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Prepare for a Career in AI & ML Engineering. Build, deploy, and innovate with advanced techniques and real-world projects. Intermediate programming knowledge of Python required.
Instructor: Microsoft
5,276 already enrolled
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(17 reviews)
Recommended experience
Intermediate level
Intermediate programming knowledge of Python is required. Familiarity with statistics is also recommended.
(17 reviews)
Recommended experience
Intermediate level
Intermediate programming knowledge of Python is required. Familiarity with statistics is also recommended.
Design and Implement AI & ML Infrastructure: Develop environments, including data pipelines, model development frameworks, and deployment platforms.
Master AI & ML Algorithms and Techniques: Apply supervised, unsupervised, reinforcement learning, and deep learning methods to solve challenges.
Develop Intelligent Troubleshooting Agents: Create AI-powered agents capable of diagnosing and resolving issues autonomously.
Leverage Microsoft Azure for AI & ML Workflows: Set up, manage, and optimize the entire AI & ML lifecycle using Azure.
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Learners who pass this course will receive a voucher for 50% off the AI-102 certificate exam.
This comprehensive program is designed to prepare you for the dynamic field of artificial intelligence and machine learning. Across five courses, you gain a deep understanding of AI & ML fundamentals, practical skills, and hands-on experience.
Starting with the design of scalable AI & ML infrastructure, you learn to build robust environments. You then explore core algorithms and techniques. The program also delves into AI agent development, teaching you how to create intelligent systems capable of autonomous troubleshooting using natural language processing (NLP) and decision-making strategies.
A key focus is on leveraging cloud-based AI & ML services, specifically through Microsoft Azure, where you manage end-to-end workflows. The program concludes with advanced concepts, ethical considerations, and a capstone project.
Upon completion, you will have the expertise to design, deploy, and optimize AI & ML solutions, making you a valuable asset in the tech industry. This program is ideal for those seeking to master AI & ML techniques, build scalable solutions, and apply your knowledge to real-world problems.
To be successful, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended. You will need a license to Microsoft Azure (or a free trial version) and appropriate hardware.
Applied Learning Project
The capstone project is the culmination of all the skills you have acquired throughout the program. It simulates real-world challenges faced by AI & ML professionals, giving you practical experience with the entire machine learning lifecycle—from problem identification to deployment and evaluation. This hands-on project allows you to integrate theoretical knowledge into a practical, comprehensive solution.
You might develop a fraud detection system for finance, an intelligent chatbot for customer support, or a predictive maintenance model for manufacturing. By focusing on real-world challenges, you gain insight into the complexities of AI & ML deployment, such as adapting models to new data or ensuring solutions can scale effectively.
This project allows you to experience firsthand the process of turning an idea into a fully functional AI & ML solution, preparing you for advanced roles in AI & ML engineering.
This course provides a comprehensive introduction to fundamental components of artificial intelligence and machine learning (AI & ML) infrastructure. You will explore the critical elements of AI & ML environments, including data pipelines, model development frameworks, and deployment platforms. The course emphasizes the importance of robust and scalable design in AI & ML infrastructure.
By the end of this course, you will be able to: 1. Analyze, describe, and critically discuss the critical components of AI & ML infrastructure and their interrelationships. 2. Analyze, describe, and critically discuss efficient data pipelines for AI & ML workflows. 3. Analyze and evaluate model development frameworks for various AI & ML applications. 4. Prepare AI & ML models for deployment in production environments. To be successful in this course, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended.
This course covers the core algorithms and techniques used in AI and ML, including approaches that use pre-trained large-language models (LLMs). You will explore supervised, unsupervised, and reinforcement learning paradigms, as well as deep learning approaches, including how these operate in pre-trained LLMs. The course emphasizes the practical application of these techniques and their strengths and limitations in solving different types of business problems.
By the end of this course, you will be able to: 1. Implement, evaluate, and explain supervised, unsupervised, and reinforcement learning algorithms. 2. Apply feature selection and engineering techniques to improve model performance. 3. Describe deep learning models for complex AI tasks. 4. Assess the suitability of various AI & ML techniques for specific business problems. To be successful in this course, you should have intermediate programming knowledge of Python, plus basic knowledge of AI and ML capabilities, and newer capabilities through generative AI (GenAI) and pretrained large language models (LLM). Familiarity with statistics is also recommended.
This course focuses on the design and implementation of intelligent troubleshooting agents. You will learn to create AI-powered agents that can diagnose and resolve issues autonomously. The course covers natural language processing, decision-making algorithms, and best practices in AI agent development.
