Learning Jupyter 5

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Learning Jupyter 5

Author : Dan Toomey
Publisher : Packt Publishing Ltd
ISBN 13 : 1789137446
Total Pages : 268 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Learning Jupyter 5 by : Dan Toomey

Download or read book Learning Jupyter 5 written by Dan Toomey and published by Packt Publishing Ltd. This book was released on 2018-08-30 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with Jupyter Key Features Learn how to use Jupyter 5.x features such as cell tagging and attractive table styles Leverage big data tools and datasets with different Python packages Explore multiple-user Jupyter Notebook servers Book Description The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you’ve explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization. What you will learn Install and run the Jupyter Notebook system on your machine Implement programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook Use interactive widgets to manipulate and visualize data in real time Start sharing your Notebook with colleagues Invite your colleagues to work with you on the same Notebook Organize your Notebook using Jupyter namespaces Access big data in Jupyter for dealing with large datasets using Spark Who this book is for Learning Jupyter 5 is for developers, data scientists, machine learning users, and anyone working on data analysis or data science projects across different teams. Data science professionals will also find this book useful for performing technical and scientific computing collaboratively.

Learning Jupyter 5 - Second Edition

Author : Dan Toomey
Publisher :
ISBN 13 :
Total Pages : 282 pages
Book Rating : 4.:/5 (19 download)

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Book Synopsis Learning Jupyter 5 - Second Edition by : Dan Toomey

Download or read book Learning Jupyter 5 - Second Edition written by Dan Toomey and published by . This book was released on 2018 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create and share livecode, equations, visualizations, and explanatory text, in both a single document and a web browser with Jupyter Key Features Learn how to use Jupyter 5.x features such as cell tagging and attractive table styles Leverage big data tools and datasets with different Python packages Explore multiple-user Jupyter Notebook servers Book Description The Jupyter Notebook allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, and machine learning. Learning Jupyter 5 will help you get to grips with interactive computing using real-world examples. The book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next, you will learn to integrate the Jupyter system with different programming languages such as R, Python, Java, JavaScript, and Julia, and explore various versions and packages that are compatible with the Notebook system. Moving ahead, you will master interactive widgets and namespaces and work with Jupyter in a multi-user mode. By the end of this book, you will have used Jupyter with a big dataset and be able to apply all the functionalities you've explored throughout the book. You will also have learned all about the Jupyter Notebook and be able to start performing data transformation, numerical simulation, and data visualization. What you will learn Install and run the Jupyter Notebook system on your machine Implement programming languages such as R, Python, Julia, and JavaScript with the Jupyter Notebook Use interactive widgets to manipulate and visualize data in real time Start sharing your Notebook with colleagues Invite your colleagues to work with you on the same Notebook Organize your Notebook using Jupyter namespaces Access big data in Jupyter for dealing with large datasets using Spark Who this book is for Learning Jupyter 5 is for developers, data scientists, machine learning users, and anyone working on data analysis or data science projects across different teams. Data science professionals will also find this book useful for performing technical and scientific computing collaboratively. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If ...

Learning Jupyter

Author : Dan Toomey
Publisher : Packt Publishing Ltd
ISBN 13 : 1785889451
Total Pages : 230 pages
Book Rating : 4.7/5 (858 download)

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Book Synopsis Learning Jupyter by : Dan Toomey

Download or read book Learning Jupyter written by Dan Toomey and published by Packt Publishing Ltd. This book was released on 2016-11-30 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to write code, mathematics, graphics, and output, all in a single document, as well as in a web browser using Project Jupyter About This Book Learn to write, execute, and comment your live code and formulae all under one roof using this unique guide This one-stop solution on Project Jupyter will teach you everything you need to know to perform scientific computation with ease This easy-to-follow, highly practical guide lets you forget your worries in scientific application development by leveraging big data tools such as Apache Spark, Python, R etc Who This Book Is For This book caters to all developers, students, or educators who want to execute code, see output, and comment all in the same document, in the browser. Data science professionals will also find this book very useful to perform technical and scientific computing in a graphical, agile manner. What You Will Learn Install and run the Jupyter Notebook system on your machine Implement programming languages such as R, Python, Julia, and JavaScript with Jupyter Notebook Use interactive widgets to manipulate and visualize data in real time Start sharing your Notebook with colleagues Invite your colleagues to work with you in the same Notebook Organize your Notebook using Jupyter namespaces Access big data in Jupyter In Detail Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This book starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next we'll help you will learn to integrate Jupyter system with different programming languages such as R, Python, JavaScript, and Julia and explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you master interactive widgets, namespaces, and working with Jupyter in a multiuser mode. Towards the end, you will use Jupyter with a big data set and will apply all the functionalities learned throughout the book. Style and approach This comprehensive practical guide will teach you how to work with the Jupyter Notebook system. It demonstrates the integration of various programming languages with Jupyter Notebook through hands-on examples in every chapter.

