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Learn PySpark

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to;

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Learn PySpark

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to;

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Machine Learning with PySpark

Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business;

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Applied Data Science Using PySpark

available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We;

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Applied Data Science Using PySpark

in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also;

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PySpark Cookbook

, machine learning, and analytics using PySpark Overcome challenges in developing and deploying Spark solutions using Python Explore recipes for;

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PySpark SQL Recipes

Carry out data analysis with PySpark SQL, graphframes, and graph data processing using a problem-solution approach. This book provides;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Crt020: Spark Certification Guide PySpark: By HadoopExam.com

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DataBricks(R) PySpark 2.x Certification Practice Questions: 75 Practice Questions and Answers

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Learning PySpark

understand the streaming capabilities of PySpark. Also, you will get a thorough overview of machine learning capabilities of PySpark using ML and;

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Hands-On Big Data Analytics with PySpark

Use PySpark to easily crush messy data at-scale and discover proven techniques to create testable, immutable, and easily parallelizable;

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Data Science Solutions with Python

in-memory, distributed cluster computing framework known as PySpark, machine learning framework platforms known as scikit-learn, PySpark MLlib, H2O;

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Advanced Python Programming

a cluster using Dask and PySpark Master skills to accurately implement concurrency in your code Gain practical experience of Python design;

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Beginning MLOps with MLFlow

the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process;

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Python High Performance -

algorithms on a cluster using Dask and PySpark In Detail Python is a versatile language that has found applications in many industries. The;

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The Azure Data Lakehouse Toolkit

Snowflake. You will learn to write efficient PySpark code for batch and streaming ELT jobs on Azure. And you will follow along with practical;

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Optimizing Databricks Workloads

learning life cycles in Databricks Find out how to choose the right cluster configuration for your workloads Explore file compaction and;

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Algorithms in Machine Learning Paradigms

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse;

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Algorithms in Machine Learning Paradigms

This book presents studies involving algorithms in the machine learning paradigms. It discusses a variety of learning problems with diverse;

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Scala and Spark for Big Data Analytics

jobs to stream processing and machine learning * Explore the most common as well as some complex use-cases to perform large-scale data;

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Machine Learning Foundations

This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning;

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E-learning 1.0, 2.0, and 3.0 in Higher Education

This book has a completely new take on e-learning in higher education, introducing a novel framework which distinguishes between e-learning;

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Adult Learning

This reader examines various approaches to learning and learning strategies, covering such topics as: informal learning; age differences;

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Machine Learning Theoretical Foundations and Practical Applications

in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;

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How We Learn

Having published in 11 languages and sold in more than 100,000 copies, this fully revised edition of How We Learn examines what learning;

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