Bộ sách luyện thi ETS 2023 LC&RC
199,000₫
299,000₫
Sách keo gáy, Bìa mềm
Thể loại:Computers - Artificial Intelligence (AI)
Năm:2021
In lần thứ:2nd ed.
Ngôn ngữ:english
Trang:230
Master the new features in PySpark 3.1 to develop data-driven,
intelligent applications. This updated edition covers topics ranging
from building scalable machine learning models, to natural language
processing, to recommender systems.
Machine Learning with PySpark, Second Edition
begins with the fundamentals of Apache Spark, including the latest
updates to the framework. Next, you will learn the full spectrum of
traditional machine learning algorithm implementations, along with
natural language processing and recommender systems. You’ll gain
familiarity with the critical process of selecting machine learning
algorithms, data ingestion, and data processing to solve business
problems. You’ll see a demonstration of how to build supervised machine
learning models such as linear regression, logistic regression, decision
trees, and random forests. You’ll also learn how to automate the steps
using Spark pipelines, followed by unsupervised models such as K-means
and hierarchical clustering. A section on Natural Language Processing
(NLP) covers text processing, text mining, and embeddings for
classification. This new edition also introduces Koalas in Spark and how
to automate data workflow using Airflow and PySpark’s latest ML
library.
After completing this book, you will understand how to
use PySpark’s machine learning library to build and train various
machine learning models, along with related components such as data
ingestion, processing and visualization to develop data-driven
intelligent applications
What you will learn:
Build a spectrum of supervised and unsupervised machine learning algorithms
Use PySpark's machine learning library to implement machine learning and recommender systems
Leverage the new features in PySpark’s machine learning library
Understand data processing using Koalas in Spark
Handle issues around feature engineering, class balance, bias and variance, and cross validation to build optimally fit models
Who This Book Is For
Data science and machine learning professionals.
