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Supervised Learning with scikit-learnIntroduction to PySpark1
Exploratory Data Analysis
Get to know a bit about your problem before you dive in! Then learn how to statistically and visually inspect your dataset!
2
Wrangling with Spark Functions
Real data is rarely clean and ready for analysis. In this chapter learn to remove unneeded information, handle missing values and add additional data to your analysis.
3
Feature Engineering
In this chapter learn how to create new features for your machine learning model to learn from. We'll look at generating them by combining fields, extracting values from messy columns or encoding them for better results.
4
Building a Model
In this chapter we'll learn how to choose which type of model we want. Then we will learn how to apply our data to the model and evaluate it. Lastly, we'll learn how to interpret the results and save the model for later!
PySpark로 하는 Feature Engineering
강의 완료
DataCamp for Mobile을 통해 데이터 분석 능력을 향상시키세요.
모바일 강좌와 매일 5분 코딩 챌린지를 통해 이동 중에도 학습 효과를 높이세요.