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Creating & Deploying Microsoft Azure Machine Learning Studio Solutions

Machine Learning is a fast growing field which will provide you with the tools to gain deeper insights from your data. You will learn to create, evaluate, and train predictive machine learning models using the Microsoft Azure Machine Learning Studio.
Course info
Rating
(16)
Level
Advanced
Updated
Dec 4, 2018
Duration
2h 19m
Table of contents
Description
Course info
Rating
(16)
Level
Advanced
Updated
Dec 4, 2018
Duration
2h 19m
Description

With technology growing at a rapid speed, keeping up with data is crucial. In this course, Creating & Deploying Microsoft Azure Machine Learning Studio Solutions, you will learn foundational knowledge of machine learning. First, you will learn the team data science process. Next, you will discover data import, cleansing, and transformation. Finally, you will explore how to deploy and consume predictive web services. By the end of this course, you will know how to create data science experiments using a variety of machine learning algorithms in a visual user interface.

About the author
About the author

Shawn has more than twenty-five years of experience as an architect and developer. He is a presenter at technology conferences and blogs as "The Legal BI Guy."

More from the author
Section Introduction Transcripts
Section Introduction Transcripts

Course Overview
Hi everyone. My name is Shawn Hainsworth. Welcome to my course, Creating and Deploying Microsoft Azure Machine Learning Studio Solutions. I am a Microsoft Certified Solutions Associate in Azure cloud data science. I work in business intelligence and data analytics solutions, and I blog as The Legal BI Guy. Machine learning and data science is an exciting and fast-growing field, which will provide you with the tools to gain deeper insights from your data. In this course, we are going to create, evaluate, and train predictive machine learning models using the Microsoft Azure machine Learning Studio. Some of the major topics that we will cover include the team data science process, data import, cleansing, and transformation, training, evaluating, and refining machine learning models, and deploying and consuming predictive web services. By the end of this course, you'll know how to create data science experiments using a variety of machine learning algorithms in a visual user interface. Before beginning the course, you should be familiar with some basic statistical concepts. From here, you should feel comfortable diving deeper into a variety of data science and machine learning courses, including machine learning using R or Python, Jupyter Notebooks, and other tools in the Microsoft Azure Machine Learning and artificial intelligence portfolio. I hope you'll join me on this journey to learn how to perform data science in the cloud with the Creating and Deploying Microsoft Azure Machine Learning Studio Solutions course, at Pluralsight.