Intro to Machine Learning using Microsoft Azure
This free online course from Udacity provides an introduction to machine learning using Microsoft Azure. In two months, participants will gain practical, in-demand tech skills and a better understanding of the Azure platform. ▼
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Course Feature
Cost:
Free
Provider:
Udemy
Certificate:
No Information
Language:
English
Course Overview
❗The content presented here is sourced directly from Udemy platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [March 06th, 2023]
Intro to Machine Learning using Microsoft Azure is a great course for those looking to gain a high-level introduction to the field of machine learning. This course will provide learners with an overview of the fundamentals of machine learning, including the different types of algorithms, the various applications of machine learning, and the basics of cloud computing. Learners will also be able to train their first machine learning model using Azure Machine Learning Studio. This course is ideal for those who are interested in learning more about artificial intelligence, data science, and machine learning algorithms. By the end of the course, learners will have a better understanding of the fundamentals of machine learning and be able to apply their knowledge to real-world scenarios.
[Applications]
After completing this course, students should be able to apply their knowledge of machine learning to create and deploy models using Microsoft Azure. They should be able to use Azure Machine Learning Studio to create and train models, and use Azure Machine Learning Service to deploy models. Additionally, they should be able to use Azure Machine Learning Service to monitor and manage models. Finally, they should be able to use Azure Machine Learning Service to create and manage pipelines for automated machine learning.
[Career Paths]
1. Machine Learning Engineer: Machine Learning Engineers are responsible for developing and deploying machine learning models. They use a variety of tools and techniques to build, test, and deploy models, and are also responsible for monitoring and maintaining the models. As the demand for machine learning increases, the need for Machine Learning Engineers is expected to grow.
2. Data Scientist: Data Scientists use a variety of techniques to analyze data and develop insights. They use machine learning algorithms to identify patterns and trends in data, and use these insights to inform decisions. Data Scientists are in high demand, and the demand is expected to continue to grow.
3. Artificial Intelligence Engineer: Artificial Intelligence Engineers are responsible for developing and deploying AI-based solutions. They use a variety of tools and techniques to build, test, and deploy AI-based solutions, and are also responsible for monitoring and maintaining the solutions. As the demand for AI increases, the need for Artificial Intelligence Engineers is expected to grow.
4. Business Intelligence Analyst: Business Intelligence Analysts use a variety of techniques to analyze data and develop insights. They use machine learning algorithms to identify patterns and trends in data, and use these insights to inform decisions. Business Intelligence Analysts are in high demand, and the demand is expected to continue to grow.
[Education Paths]
1. Bachelor of Science in Computer Science: This degree path provides a comprehensive overview of computer science fundamentals, including programming, algorithms, data structures, and software engineering. It also covers topics such as artificial intelligence, machine learning, and data science. This degree is ideal for those interested in developing and deploying machine learning models in the cloud.
2. Master of Science in Artificial Intelligence: This degree path focuses on the development of intelligent systems, including machine learning, natural language processing, and robotics. It provides a deep dive into the theoretical and practical aspects of artificial intelligence, as well as the development of machine learning models. This degree is ideal for those interested in advancing their knowledge and skills in the field of artificial intelligence.
3. Master of Science in Data Science: This degree path focuses on the analysis and interpretation of large datasets. It covers topics such as data mining, machine learning, and predictive analytics. This degree is ideal for those interested in leveraging data to gain insights and make decisions.
4. Master of Science in Machine Learning: This degree path focuses on the development of machine learning models and algorithms. It covers topics such as supervised and unsupervised learning, deep learning, and reinforcement learning. This degree is ideal for those interested in developing and deploying machine learning models in the cloud.
Course Syllabus
Learn how to prepare data and then transform it into trained machine learning models and get an introduction to ensemble learning and automated machine learning
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