Coursera

Computer Vision in Microsoft Azure (Coursera)

Offered by Microsoft,
Computer Vision in Microsoft Azure (Coursera)

In Microsoft Azure, the Computer Vision cognitive service uses pre-trained models to analyze images, enabling software developers to easily build applications"see" the world and make sense of it. This ability to process images is the key to creating software that can emulate human visual perception. In this course, you'll explore some of these capabilities as you learn how to use the Computer Vision service to analyze images.

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

This course will help you prepare for Exam AI-900: Microsoft Azure AI Fundamentals. This is the third course in a five-course program that prepares you to take the AI-900 certification exam. This course teaches you the core concepts and skills that are assessed in the AI fundamentals exam domains. This beginner course is suitable for IT personnel who are just beginning to work with Microsoft Azure and want to learn about Microsoft Azure offerings and get hands-on experience with the product. Microsoft Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Microsoft Azure Data Scientist Associate or Microsoft Azure AI Engineer Associate, but it is not a prerequisite for any of them.
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 3 of 5 in the Microsoft Azure AI-900 AI Fundamentals Exam Prep Specialization

Syllabus

WEEK 1
Analyze and Classify Images with the Computer Vision Service
The Computer Vision cognitive service uses pre-trained models to analyze images, enabling software developers to easily build applications. in this module, you'll explore some of its capabilities as you learn how to use the Computer Vision service to analyze images.

WEEK 2
Detect objects in images with the Custom Vision service
Image classification is a common workload in artificial intelligence (AI) applications. In this module, you will learn how to harnesses the predictive power of machine learning to enable AI systems to identify real-world items based on images.

WEEK 3
Analyze Faces, Text, and Receipts with Azure AI
Face detection, analysis, and recognition is an important capability for artificial intelligence (AI) solutions. The Face cognitive service in Azure makes it easy integrate these capabilities into your applications. Optical character recognition (OCR) enables artificial intelligence (AI) systems to read text in images, enabling applications to extract information from photographs, scanned documents, and other sources of digitized text. Processing invoices and receipts is a common task in many business scenarios. Increasingly, organizations are turning to artificial intelligence (AI) to automate data extraction from scanned receipts. In this module, you will learn how you can use a mix of these services in Microsoft Azure to solve problems.

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

Related Courses

Exam Prep AI-102: Microsoft Azure AI Engineer Associate (Coursera) Coursera
Whizlabs

Exam Prep AI-102: Microsoft Azure AI Engineer Associate (Coursera)

CS: Information & Technology

The AI-102: Designing and Implementing a Microsoft Azure AI Solution certification exam tests the candidate’s experience and knowledge of the AI solutions that make the most of Azure Cognitive Services and Azure services. In addition, the exam also tests the candidate's ability to implement this knowledge by participating in all phases of AI solutions development—from defining requirements, and design to development, deployment, integration, maintenance, performance tuning, and monitoring.

Nov 16th 2026
5-12 Weeks
Python Artificial Intelligence Microsoft Azure
Details
Preparing for the Google Cloud Professional Data Engineer Exam (Coursera) Coursera
Google Cloud

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

CS: Information & Technology Computer Science

From the course: "The best way to prepare for the exam is to be competent in the skills required of the job." This course uses a top-down approach to recognize knowledge and skills already known, and to surface information and skill areas for additional preparation. You can use this course to help create your own custom preparation plan. It helps you distinguish what you know from what you don't know. And it helps you develop and practice skills required of practitioners who perform this job.

Nov 16th 2026
5-12 Weeks
Machine Learning Data Engineer Google Cloud
Details
Machine Learning for Accounting with Python (Coursera) Coursera
University of Illinois at Urbana-Champaign

Machine Learning for Accounting with Python (Coursera)

Data Science

This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. It also discusses model evaluation and model optimization. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems.

Nov 16th 2026
5-12 Weeks
Algorithms Machine Learning Clustering
Details
AI-Driven Attribution Testing (Coursera) Coursera
Board Infinity

AI-Driven Attribution Testing (Coursera)

Data Science

Welcome to AI-Driven Attribution Testing course an engaging and comprehensive course designed to guide you through the fundamental concepts and practical applications of attribution testing powered by artificial intelligence. This course is most suitable for marketers, data analysts, data scientists, and business leaders who aim to leverage data-driven insights for decision-making. It's also beneficial for students and professionals with a keen interest in the convergence of AI, data analysis, and marketing.

Nov 16th 2026
2 Weeks
ML Artificial Intelligence Machine Learning
Details
Deep Learning for Business (Coursera) Coursera
Yonsei University

Deep Learning for Business (Coursera)

Management & Leadership

Your smartphone, smartwatch, and automobile (if it is a newer model) have AI (Artificial Intelligence) inside serving you every day. In the near future, more advanced “self-learning” capable DL (Deep Learning) and ML (Machine Learning) technology will be used in almost every aspect of your business and industry. So now is the right time to learn what DL and ML is and how to use it in advantage of your company. This course has three parts, where the first part focuses on DL and ML technology based future business strategy including details on new state-of-the-art products/services and open source DL software, which are the future enablers.

Nov 16th 2026
5-12 Weeks
Business ML Artificial Intelligence
Details
Machine Learning: Concepts and Applications (Coursera) Coursera
University of Chicago

Machine Learning: Concepts and Applications (Coursera)

Data Science

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
Python Machine Learning Clustering
Details
Data Science for Business Innovation (Coursera) Coursera
Politecnico di Milano,EIT Digital

Data Science for Business Innovation (Coursera)

Statistics & Data Analysis Data Science

The course is a compendium of the must-have expertise in data science for executive and middle-management to foster data-driven innovation. It consists of introductory lectures spanning big data, machine learning, data valorization and communication. Topics cover the essential concepts and intuitions on data needs, data analysis, machine learning methods, respective pros and cons, and practical applicability issues.

Nov 16th 2026
4 Weeks
NoSQL Machine Learning Big Data
Details
Machine Learning for All (Coursera) Coursera
University of London

Machine Learning for All (Coursera)

Data Science Computer Science

Machine Learning, often called Artificial Intelligence or AI, is one of the most exciting areas of technology at the moment. We see daily news stories that herald new breakthroughs in facial recognition technology, self driving cars or computers that can have a conversation just like a real person. Machine Learning technology is set to revolutionise almost any area of human life and work, and so will affect all our lives, and so you are likely to want to find out more about it.

Nov 16th 2026
4 Weeks
Artificial Intelligence Machine Learning AI Technology
Details
Using R for Regression and Machine Learning in Investment (Coursera) Coursera
Sungkyunkwan University - SKKU

Using R for Regression and Machine Learning in Investment (Coursera)

Economics & Finance Business

In this course, the instructor will discuss various uses of regression in investment problems, and she will extend the discussion to logistic, Lasso, and Ridge regressions. At the same time, the instructor will introduce various concepts of machine learning. You can consider this course as the first step toward using machine learning methodologies in solving investment problems. The course will cover investment analysis topics, but at the same time, make you practice it using R programming. This course's focus is to train you to use various regression methodologies for investment management that you might need to do in your job every day and make you ready for more advanced topics in machine learning.

Nov 16th 2026
2 Weeks
Machine Learning Regression Investment
Details