AWS Certified Machine Learning: Feature Engineering Techniques
Amazon Web Services
| Intermediate
- 13 videos | 28m 15s
- Includes Assessment
- Earns a Badge
Raw data is typically not perfect for developing effective machine learning (ML) models. Often, it needs to be processed using various feature engineering techniques to make it more suitable for building accurate and optimized ML models. Take this course to learn about techniques that help prepare the data to be compatible and improve the performance of machine learning models. Investigate techniques that are used to improve data usability, such as one-hot encoding, binning, transformations, scaling, and shuffling. You will also learn about the importance and usage of text feature engineering and major workflows in the AWS environment. After completing this course, you'll be able to implement feature engineering techniques using AWS workflows, further preparing you for the AWS Certified Machine Learning - Specialty certification exam.
WHAT YOU WILL LEARN
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Discover the key concepts covered in this courseDescribe how to perform one-hot encoding and its main purposeDefine binning and discretization as the process of transforming numerical variables into categorical counterpartsOutline how data transformation can be used to make data more useful for data analysisDefine data scaling and normalization and describe why it is important to standardize independent variablesOutline data shuffling and define its role in removing biases and building more robust training modelsWork with commonly used feature engineering techniques on real data
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Recognize the basic principles behind text feature engineeringDescribe the process of term frequency-inverse document frequency (tf-idf) and its uses in text miningDescribe bag-of-words model and compare it to tf-idfDescribe the concept of n-gram and why they are used for machine learningUse spark and emr workflows to prepare data for a tf-idf problemSummarize the key concepts covered in this course
IN THIS COURSE
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53s
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1m 13sIn this video, you will learn how to describe how to perform one-hot encoding and its main purpose. FREE ACCESS
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1m 2sDuring this video, you will learn how to define binning and discretization as the process of transforming numerical variables into categorical counterparts. FREE ACCESS
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1m 29sIn this video, discover how to outline data transformation to make data more useful for data analysis. FREE ACCESS
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1m 28sDiscover how to define data scaling and normalization and describe why it is important to standardize independent variables. FREE ACCESS
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1m 12sIn this video, you will outline data shuffling and define its role in removing biases and building more robust training models. FREE ACCESS
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7m 6sAfter completing this video, you will be able to work with commonly used feature engineering techniques on real data. FREE ACCESS
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1m 29sIn this video, you will learn how to identify the basic principles behind text feature engineering. FREE ACCESS
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1m 53sUpon completion of this video, you will be able to describe the process of term frequency-inverse document frequency (TF-IDF) and its uses in text mining. FREE ACCESS
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1m 27sDuring this video, you will learn how to describe the bag-of-words model and compare it to TF-IDF. FREE ACCESS
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1m 55sFind out how to describe the concept of an n-gram and why they are used for machine learning. FREE ACCESS
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6m 21sLearn how to use Spark and EMR workflows to prepare data for a TF-IDF problem. FREE ACCESS
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46sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
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