Data Science with Jupyter

Author : Gupta Prateek
Publisher : BPB Publications
ISBN 13 : 9389423708
Total Pages : 323 pages
Book Rating : 4.3/5 (894 download)

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Book Synopsis Data Science with Jupyter by : Gupta Prateek

Download or read book Data Science with Jupyter written by Gupta Prateek and published by BPB Publications. This book was released on 2019-09-20 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step guide to practising data science techniques with Jupyter notebooksKey features Acquire Python skills to do independent data science projects Learn the basics of linear algebra and statistical science in Python way Understand how and when they're used in data science Build predictive models, tune their parameters and analyze performance in few steps Cluster, transform, visualize, and extract insights from unlabelled datasets Learn how to use matplotlib and seaborn for data visualization Implement and save machine learning models for real-world business scenarios Description Modern businesses are awash with data, making data driven decision-making tasks increasingly complex. As a result, relevant technical expertise and analytical skills are required to do such tasks. This book aims to equip you with just enough knowledge of Python in conjunction with skills to use powerful tool such as Jupyter Notebook in order to succeed in the role of a data scientist. The book starts with a brief introduction to the world of data science and the opportunities you may come across along with an overview of the key topics covered in the book. You will learn how to setup Anaconda installation which comes with Jupyter and preinstalled Python packages. Before diving in to several supervised, unsupervised and other machine learning techniques, you'll learn how to use basic data structures, functions, libraries and packages required to import, clean, visualize and process data. Several machine learning techniques such as regression, classification, clustering, time-series etc have been explained with the use of practical examples and by comparing the performance of various models. By the end of the book, you will come across few case studies to put your knowledge to practice and solve real-life business problems such as building a movie recommendation engine, classifying spam messages, predicting the ability of a borrower to repay loan on time and time series forecasting of housing prices. Remember to practice additional examples provided in the code bundle of the book to master these techniques.Who this book is forThe book is intended for anyone looking for a career in data science, all aspiring data scientists who want to learn the most powerful programming language in Machine Learning or working professionals who want to switch their career in Data Science. While no prior knowledge of Data Science or related technologies is assumed, it will be helpful to have some programming experience.Table of contents1. Data Science Fundamentals2. Installing Software and Setting up3. Lists and Dictionaries4. Function and Packages5. NumPy Foundation6. Pandas and Dataframe7. Interacting with Databases8. Thinking Statistically in Data Science9. How to import data in Python?10. Cleaning of imported data11. Data Visualization12. Data Pre-processing13. Supervised Machine Learning14. Unsupervised Machine Learning15. Handling Time-Series Data16. Time-Series Methods 17. Case Study - 118. Case Study - 219. Case Study - 320. Case Study - 4About the authorPrateek is a Data Enthusiast and loves the data driven technologies. Prateek has total 7 years of experience and currently he is working as a Data Scientist in an MNC. He has worked with finance and retail clients and has developed Machine Learning and Deep Learning solutions for their business. His keen area of interest is in natural language processing and in computer vision. In leisure he writes posts about Data Science with Python in his blog.

