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Taschenbuch. Zustand: Neu. Streamlit for Data Science - Second Edition | Create interactive data apps in Python | Tyler Richards | Taschenbuch | Englisch | 2023 | Packt Publishing | EAN 9781803248226 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - An easy-to-follow and comprehensive guide to creating data apps with Streamlit, including how-to guides for working with cloud data warehouses like Snowflake, using pretrained Hugging Face and OpenAI models, and creating apps for job interviews.Key Features:Create machine learning apps with random forest, Hugging Face, and GPT-3.5 turbo modelsGain an insight into how experts harness Streamlit with in-depth interviews with Streamlit power usersDiscover the full range of Streamlit's capabilities via hands-on exercises to effortlessly create and deploy well-designed appsBook Description:If you work with data in Python and are looking to create data apps that showcase ML models and make beautiful interactive visualizations, then this is the ideal book for you. Streamlit for Data Science, Second Edition, shows you how to create and deploy data apps quickly, all within Python. This helps you create prototypes in hours instead of days!Written by a prolific Streamlit user and senior data scientist at Snowflake, this fully updated second edition builds on the practical nature of the previous edition with exciting updates, including connecting Streamlit to data warehouses like Snowflake, integrating Hugging Face and OpenAI models into your apps, and connecting and building apps on top of Streamlit databases. Plus, there is a totally updated code repository on GitHub to help you practice your newfound skills.You'll start your journey with the fundamentals of Streamlit and gradually build on this foundation by working with machine learning models and producing high-quality interactive apps. The practical examples of both personal data projects and work-related data-focused web applications will help you get to grips with more challenging topics such as Streamlit Components, beautifying your apps, and quick deployment.By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly.What You Will Learn:Set up your first development environment and create a basic Streamlit app from scratchCreate dynamic visualizations using built-in and imported Python librariesDiscover strategies for creating and deploying machine learning models in StreamlitDeploy Streamlit apps with Streamlit Community Cloud, AWS, and HerokuIntegrate Streamlit with Hugging Face, OpenAI, and SnowflakeBeautify Streamlit apps using themes and componentsImplement best practices for prototyping your data science work with StreamlitWho this book is for:This book is for data scientists and machine learning enthusiasts who want to get started with creating data apps in Streamlit. It is terrific for junior data scientists looking to gain some valuable new skills in a specific and actionable fashion and is also a great resource for senior data scientists looking for a comprehensive overview of the library and how people use it. Prior knowledge of Python programming is a must, and you'll get the most out of this book if you've used Python libraries like Pandas and NumPy in the past.
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Taschenbuch. Zustand: Neu. Getting Started with Streamlit for Data Science | Create and deploy Streamlit web applications from scratch in Python | Tyler Richards | Taschenbuch | Englisch | 2021 | Packt Publishing | EAN 9781800565500 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Create, deploy, and test your Python applications, analyses, and models with ease using StreamlitKey Features:Learn how to showcase machine learning models in a Streamlit application effectively and efficientlyBecome an expert Streamlit creator by getting hands-on with complex application creationDiscover how Streamlit enables you to create and deploy apps effortlesslyBook Description:Streamlit shortens the development time for the creation of data-focused web applications, allowing data scientists to create web app prototypes using Python in hours instead of days. Getting Started with Streamlit for Data Science takes a hands-on approach to helping you learn the tips and tricks that will have you up and running with Streamlit in no time.You'll start with the fundamentals of Streamlit by creating a basic app and gradually build on the foundation by producing high-quality graphics with data visualization and testing machine learning models. As you advance through the chapters, you'll walk through practical examples of both personal data projects and work-related data-focused web applications, and get to grips with more challenging topics such as using Streamlit Components, beautifying your apps, and quick deployment of your new apps.By the end of this book, you'll be able to create dynamic web apps in Streamlit quickly and effortlessly using the power of Python.What You Will Learn:Set up your first development environment and create a basic Streamlit app from scratchExplore methods for uploading, downloading, and manipulating data in Streamlit appsCreate dynamic visualizations in Streamlit using built-in and imported Python librariesDiscover strategies for creating and deploying machine learning models in StreamlitUse Streamlit sharing for one-click deploymentBeautify Streamlit apps using themes, Streamlit Components, and Streamlit sidebarImplement best practices for prototyping your data science work with StreamlitWho this book is for:This book is for data scientists and machine learning enthusiasts who want to create web apps using Streamlit. Whether you're a junior data scientist looking to deploy your first machine learning project in Python to improve your resume or a senior data scientist who wants to use Streamlit to make convincing and dynamic data analyses, this book will help you get there! Prior knowledge of Python programming will assist with understanding the concepts covered.