By the end of this course, you will be able to: 1. Define, describe, and design the architecture of an intelligent troubleshooting agent. 2. Implement natural language processing techniques for user interaction. 3. Develop decision-making algorithms for problem diagnosis and resolution. 4. Optimize and evaluate the performance of AI-based troubleshooting agents. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure and core algorithms and techniques, including approaches using pretrained large-language models (LLMs). Familiarity with statistics is also recommended.
This course provides hands-on experience with Microsoft Azure's AI and ML services. You will learn to set up, manage, and troubleshoot Azure-based AI & ML workflows. The course covers the entire ML lifecycle in Azure, from data preparation to model deployment and monitoring.
By the end of this course, you will be able to: 1. Configure and manage Azure resources for AI & ML projects. 2. Implement end-to-end ML pipelines using Azure services. 3. Deploy and monitor ML models in Azure production environments. 4. Troubleshoot common issues in Azure AI & ML workflows. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, and the design and implementation of intelligent troubleshooting agents. Familiarity with statistics is also recommended.
This course explores advanced AI & ML techniques, ending with a comprehensive capstone project. You will learn about cutting-edge ML methods, ethical considerations in GenAI, and strategies for building scalable AI systems. The capstone project allows students to apply all their learned skills to solve a real-world problem.
By the end of this course, you will be able to: 1. Implement advanced ML techniques such as ensemble methods and transfer learning. 2. Analyze ethical implications and develop strategies for responsible AI. 3. Design scalable AI & ML systems for high-performance scenarios. 4. Develop and present a comprehensive AI & ML solution addressing a real-world problem. To be successful in this course, you should have intermediate programming knowledge of Python, plus experience with AI & ML infrastructure, core AI & ML algorithms and techniques, the design and implementation of intelligent troubleshooting agents, and Microsoft Azure’s AI & ML services. Familiarity with statistics is also recommended.
Our goal at Microsoft is to empower every individual and organization on the planet to achieve more. In this next revolution of digital transformation, growth is being driven by technology. Our integrated cloud approach creates an unmatched platform for digital transformation. We address the real-world needs of customers by seamlessly integrating Microsoft 365, Dynamics 365, LinkedIn, GitHub, Microsoft Power Platform, and Azure to unlock business value for every organization—from large enterprises to family-run businesses. The backbone and foundation of this is Azure.
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Interested in working on complex, challenging, and intellectually stimulating projects.
Focused on remaining up to date with the latest evolutions and technical advancements in the artificial intelligence/machine learning industry or concerning the Python language.
Seeking to work in a dynamic, inclusive, collaborative, and intellectually stimulating environment.
Monitoring
Reporting
Ticketing
Troubleshooting/Debugging
Quality Testing
Escalation
Governance
Policy and Protocol
Version Control
Cloud Architecture
Continuous Integration/Delivery (CI/CD)
DevSecOps Practices
Agile Practices and Tools
Intermediate programming knowledge of Python.
Familiarity with statistics is also recommended.
You will need a license to Microsoft Azure (or a free trial version) and appropriate hardware. Note: the free trial version of Azure is time limited and may expire before completion of the program.
A career in AI & ML engineering offers the opportunity to shape the future of technology by solving complex problems and driving innovation across industries. With applications in healthcare, finance, e-commerce, and more, AI & ML engineers are in high demand. This field combines creativity, critical thinking, and technical expertise, allowing you to work on impactful projects, develop cutting-edge solutions, and enjoy competitive salaries, making it an exciting and rewarding career choice.
An AI & ML engineer designs, develops, and deploys machine learning models and artificial intelligence solutions to solve complex problems. They build data pipelines, train algorithms, and optimize model performance. Their work often involves using programming, statistical analysis, and cloud platforms to create intelligent systems that improve decision-making and automate tasks.
Whether you’re looking to start a new career or change your current one, Professional Certificates help you become job ready. Apply your new skills on hands-on projects that showcase your expertise to potential employers and earn a career credential to kickstart your new career.
Machine Learning Engineer
AI Solutions Architect
NLP Engineer
AI & ML DevOps Engineer
It’s recommended to take each course in the order listed, as each one builds on the skills learned in the previous course. If you skip ahead without prior knowledge, you may find it difficult to complete later courses since they assume you’ve mastered the skills from earlier ones.
Each course will take a learner approximately 35-40 hours to complete.
This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.
If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.
Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.
¹Based on Coursera learner outcome survey responses, United States, 2021.
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