Jupyter for Data Science

Author : Paul Jeon
Publisher :
ISBN 13 : 9781785880070
Total Pages : 400 pages
Book Rating : 4.8/5 (8 download)

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Book Synopsis Jupyter for Data Science by : Paul Jeon

Download or read book Jupyter for Data Science written by Paul Jeon and published by . This book was released on 2017-03-31 with total page 400 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore the power of Jupyter and start deploying it in various contexts with the help of this highly practical, up-to-date guideAbout This Book* Find an easy way to code, execute, document, and share your live code with this unique guide* Learn to code efficient notebooks for interactive data analysis and deploy dashboards as web applications* This example-rich guide teaches you how to use Jupyter kernels for the major players in data science-Python, R, and JuliaWho This Book Is ForThis book is for those who are already familiar with data analytics languages such as Python, Scala, or R. It will be particularly useful for those who have used Jupyter but want to take their data analytics skills to the next level by utilizing Jupyter and other data science tools.What you will learn* Understand all the functionalities of Jupyter Notebooks such as creating / sharing documents with code, equations, and visualizations* Find out how to secure and share Notebooks* Use Python, Scala, and R with multiple other packages and other web applications to build different data visualization platforms with Jupyter* Create Jupyter Extensions to build new applications* Develop code that is efficient and effective in the realm of data science / analysis* Build interactive dashboards / widgets of Notebooks* Improve the scalability and performance of Notebooks* Use Jupyter for Machine LearningIn DetailJupyter is a very popular web application that allows you to code, analyze vast amounts of data, and create visualization, text, and rich media in a single document that can be shared across people you wish to collaborate with. Initially designed and used for statistical analysis and creating visualizations out of data, Jupyter Notebook has become so popular among data scientists that over 150,000 Jupyter Notebooks have been created on GitHub.This book will take you a step further with notebooks and help you to build multiple data analytics platforms using Jupyter and other data science tools. You'll learn different ways to engineer your data and analyze it for different purposes using multiple Jupyter Notebooks. You'll begin by setting up a data science environment to create and share Jupyter Notebooks. You'll learn to create Jupyter Notebooks to analyze simple to complex big datasets and visualize using python packages.Further on, you'll learn to capitalize on Python's flexibility and R's structured statistical packages to accelerate your data science investigations for real-world applications. Additionally, we'll cover techniques to scale an application using Jupyter extensions for Spark and other dynamic widgets. By the end of the book, you'll have gained mastery over creating and integrating multiple notebooks, being able to secure and optimize them to perform intuitive, iterative, and robust analytics.

Practical Data Science with Jupyter

Author : Prateek Gupta
Publisher : BPB Publications
ISBN 13 : 9389898064
Total Pages : 437 pages
Book Rating : 4.3/5 (898 download)

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Book Synopsis Practical Data Science with Jupyter by : Prateek Gupta

Download or read book Practical Data Science with Jupyter written by Prateek Gupta and published by BPB Publications. This book was released on 2021-03-01 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve business problems with data-driven techniques and easy-to-follow Python examples Ê KEY FEATURESÊÊ _ Essential coverage on statistics and data science techniques. _ Exposure to Jupyter, PyCharm, and use of GitHub. _ Real use-cases, best practices, and smart techniques on the use of data science for data applications. DESCRIPTIONÊÊ This book begins with an introduction to Data Science followed by the Python concepts. The readers will understand how to interact with various database and Statistics concepts with their Python implementations. You will learn how to import various types of data in Python, which is the first step of the data analysis process. Once you become comfortable with data importing, you willÊ clean the dataset and after that will gain an understanding about various visualization charts. This book focuses on how to apply feature engineering techniques to make your data more valuable to an algorithm. The readers will get to know various Machine Learning Algorithms, concepts, Time Series data, and a few real-world case studies. This book also presents some best practices that will help you to be industry-ready. This book focuses on how to practice data science techniques while learning their concepts using Python and Jupyter. This book is a complete answer to the most common question that how can you get started with Data Science instead of explaining Mathematics and Statistics behind the Machine Learning Algorithms. WHAT YOU WILL LEARN _ Rapid understanding of Python concepts for data science applications. _ Understand and practice how to run data analysis with data science techniques and algorithms. _ Learn feature engineering, dealing with different datasets, and most trending machine learning algorithms. _ Become self-sufficient to perform data science tasks with the best tools and techniques. Ê WHO THIS BOOK IS FORÊÊ This book is for a beginner or an experienced professional who is thinking about a career or a career switch to Data Science. Each chapter contains easy-to-follow Python examples. Ê TABLE OF CONTENTS 1. Data Science Fundamentals 2. Installing Software and System Setup 3. Lists and Dictionaries 4. Package, Function, and Loop 5. NumPy Foundation 6. Pandas and DataFrame 7. Interacting with Databases 8. Thinking Statistically in Data Science 9. How to Import Data in Python? 10. Cleaning of Imported Data 11. Data Visualization 12. Data Pre-processing 13. Supervised Machine Learning 14. Unsupervised Machine Learning 15. Handling Time-Series Data 16. Time-Series Methods 17. Case Study-1 18. Case Study-2 19. Case Study-3 20. Case Study-4 21. Python Virtual Environment 22. Introduction to An Advanced Algorithm - CatBoost 23. Revision of All ChaptersÕ Learning

