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.

Machine Learning & Spark

Karakteristik Spark

Komponen dalam Kluster Spark

Menyambungkan ke Spark

Lokasi Spark master

Membuat SparkSession

Memuat Data

Memuat data penerbangan

Memuat data spam SMS

Introduction

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.

Data Preparation

Removing columns and rows

Column manipulation

Categorical columns

Assembling columns

Decision Tree

Train/test split

Build a Decision Tree

Evaluate the Decision Tree

Logistic Regression

Build a Logistic Regression model

Evaluate the Logistic Regression model

Turning Text into Tables

Punctuation, numbers and tokens

Stopwords and hashing

Training a spam classifier

Classification

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.

One-Hot Encoding

Encoding flight origin

Encoding shirt sizes

Regression

Flight duration model: Just distance

Interpreting the coefficients

Flight duration model: Adding origin airport

Interpreting coefficients

Bucketing & Engineering

Bucketing departure time

Flight duration model: Adding departure time

Regularization

Flight duration model: More features!

Flight duration model: Regularization!

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.

Pipeline

Flight duration model: Pipeline stages

Flight duration model: Pipeline model

SMS spam pipeline

Cross-Validation

Cross validating simple flight duration model

Cross validating flight duration model pipeline

Grid Search

Optimizing flights linear regression

Dissecting the best flight duration model

SMS spam optimised

How many models for grid search?

Ensemble

Delayed flights with Gradient-Boosted Trees

Delayed flights with a Random Forest

Evaluating Random Forest

Closing thoughts

Ensembles & Pipelines

Flights

Spark adalah alat serbaguna yang kuat untuk bekerja dengan Big Data. Spark secara transparan menangani distribusi tugas komputasi di seluruh kluster. Artinya, operasi menjadi cepat, dan Anda dapat berfokus pada analisis tanpa khawatir pada detail teknis. Dalam kursus ini Anda akan mempelajari cara memasukkan data ke Spark, lalu mendalami tiga algoritma Machine Learning dasar di Spark: Linear Regression, Logistic Regression/Klasifikasi, dan pembuatan pipeline. Sepanjang materi Anda akan menganalisis himpunan data besar tentang keterlambatan penerbangan dan pesan teks spam. Dengan bekal ini, Anda siap memanfaatkan kekuatan Spark dan menerapkannya pada proyek Machine Learning Anda sendiri!

Supervised Learning with scikit-learn

Introduction to PySpark

Pelajari cara memprediksi data dengan Apache Spark menggunakan pohon keputusan, regresi, dan pipeline.

Machine Learning dengan PySpark

Big Data dengan PySpark

Ilmuwan Pembelajaran Mesin dalam Python

Karakteristik Spark

Machine Learning dengan PySpark

Latihan interaktif praktis