 Weeks
Preparing for the Google Cloud Professional Data Engineer Exam em Português Brasileiro (Coursera) Coursera
Google Cloud

Preparing for the Google Cloud Professional Data Engineer Exam em Português Brasileiro (Coursera)

Por que fazer o curso: "A melhor forma de se preparar para o exame é ser competente nas habilidades necessárias ao trabalho." Este curso usa uma abordagem "top-down". Ele identifica as habilidades que você já tem e apresenta novas informações e áreas para ampliar seus conhecimentos. Use este curso para criar seu plano de preparação personalizado. Ele ajudará você a identificar o que sabe e o que precisa estudar mais, além de desenvolver e praticar as habilidades necessárias às competências do cargo.

Nov 23rd 2026
1 Week
Machine Learning and Human Learning (Coursera) Coursera
University of Illinois at Urbana-Champaign

Machine Learning and Human Learning (Coursera)

This course examines the differences between machine and human learning and the ways in which machines can complement human learning. It examines technical definitions of supervised and unsupervised machine learning, as well as broader views of mechanical intelligence able to replicate or exceed human intelligence.

Nov 23rd 2026
4 Weeks
Machine Learning: Concepts and Applications (Coursera) Coursera
University of Chicago

Machine Learning: Concepts and Applications (Coursera)

This course gives you a comprehensive introduction to both the theory and practice of machine learning. You will learn to use Python along with industry-standard libraries and tools, including Pandas, Scikit-learn, and Tensorflow, to ingest, explore, and prepare data for modeling and then train and evaluate models using a wide variety of techniques. Those techniques include linear regression with ordinary least squares, logistic regression, support vector machines, decision trees and ensembles, clustering, principal component analysis, hidden Markov models, and deep learning.

Nov 16th 2026
5-12 Weeks
Probabilistic Graphical Models 3: Learning (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 3: Learning (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.

Nov 23rd 2026
5-12 Weeks
Google Cloud Product Fundamentals en Español (Coursera) Coursera
Google Cloud

Google Cloud Product Fundamentals en Español (Coursera)

Este curso, que es una continuación de Business Transformation with Google Cloud, le permitirá conocer la perspectiva tecnológica de la transformación de una organización. Para ser más específicos, explicaremos cómo la tecnología de Google Cloud puede transformar digitalmente una organización en los siguientes aspectos: modernizar la infraestructura de TI; mejorar la forma en que los equipos desarrollan las aplicaciones que utiliza la empresa; saber cómo aprovechar el aprendizaje automático y la inteligencia artificial para generar más valor; advertir el rol fundamental de las herramientas de productividad basadas en la nube, como G Suite, para cumplir con el trabajo, y comprender los desafíos y las oportunidades de la administración de costos que trae aparejados una infraestructura de TI cambiante basada en la nube.

Nov 23rd 2026
5-12 Weeks