Beginning Data Science with Python and Jupyter

Author : Alex Galea
Publisher : Packt Publishing Ltd
ISBN 13 : 1789534658
Total Pages : 194 pages
Book Rating : 4.7/5 (895 download)

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Book Synopsis Beginning Data Science with Python and Jupyter by : Alex Galea

Download or read book Beginning Data Science with Python and Jupyter written by Alex Galea and published by Packt Publishing Ltd. This book was released on 2018-06-05 with total page 194 pages. Available in PDF, EPUB and Kindle. Book excerpt: Getting started with data science doesn't have to be an uphill battle. This step-by-step guide is ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction. Key Features Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers and Random Forests Discover how you can use web scraping to gather and parse your own bespoke datasets Book Description Get to grips with the skills you need for entry-level data science in this hands-on Python and Jupyter course. You'll learn about some of the most commonly used libraries that are part of the Anaconda distribution, and then explore machine learning models with real datasets to give you the skills and exposure you need for the real world. We'll finish up by showing you how easy it can be to scrape and gather your own data from the open web, so that you can apply your new skills in an actionable context. What you will learn Get up and running with the Jupyter ecosystem and some example datasets Learn about key machine learning concepts like SVM, KNN classifiers, and Random Forests Plan a machine learning classification strategy and train classification, models Use validation curves and dimensionality reduction to tune and enhance your models Discover how you can use web scraping to gather and parse your own bespoke datasets Scrape tabular data from web pages and transform them into Pandas DataFrames Create interactive, web-friendly visualizations to clearly communicate your findings Who this book is for This book is ideal for professionals with a variety of job descriptions across large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries like Pandas, Matplotlib and Pandas providing you a useful head start.

Applied Data Science with Python and Jupyter

Author : Alex Galea
Publisher : Packt Publishing Ltd
ISBN 13 : 1789951925
Total Pages : 192 pages
Book Rating : 4.7/5 (899 download)

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Book Synopsis Applied Data Science with Python and Jupyter by : Alex Galea

Download or read book Applied Data Science with Python and Jupyter written by Alex Galea and published by Packt Publishing Ltd. This book was released on 2018-10-31 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become the master player of data exploration by creating reproducible data processing pipelines, visualizations, and prediction models for your applications. Key FeaturesGet up and running with the Jupyter ecosystem and some example datasetsLearn about key machine learning concepts such as SVM, KNN classifiers, and Random ForestsDiscover how you can use web scraping to gather and parse your own bespoke datasetsBook Description Getting started with data science doesn't have to be an uphill battle. Applied Data Science with Python and Jupyter is a step-by-step guide ideal for beginners who know a little Python and are looking for a quick, fast-paced introduction to these concepts. In this book, you'll learn every aspect of the standard data workflow process, including collecting, cleaning, investigating, visualizing, and modeling data. You'll start with the basics of Jupyter, which will be the backbone of the book. After familiarizing ourselves with its standard features, you'll look at an example of it in practice with our first analysis. In the next lesson, you dive right into predictive analytics, where multiple classification algorithms are implemented. Finally, the book ends by looking at data collection techniques. You'll see how web data can be acquired with scraping techniques and via APIs, and then briefly explore interactive visualizations. What you will learnGet up and running with the Jupyter ecosystemIdentify potential areas of investigation and perform exploratory data analysisPlan a machine learning classification strategy and train classification modelsUse validation curves and dimensionality reduction to tune and enhance your modelsScrape tabular data from web pages and transform it into Pandas DataFramesCreate interactive, web-friendly visualizations to clearly communicate your findingsWho this book is for Applied Data Science with Python and Jupyter is ideal for professionals with a variety of job descriptions across a large range of industries, given the rising popularity and accessibility of data science. You'll need some prior experience with Python, with any prior work with libraries such as Pandas, Matplotlib, and Pandas providing you a useful head start.

