# PySpark로 하는 Machine Learning
This is a DataCamp course: Apache Spark로 데이터에서 예측을 수행합니다. 의사결정나무, 로지스틱 회귀, 선형 회귀, 앙상블, 파이프라인을 다룹니다.
## Course Details
- **Duration:** ~4h
- **Level:** Advanced
- **Instructor:** Andrew Collier
- **Students:** ~19,440,000 learners
- **Subjects:** Spark, Machine Learning, Python, Data Engineering
- **Content brand:** DataCamp
- **Practice:** Hands-on practice included
- **Prerequisites:** Supervised Learning with scikit-learn, Introduction to PySpark
## Learning Outcomes
- Spark
- Machine Learning
- Python
- Data Engineering
- PySpark로 하는 Machine Learning
## Traditional Course Outline
1. Introduction - Spark is a framework for working with Big Data. In this chapter you'll cover some background about Spark and Machine Learning. You'll then find out how to connect to Spark using Python and load CSV data.
2. Classification - Now that you are familiar with getting data into Spark, you'll move onto building two types of classification model: Decision Trees and Logistic Regression. You'll also find out about a few approaches to data preparation.
3. Regression - Next you'll learn to create Linear Regression models. You'll also find out how to augment your data by engineering new predictors as well as a robust approach to selecting only the most relevant predictors.
4. Ensembles & Pipelines - Finally you'll learn how to make your models more efficient. You'll find out how to use pipelines to make your code clearer and easier to maintain. Then you'll use cross-validation to better test your models and select good model parameters. Finally you'll dabble in two types of ensemble model.
## Resources and Related Learning
**Resources:** Flights (dataset), SMS (dataset)
**Related tracks:** PySpark를 활용한 빅데이터 분석, 머신러닝 과학자 파이썬에서
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Supervised Learning with scikit-learnIntroduction to PySpark1
Introduction
Spark is a framework for working with Big Data. In this chapter you'll cover some background about Spark and Machine Learning. You'll then find out how to connect to Spark using Python and load CSV data.
2
Classification
Now that you are familiar with getting data into Spark, you'll move onto building two types of classification model: Decision Trees and Logistic Regression. You'll also find out about a few approaches to data preparation.
3
Regression
Next you'll learn to create Linear Regression models. You'll also find out how to augment your data by engineering new predictors as well as a robust approach to selecting only the most relevant predictors.
4
Ensembles & Pipelines
Finally you'll learn how to make your models more efficient. You'll find out how to use pipelines to make your code clearer and easier to maintain. Then you'll use cross-validation to better test your models and select good model parameters. Finally you'll dabble in two types of ensemble model.
PySpark로 하는 Machine Learning
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