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Applied Data Science Using PySpark

. Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics;

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Applied Data Science Using PySpark

Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark;

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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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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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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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Machine Learning with PySpark

algorithms along with natural language processing and recommender systems using PySpark. Machine Learning with PySpark shows you how to build;

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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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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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Learning PySpark

Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0 About This Book;

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Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Machine Learning and Knowledge Discovery in Databases

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Machine Learning and Knowledge Discovery in Databases

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Machine Learning and Knowledge Discovery in Databases

vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data;

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Learn R for Applied Statistics

Is For Those who are interested in data science, in particular data exploration using applied statistics, and the use of R programming for data;

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The The Applied Data Science Workshop

understand how data science can be applied to solve real-world data problems. Key Features Gain useful insights into data science and machine;

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Data Science Solutions with Python

, test, and validate skilled machine models and deep learning models Optimize model performance using data transformation, regularization;

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Statistical Methods for Materials Science: The Data Science of Microstructure Characterization

researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to;

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Harnessing the Power of Analytics

This text highlights the difference between analytics and data science, using predictive analytic techniques to analyze different;

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Beginning MLOps with MLFlow

. What You Will Learn Perform basic data analysis and construct models in scikit-learn and PySpark Train, test, and validate your;

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The Azure Data Lakehouse Toolkit

Design and implement a modern data lakehouse on the Azure Data Platform using Delta Lake, Apache Spark, Azure Databricks, Azure Synapse;

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Statistics for Data Scientists

often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books;

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Statistical Methods for Materials Science

researchers in materials science to understand how to analyze large datasets using statistical methods, especially inverse methods applied to;

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Applied Statistics for Environmental Science with R

> Shows how to analyze data using R software and provides R scripts for all examples and figures;

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Artificial Intelligence and Data Mining in Healthcare

This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific;

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