Learning IPython for Interactive Computing and Data Visualization

Author : Cyrille Rossant
Publisher : Packt Publishing Ltd
ISBN 13 : 1783986999
Total Pages : 201 pages
Book Rating : 4.7/5 (839 download)

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Book Synopsis Learning IPython for Interactive Computing and Data Visualization by : Cyrille Rossant

Download or read book Learning IPython for Interactive Computing and Data Visualization written by Cyrille Rossant and published by Packt Publishing Ltd. This book was released on 2015-10-21 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get started with Python for data analysis and numerical computing in the Jupyter notebook About This Book Learn the basics of Python in the Jupyter Notebook Analyze and visualize data with pandas, NumPy, matplotlib, and seaborn Perform highly-efficient numerical computations with Numba, Cython, and ipyparallel Who This Book Is For This book targets students, teachers, researchers, engineers, analysts, journalists, hobbyists, and all data enthusiasts who are interested in analyzing and visualizing real-world datasets. If you are new to programming and data analysis, this book is exactly for you. If you're already familiar with another language or analysis software, you will also appreciate this introduction to the Python data analysis platform. Finally, there are more technical topics for advanced readers. No prior experience is required; this book contains everything you need to know. What You Will Learn Install Anaconda and code in Python in the Jupyter Notebook Load and explore datasets interactively Perform complex data manipulations effectively with pandas Create engaging data visualizations with matplotlib and seaborn Simulate mathematical models with NumPy Visualize and process images interactively in the Jupyter Notebook with scikit-image Accelerate your code with Numba, Cython, and IPython.parallel Extend the Notebook interface with HTML, JavaScript, and D3 In Detail Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data. Style and approach This is a hands-on beginner-friendly guide to analyze and visualize data on real-world examples with Python and the Jupyter Notebook.

Jupyter Notebook for All

Author : Dan Toomey
Publisher :
ISBN 13 : 9781788299176
Total Pages : pages
Book Rating : 4.2/5 (991 download)

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Book Synopsis Jupyter Notebook for All by : Dan Toomey

Download or read book Jupyter Notebook for All written by Dan Toomey and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Jupyter Notebook is a web-based environment that enables interactive computing in notebook documents. It allows you to create and share documents that contain live code, equations, visualizations, and explanatory text. The Jupyter Notebook system is extensively used in domains such as data cleaning and transformation, numerical simulation, statistical modeling, machine learning, and much more. This tutorial starts with a detailed overview of the Jupyter Notebook system and its installation in different environments. Next you will learn to integrate the Jupyter system with different programming languages such as R, Python, JavaScript, and Julia; further, you'll explore the various versions and packages that are compatible with the Notebook system. Moving ahead, you'll master interactive widgets, namespaces, and working with Jupyter in multiuser mode. Towards the end, you will use Jupyter with a big dataset and will apply all the functionalities learned throughout the video."--Resource description page.

