Applying statistical techniques to your data within Azure Machine Learning Service will often boost model performance. This course will teach you the basics of data cleansing, including basic syntax and functions.
At the core of applied machine learning is data. In this course, Building Features from Nominal and Numeric Data in Microsoft Azure, you will learn how to cleanse data within the confines of Azure Machine Learning Service. First, you will discover the sundry options you have within Azure Machine Learning Service for building your models end to end. Next, you will explore the importance of applying statistical techniques to your data to improve model performance. Finally, you will learn how to apply various data cleansing techniques to your data for enhancing real-world performance. When you are finished with this course, you will have a foundational knowledge of Azure Machine Learning Service and a solid understating of how to apply statistical techniques to your data that will help you as you move forward to becoming a machine learning engineer.
Course Overview [Autogenerated] Hello, everyone. My name is Mike West. Welcome to my course building features from nominal in numeric data and Microsoft Azure Applied Machine Learning. His data driven in the real world data is dirty, and it's the responsibility of the machine learning engineer To ensure that the data you'll be using to build your models as well cleansed, Microsoft is a top player in the machine learning in data sign space. This course is a quick introduction to date a cleansing within the confines of Microsoft Azure Machine Learning Service. The course will introduce you to the machine under process. Within that process or various statistical and programmatic techniques, you can apply to your data. The application of these techniques to your data prior to modeling often help you get better performance from your models. With that as your machine learning service or notebook virtual machines, these nopal fans almost identical to Jupiter notebooks. The gold standard for ability ended in models in the applied space. The course will introduce you to Gaussian distributions, gassing and distributions air very well understood. So much so that large parts of the field of statistics is dedicated to methods for this distribution. You'll learn how machine learning engineers using mutation to fill gaps in their data machine learning models don't like missing values, and handling these values is often critical to the model's performance. You'll learn multiple approaches to standardize, and it's scaling your data. Whatever you're doing, with features that differ from each other in terms of range of values, you'll often normalize the data so that the difference in these ranges of values does not affect your outcome. You'll learn about the different types of data and statistics and some recommended approaches to handling categorical data. Categorical Data's data That's not in America. Data preparation is one of the most important fastest to the outcome of your machinery models. I hope you'll join me in this journey to learn more about building features from nominal numeric data and Microsoft Azure Plural site.