Microsoft AI & ML Engineering

Microsoft via Coursera Professional Certificate

Overview

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.

Syllabus

Course 1: Foundations of AI and Machine Learning
- Offered by Microsoft. This course provides a comprehensive introduction to fundamental components of artificial intelligence and machine ... Enroll for free.

Course 2: AI and Machine Learning Algorithms and Techniques
- Offered by Microsoft. This course covers the core algorithms and techniques used in AI and ML, including approaches that use pre-trained ... Enroll for free.

Course 3: Building Intelligent Troubleshooting Agents
- Offered by Microsoft. This course focuses on the design and implementation of intelligent troubleshooting agents. You will learn to create ... Enroll for free.

Course 4: Microsoft Azure for AI and Machine Learning
- Offered by Microsoft. This course provides hands-on experience with Microsoft Azure's AI and ML services. You will learn to set up, manage, ... Enroll for free.

Course 5: Advanced AI and Machine Learning Techniques and Capstone
- Offered by Microsoft. This course explores advanced AI & ML techniques, ending with a comprehensive capstone project. You will learn about ... Enroll for free.

Courses

0 reviews
1 day 21 hours 27 minutes
View details

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.
0 reviews

View details

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.
0 reviews
1 day 21 hours 16 minutes
View details

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.
0 reviews
1 day 11 hours 22 minutes
View details

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.
0 reviews
9 hours 52 minutes
View details

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.