Jupyter Cookbook

Author : Dan Toomey
Publisher : Packt Publishing
ISBN 13 : 9781788839440
Total Pages : 238 pages
Book Rating : 4.8/5 (394 download)

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Book Synopsis Jupyter Cookbook by : Dan Toomey

Download or read book Jupyter Cookbook written by Dan Toomey and published by Packt Publishing. This book was released on 2018-04-28 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of the popular Jupyter notebooks to simplify your data science tasks without any hassle Key Features Create and share interactive documents with live code, text and visualizations Integrate popular programming languages such as Python, R, Julia, Scala with Jupyter Develop your widgets and interactive dashboards with these innovative recipes Book Description Jupyter has garnered a strong interest in the data science community of late, as it makes common data processing and analysis tasks much simpler. This book is for data science professionals who want to master various tasks related to Jupyter to create efficient, easy-to-share, scientific applications. The book starts with recipes on installing and running the Jupyter Notebook system on various platforms and configuring the various packages that can be used with it. You will then see how you can implement different programming languages and frameworks, such as Python, R, Julia, JavaScript, Scala, and Spark on your Jupyter Notebook. This book contains intuitive recipes on building interactive widgets to manipulate and visualize data in real time, sharing your code, creating a multi-user environment, and organizing your notebook. You will then get hands-on experience with Jupyter Labs, microservices, and deploying them on the web. By the end of this book, you will have taken your knowledge of Jupyter to the next level to perform all key tasks associated with it. What you will learn Install Jupyter and configure engines for Python, R, Scala and more Access and retrieve data on Jupyter Notebooks Create interactive visualizations and dashboards for different scenarios Convert and share your dynamic codes using HTML, JavaScript, Docker, and more Create custom user data interactions using various Jupyter widgets Manage user authentication and file permissions Interact with Big Data to perform numerical computing and statistical modeling Get familiar with Jupyter's next-gen user interface - JupyterLab Who this book is for This cookbook is for data science professionals, developers, technical data analysts, and programmers who want to execute technical coding, visualize output, and do scientific computing in one tool. Prior understanding of data science concepts will be helpful, but not mandatory, to use this book.

Machine Learning Concepts with Python and the Jupyter Notebook Environment

Author : Nikita Silaparasetty
Publisher : Apress
ISBN 13 : 9781484259665
Total Pages : 290 pages
Book Rating : 4.2/5 (596 download)

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Book Synopsis Machine Learning Concepts with Python and the Jupyter Notebook Environment by : Nikita Silaparasetty

Download or read book Machine Learning Concepts with Python and the Jupyter Notebook Environment written by Nikita Silaparasetty and published by Apress. This book was released on 2020-10-06 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Create, execute, modify, and share machine learning applications with Python and TensorFlow 2.0 in the Jupyter Notebook environment. This book breaks down any barriers to programming machine learning applications through the use of Jupyter Notebook instead of a text editor or a regular IDE. You’ll start by learning how to use Jupyter Notebooks to improve the way you program with Python. After getting a good grounding in working with Python in Jupyter Notebooks, you’ll dive into what TensorFlow is, how it helps machine learning enthusiasts, and how to tackle the challenges it presents. Along the way, sample programs created using Jupyter Notebooks allow you to apply concepts from earlier in the book. Those who are new to machine learning can dive in with these easy programs and develop basic skills. A glossary at the end of the book provides common machine learning and Python keywords and definitions to make learning even easier. What You Will Learn Program in Python and TensorFlow Tackle basic machine learning obstacles Develop in the Jupyter Notebooks environment Who This Book Is For Ideal for Machine Learning and Deep Learning enthusiasts who are interested in programming with Python using Tensorflow 2.0 in the Jupyter Notebook Application. Some basic knowledge of Machine Learning concepts and Python Programming (using Python version 3) is helpful.

Practical Data Analysis Using Jupyter Notebook

Author : Marc Wintjen
Publisher : Packt Publishing Ltd
ISBN 13 : 1838825096
Total Pages : 309 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Practical Data Analysis Using Jupyter Notebook by : Marc Wintjen

