Curso
Feature Engineering for Machine Learning in Python
IntermediárioNível de habilidade
Atualizado 02/2023Iniciar Curso Gratuitamente
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PythonMachine Learning4 h16 vídeos53 Exercícios4,350 XP37,904Certificado de conclusão
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Pré-requisitos
Supervised Learning with scikit-learn1
Creating Features
In this chapter, you will explore what feature engineering is and how to get started with applying it to real-world data. You will load, explore and visualize a survey response dataset, and in doing so you will learn about its underlying data types and why they have an influence on how you should engineer your features. Using the pandas package you will create new features from both categorical and continuous columns.
2
Dealing with Messy Data
This chapter introduces you to the reality of messy and incomplete data. You will learn how to find where your data has missing values and explore multiple approaches on how to deal with them. You will also use string manipulation techniques to deal with unwanted characters in your dataset.
3
Conforming to Statistical Assumptions
In this chapter, you will focus on analyzing the underlying distribution of your data and whether it will impact your machine learning pipeline. You will learn how to deal with skewed data and situations where outliers may be negatively impacting your analysis.
4
Dealing with Text Data
Finally, in this chapter, you will work with unstructured text data, understanding ways in which you can engineer columnar features out of a text corpus. You will compare how different approaches may impact how much context is being extracted from a text, and how to balance the need for context, without too many features being created.
Feature Engineering for Machine Learning in Python
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