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Perform effective data processing, machine learning, and analytics using PySpark Overcome challenges in developing and deploying Spark solutions using Python Explore recipes for efficiently combining Python and Apache Spark to process data

Learning Spark SQL - Aurobindo Sarkar　a easy way to learn spark and become a big data scientist

Learning PySpark by Tomasz Drabas

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Key Features, Get the most up-to-date book on the market that focuses on design, engineering, and scalable solutions in machine learning with Spark 2We use Spark's machine learning library in a big data environmentYou will learn to develop high-value applications at scale with ease and a personalized design, Book Description, Scaling out and deploying algorithms, interactions, and clustering are crucial steps in the process of optimizing any application. By maintaining and streaming data, Spark can figure out when to cache data in-memory, 100x faster than Hadoop and Mahoot. This means data streaming and analytics can run and complete jobs a lot quicker, making Spark ideal for large data-intensive applications., This book focuses on design, engineering, and scalable solutions in machine learning with Spark. You will learn how to install Spark with all new features as in the latest version Spark 2. You will also get to grips with Spark MLlib and Spark ML and its implementation for machine learning algorithms. Moving ahead, we'll explore about important concepts such as Dataframes and advanced feature engineering. After studying more about the development and deployment of an application, you will also find out about the other external libraries available for your data analysis., What you will learn, Solid theoretical understanding about machine learning algorithms and techniques for new and unknown datasetsSet up and configure Spark, and develop your first Spark application using Scala, Java, and SparkRUse ML and MLlib implement practical and large-scale machine learning pipelines and applications including collaborative filtering, classification, regression, clustering, association rule mining, twitter sentiment analysis, and dimensionality reductionScale up your machine learning application on large cluster or even cloud computing environment like Amazon EC2Enhance performance of your machine learning modelsTune your machine learning models for cross-validation, grid searching, hyperparameter tuning and train validation splitDeal with large-scale text data, including feature extraction and using text data as input to machine learning modelsDevelop machine learning application real-time streaming data using Spark Streaming

Key Features, Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning, Want to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library—TensorFlow!, Dive into the next generation of numerical computing and get the most out of your data with this quick guide, Book Description, Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks., This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples., By the end of this book, you’ll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application., What you will learn, Install and adopt TensorFlow in your Python environment to solve mathematical problems, Get to know the basic machine and deep learning concepts, Train and test neural networks to fit your data model, Make predictions using regression algorithms, Analyze your data with a clustering procedure, Develop algorithms for clustering and data classification, Use GPU computing to analyze big data

Apache Spark Machine Learning Blueprints

Key Features, Put machine learning principles into practice to solve real-world problemsGet to grips with Python's impressive range of Machine Learning libraries and frameworksFrom retrieving data from APIs to cleaning and visualization, become more confident at tackling every stage of the data pipeline, Book Description, Machine Learning is transforming the way we understand and interact with the world around us. But how much do you really understand it? How confident are you interacting with the tools and models that drive it?, Python Machine Learning Blueprints puts your skills and knowledge to the test, guiding you through the development of some awesome machine learning applications and algorithms with real-world examples that demonstrate how to put concepts into practice., You’ll learn how to use cluster techniques to discover bargain air fares, and apply linear regression to find yourself a cheap apartment – and much more. Everything you learn is backed by a real-world example, whether its data manipulation or statistical modelling., That way you’re never left floundering in theory – you’ll be simply collecting and analyzing data in a way that makes a real impact., What you will learn, Explore and use Python's impressive machine learning ecosystemSuccessfully evaluate and apply the most effective models to problemsLearn the fundamentals of NLP - and put them into practiceVisualize data for maximum impact and clarityDeploy machine learning models using third party APIsGet to grips with feature engineering, About the Author, Alexander T. Combs is an experienced data scientist, strategist, and developer with a background in financial data extraction, natural language processing and generation, and quantitative and statistical modeling. He is currently a full-time lead instructor for a data science immersive program in New York City., Table of Contents, The Python Machine Learning EcosystemBuild an App to Find Underpriced ApartmentsBuild an App to Find Cheap AirfaresForecast the IPO Market using Logistic RegressionCreate a Custom NewsfeedPredict whether Your Content Will Go ViralForecast the Stock Market with Machine LearningBuild an Image Similarity EngineBuild a ChatbotBuild a Recommendation Engine

Spark for Python Developers

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