Download or read book Practical Data Analysis Using Jupyter Notebook written by Marc Wintjen and published by Packt Publishing Ltd. This book was released on 2020-06-19 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand data analysis concepts to make accurate decisions based on data using Python programming and Jupyter Notebook Key FeaturesFind out how to use Python code to extract insights from data using real-world examplesWork with structured data and free text sources to answer questions and add value using dataPerform data analysis from scratch with the help of clear explanations for cleaning, transforming, and visualizing dataBook Description Data literacy is the ability to read, analyze, work with, and argue using data. Data analysis is the process of cleaning and modeling your data to discover useful information. This book combines these two concepts by sharing proven techniques and hands-on examples so that you can learn how to communicate effectively using data. After introducing you to the basics of data analysis using Jupyter Notebook and Python, the book will take you through the fundamentals of data. Packed with practical examples, this guide will teach you how to clean, wrangle, analyze, and visualize data to gain useful insights, and you'll discover how to answer questions using data with easy-to-follow steps. Later chapters teach you about storytelling with data using charts, such as histograms and scatter plots. As you advance, you'll understand how to work with unstructured data using natural language processing (NLP) techniques to perform sentiment analysis. All the knowledge you gain will help you discover key patterns and trends in data using real-world examples. In addition to this, you will learn how to handle data of varying complexity to perform efficient data analysis using modern Python libraries. By the end of this book, you'll have gained the practical skills you need to analyze data with confidence. What you will learnUnderstand the importance of data literacy and how to communicate effectively using dataFind out how to use Python packages such as NumPy, pandas, Matplotlib, and the Natural Language Toolkit (NLTK) for data analysisWrangle data and create DataFrames using pandasProduce charts and data visualizations using time-series datasetsDiscover relationships and how to join data together using SQLUse NLP techniques to work with unstructured data to create sentiment analysis modelsDiscover patterns in real-world datasets that provide accurate insightsWho this book is for This book is for aspiring data analysts and data scientists looking for hands-on tutorials and real-world examples to understand data analysis concepts using SQL, Python, and Jupyter Notebook. Anyone looking to evolve their skills to become data-driven personally and professionally will also find this book useful. No prior knowledge of data analysis or programming is required to get started with this book.

Learning Path

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Learning Path by :

Download or read book Learning Path written by and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "The Jupyter Notebook is an open-source web application and has support for more than 40 programming languages including those popular in data science such as Python, R, Julia, and Scala. This Learning Path starts with a brief on the Jupyter Notebook and its installation in different environments. You will learn how to write code, mathematics, graphics, and output, all in a single document and in a web browser, using Jupyter. Next, you will see how to integrate the Jupyter system with different programming languages such as R, Python, JavaScript, and Julia. Moving ahead, you will master interactive widgets, name spaces, and working with Jupyter in multiuser mode. You will also see how to share your Notebook with colleagues. Finally, you will learn to access Big Data using Jupyter. By the end of the Learning Path, you will be able to write code, mathematics, graphics, and output, all in a single document and web browser, using the Jupyter Notebook."--Resource description page.

Getting Started in Machine Learning

Author : Isabella Romeo
Publisher :
ISBN 13 : 9780996686082
Total Pages : 345 pages
Book Rating : 4.6/5 (86 download)

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Book Synopsis Getting Started in Machine Learning by : Isabella Romeo

Download or read book Getting Started in Machine Learning written by Isabella Romeo and published by . This book was released on 2020 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an introductory book in machine learning with a hands on approach. It uses Python 3 and Jupyter notebooks for all applications. The emphasis is primarily on learning to use existing libraries such as Scikit-Learn with easy recipes and existing data files that can found on-line. Topics include linear, multilinear, polynomial, stepwise, lasso, ridge, and logistic regression; ROC curves and measures of binary classification; nonlinear regression (including an introduction to gradient descent); classification and regression trees; random forests;? neural networks; probabilistic methods (KNN, naive Bayes', QDA, LDA); dimensionality reduction with PCA; support vector machines; and clustering with K-Means, hierarchical, and DBScan. Appendices provide a review of probability and linear algebra. While some mathematical foundation is provided, it is not essential for understanding the implementations. The target audience is advanced community college students and intermediate university students in the sciences and engineering.

