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Titlebook: Machine Learning with PySpark; With Natural Languag Pramod Singh Book 20191st edition Pramod Singh 2019 Machine Learning.PySpark.Python.Sup

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发表于 2025-3-21 16:24:56 | 显示全部楼层 |阅读模式
书目名称Machine Learning with PySpark
副标题With Natural Languag
编辑Pramod Singh
视频video
概述Covers all PySpark machine learning models including PySpark advanced methods.Contains practical applications of machine learning algorithms.Presents advanced features of engineering techniques for ma
图书封面Titlebook: Machine Learning with PySpark; With Natural Languag Pramod Singh Book 20191st edition Pramod Singh 2019 Machine Learning.PySpark.Python.Sup
描述Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along with natural language processing and recommender systems using PySpark. .Machine Learning with PySpark. shows you how to build supervised machine learning models such as linear regression, logistic regression, decision trees, and random forest. You’ll also see unsupervised machine learning models such as K-means and hierarchical clustering. A major portion of the book focuses on feature engineering to create useful features with PySpark to train the machine learning models. The natural language processing section covers text processing, text mining, and embedding for classification. .After reading thisbook, you will understand how to use PySpark’s machine learning library to build and train various machine learning models. Additionally you’ll become comfortable with related PySpark components, such as data ingestion, data processing, and data analysis, that you can use to develop data-dri
出版日期Book 20191st edition
关键词Machine Learning; PySpark; Python; Supervised Learning; Unsurpervised Learning; Reinforcement Learning; Re
版次1
doihttps://doi.org/10.1007/978-1-4842-4131-8
isbn_ebook978-1-4842-4131-8
copyrightPramod Singh 2019
The information of publication is updating

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发表于 2025-3-21 20:38:38 | 显示全部楼层
Linear Regression,PySpark and dives deep into the workings of an LR model. It will cover various assumptions to be considered before using LR along with different evaluation metrics. But before even jumping into trying to understand Linear Regression, we must understand the types of variables.
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Random Forests,is also used for Classification/Regression. but in terms of accuracy, random forests beat DT classifiers due to various reasons that we will cover later in the chapter. Let’s learn more about decision trees.
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Recommender Systems,ation is that users have too many options and choices available, yet they don’t like to invest a lot of time going through the entire catalogue of items. Hence, the role of Recommender Systems (RS) becomes critical for recommending relevant items and driving customer conversion.
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Introduction to Machine Learning,earn to recognize a house. We can easily differentiate between a car and a bike just by seeing a few cars and bikes around. We can easily differentiate between a cat and a dog. Even though it seems very easy and intuitive to us as human beings, for machines it can be a herculean task.
发表于 2025-3-22 20:38:13 | 显示全部楼层
Natural Language Processing,slation, recommender systems, spam detection, and sentiment analysis. This chapter demonstrates a series of steps in order to process text data and apply a Machine Learning Algorithm on it. It also showcases the sequence embeddings that can be used as an alternative to traditional input features for classification.
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