Machine Learning with PySpark: With Natural Language Processing and Recommender Systems
商品資訊
ISBN13：9781484277768
出版社：Apress
作者：Pramod Singh
出版日：2021/12/23
裝訂：平裝
規格：25.4cm*17.8cm*1.3cm (高/寬/厚)
商品簡介
Chapter 1: Introduction to Spark 3.1
Chapter Goal: The book's opening chapter introduces the readers to latest changes in PySpark and updates to the framework. This chapter covers the different components of Spark ecosystem. The chapter doubles up as an introduction to the book's format, including explanation of formatting practices, pointers to the book's accompanying codebase online, and support contact information. The chapter sets readers' expectations in terms of the content and structure of the rest of the book. This chapter provides the audience with a set of required libraries and code/data download information so that the user is able to set up their environment appropriately.
No of pages -30
Sub -Topics
1. Data status
2. Apache Spark evolution
3. Apache Spark fundamentals
4. Spark components
5. Setting up Spark 3.1
Chapter 2: Manage Data with PySpark
Chapter Goal:
This chapter covers the steps right from reading the data, pre-processing and cleaning for machine learning purpose. The chapter showcases the steps to build end to end data handling pipelines to transform and create features for machine learning. It covers simple way to use Koalas in order to leverage pandas in a distributed way in Spark.It also covers the method to automate the data scripts in order to run schedules data jobs using Airflow.
No of pages:50
Sub - Topics
1. Data ingestion
2. Data cleaning
3. Data transformation
4. End- to end data pipelines
5. Data processing using koalas in Spark on Pandas DataFrame
6. Automate data workflow using Airflow
Chapter 3: Introduction to Machine Learning
Chapter Goal:
This chapter introduces the readers to basic fundamentals of machine learning. This chapter covers different categories of machine learning and different stages in the machine learning lifecycle. It highlights the method to extract information related to model interpretation to understand the reasoning behind model predictions in PySpark .
No of pages: 25
Sub - Topics:
1. Supervised machine learning
2. Unsupervised machine learning
3. Model interpretation
4. Machine learning lifecycle
Chapter 4: Linear Regression with PySpark
Chapter Goal:
This chapter covers the fundamentals of linear regression for readers. This chapter then showcases the steps to build feature engineering pipeline and fitting a regression model using PySpark latest machine learning library
No of pages:20
Sub - Topics:
1. Introduction to linear regression
2. Feature engineering in PySpark
3. Model training
4. End-to end pipeline for model prediction
Chapter 5: Logistic Regression with PySpark
Chapter Goal:
This chapter covers the fundamentals of logistic regression for readers. This chapter then showcases the steps to build feature engineering pipeline and fitting a logistic regression model using PySpark machine learning library on a customer dataset
No of pages:25
1. Introduction to logistic regression
2. Feature engineering in PySpark
3. Model training
4. End-to end pipeline for model prediction
Chapter 6: Ensembling with Pyspark
Chapter Goal:
This chapter covers the fundamentals of ensembling methods including bagging, boosting and stacking. This chapter then showcases strengths of ensembling methods over other machine learning techniques. In the final part -the steps to build feature engineering pipeline and fitting random forest model using PySpark Machine learning library are covered
No of pages:30
1. Introduction to ensembling methods
2. Feature engineering in PySpark
今日66折
您曾經瀏覽過的商品
購物須知
外文書商品之書封，為出版社提供之樣本。實際出貨商品，以出版社所提供之現有版本為主。部份書籍，因出版社供應狀況特殊，匯率將依實際狀況做調整。
無庫存之商品，在您完成訂單程序之後，將以空運的方式為你下單調貨。為了縮短等待的時間，建議您將外文書與其他商品分開下單，以獲得最快的取貨速度，平均調貨時間為1~2個月。
為了保護您的權益，「三民網路書店」提供會員七日商品鑑賞期(收到商品為起始日)。
若要辦理退貨，請在商品鑑賞期內寄回，且商品必須是全新狀態與完整包裝(商品、附件、發票、隨貨贈品等)否則恕不接受退貨。