Machine Learning for Business

Author : Doug Hudgeon
Publisher : Simon and Schuster
ISBN 13 : 1638353972
Total Pages : 410 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Machine Learning for Business by : Doug Hudgeon

Download or read book Machine Learning for Business written by Doug Hudgeon and published by Simon and Schuster. This book was released on 2019-12-24 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Imagine predicting which customers are thinking about switching to a competitor or flagging potential process failures before they happen Think about the benefits of forecasting tedious business processes and back-office tasks Envision quickly gauging customer sentiment from social media content (even large volumes of it). Consider the competitive advantage of making decisions when you know the most likely future events Machine learning can deliver these and other advantages to your business, and it’s never been easier to get started! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Machine learning can deliver huge benefits for everyday business tasks. With some guidance, you can get those big wins yourself without complex math or highly paid consultants! If you can crunch numbers in Excel, you can use modern ML services to efficiently direct marketing dollars, identify and keep your best customers, and optimize back office processes. This book shows you how. About the book Machine Learning for Business teaches business-oriented machine learning techniques you can do yourself. Concentrating on practical topics like customer retention, forecasting, and back office processes, you’ll work through six projects that help you form an ML-for-business mindset. To guarantee your success, you’ll use the Amazon SageMaker ML service, which makes it a snap to turn your questions into results. What's inside Identifying tasks suited to machine learning Automating back office processes Using open source and cloud-based tools Relevant case studies About the reader For technically inclined business professionals or business application developers. About the author Doug Hudgeon and Richard Nichol specialize in maximizing the value of business data through AI and machine learning for companies of any size. Table of Contents: PART 1 MACHINE LEARNING FOR BUSINESS 1 ¦ How machine learning applies to your business PART 2 SIX SCENARIOS: MACHINE LEARNING FOR BUSINESS 2 ¦ Should you send a purchase order to a technical approver? 3 ¦ Should you call a customer because they are at risk of churning? 4 ¦ Should an incident be escalated to your support team? 5 ¦ Should you question an invoice sent by a supplier? 6 ¦ Forecasting your company’s monthly power usage 7 ¦ Improving your company’s monthly power usage forecast PART 3 MOVING MACHINE LEARNING INTO PRODUCTION 8 ¦ Serving predictions over the web 9 ¦ Case studies

The The Applied Data Science Workshop

Author : Alex Galea
Publisher : Packt Publishing Ltd
ISBN 13 : 180020700X
Total Pages : 351 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis The The Applied Data Science Workshop by : Alex Galea

Download or read book The The Applied Data Science Workshop written by Alex Galea and published by Packt Publishing Ltd. This book was released on 2020-07-22 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Designed with beginners in mind, this workshop helps you make the most of Python libraries and the Jupyter Notebook’s functionality to understand how data science can be applied to solve real-world data problems. Key FeaturesGain useful insights into data science and machine learningExplore the different functionalities and features of a Jupyter NotebookDiscover how Python libraries are used with Jupyter for data analysisBook Description From banking and manufacturing through to education and entertainment, using data science for business has revolutionized almost every sector in the modern world. It has an important role to play in everything from app development to network security. Taking an interactive approach to learning the fundamentals, this book is ideal for beginners. You’ll learn all the best practices and techniques for applying data science in the context of real-world scenarios and examples. Starting with an introduction to data science and machine learning, you’ll start by getting to grips with Jupyter functionality and features. You’ll use Python libraries like sci-kit learn, pandas, Matplotlib, and Seaborn to perform data analysis and data preprocessing on real-world datasets from within your own Jupyter environment. Progressing through the chapters, you’ll train classification models using sci-kit learn, and assess model performance using advanced validation techniques. Towards the end, you’ll use Jupyter Notebooks to document your research, build stakeholder reports, and even analyze web performance data. By the end of The Applied Data Science Workshop, you’ll be prepared to progress from being a beginner to taking your skills to the next level by confidently applying data science techniques and tools to real-world projects. What you will learnUnderstand the key opportunities and challenges in data scienceUse Jupyter for data science tasks such as data analysis and modelingRun exploratory data analysis within a Jupyter NotebookVisualize data with pairwise scatter plots and segmented distributionAssess model performance with advanced validation techniquesParse HTML responses and analyze HTTP requestsWho this book is for If you are an aspiring data scientist who wants to build a career in data science or a developer who wants to explore the applications of data science from scratch and analyze data in Jupyter using Python libraries, then this book is for you. Although a brief understanding of Python programming and machine learning is recommended to help you grasp the topics covered in the book more quickly, it is not mandatory.