This course will provide an introduction to the power, flexibility and scalability of Azure Machine Learning. You will learn to implement the data science process, to prepare data and integrate data sources for use in machine learning experiments.
Machine Learning and Data Science is an exciting, fast growing field which will provide you with the tools to gain deeper insights from your data. In this course, Creating & Deploying Microsoft Azure Machine Learning Studio Solutions, you'll be Creating & Deploying Microsoft Azure Machine Learning Studio Solutions. First, you’ll explore data import, cleansing, and transformation. Next, you’ll discover training, evaluating and refining Machine Learning Models. Finally, you’ll learn how to deploy and consume Predictive Web Services. When you’re finished with this course, you’ll know how to create data science experiments using a variety of machine learning algorithms using both a visual user interface and code first using Jupyter notebooks and Visual Studio Code.
Course Overview [Autogenerated] Hi, everyone. My name is Sean Haynesworth. Welcome to my course. Creating and deploying Microsoft has your machine Learning studio solutions. I am a Microsoft Certified Solutions expert in data management and analytics. I work in Business Intelligence and Data Analytics Solutions, and I blog is the legal B I 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're 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, automated machine learning, 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, using both a visual user interface and code. First using Jupyter notebooks and visual studio code before beginning the course, you should be familiar with some basic statistical concepts. It will also be useful to have a working knowledge of python from here. You should feel comfortable diving deeper into a variety of data science and machine learning courses, including scalable machine learning using the Microsoft Machine Learning Server and Apache Spark Deep Learning using tensorflow and Pytorch, as well as the azure cognitive services suite of artificial intelligence tools. 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 has your machine Learning Studio Solutions course at Pluralsight.