Python For Data Mining Quick Syntax Reference
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Python For Data Mining Quick Syntax Reference
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Author : Valentina Porcu
language : en
Publisher: Apress
Release Date : 2018-12-19
Python For Data Mining Quick Syntax Reference written by Valentina Porcu and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-19 with Computers categories.
Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This book provides you with a handy reference and tutorial on topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis. Python for Data Mining Quick Syntax Reference covers each concept concisely, with many illustrative examples. You'll be introduced to several data mining packages, with examples of how to use each of them. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning. What You'll Learn Install Python and choose a development environment Understand the basic concepts of object-oriented programming Import, open, and edit files Review the differences between Python 2.x and 3.x Who This Book Is For Programmers new to Python's data mining packages or with experience in other languages, who want a quick guide to Pythonic tools and techniques.
Mathematics And Computer Science Volume 1
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Author : Sharmistha Ghosh
language : en
Publisher: John Wiley & Sons
Release Date : 2023-07-19
Mathematics And Computer Science Volume 1 written by Sharmistha Ghosh and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-19 with Mathematics categories.
MATHEMATICS AND COMPUTER SCIENCE This first volume in a new multi-volume set gives readers the basic concepts and applications for diverse ideas and innovations in the field of computing together with its growing interactions with mathematics. This new edited volume from Wiley-Scrivener is the first of its kind to present scientific and technological innovations by leading academicians, eminent researchers, and experts around the world in the areas of mathematical sciences and computing. The chapters focus on recent advances in computer science, and mathematics, and where the two intersect to create value for end users through practical applications of the theory. The chapters herein cover scientific advancements across a diversified spectrum that includes differential as well as integral equations with applications, computational fluid dynamics, nanofluids, network theory and optimization, control theory, machine learning and artificial intelligence, big data analytics, Internet of Things, cryptography, fuzzy automata, statistics, and many more. Readers of this book will get access to diverse ideas and innovations in the field of computing together with its growing interactions in various fields of mathematics. Whether for the engineer, scientist, student, academic, or other industry professional, this is a must-have for any library.
Numerical Methods Using Python For Scientists And Engineers
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Author : Pankaj Dumka
language : en
Publisher: Blue Rose Publishers
Release Date : 2022-11-21
Numerical Methods Using Python For Scientists And Engineers written by Pankaj Dumka and has been published by Blue Rose Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-21 with Technology & Engineering categories.
The book is specifically intended for scientists, engineers, and engineering students who have taken a course on numeric methods and wish to comprehend and learn the subject through programming. The book's chapters are written methodically (step-by-step) so that programming becomes simple. More emphasis is placed on computationally modelling the methodologies and discussing the numerical method. Python is chosen as the programming language because it is simple to comprehend and use compared to other programming languages. The book allows readers to use and experiment with the approaches it describes. With very few adjustments, many of the programmes in the book can be utilised for applications in science and engineering.
Applications Of Evolutionary Computation
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Author : Juan Luis Jiménez Laredo
language : en
Publisher: Springer Nature
Release Date : 2022-04-15
Applications Of Evolutionary Computation written by Juan Luis Jiménez Laredo and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-15 with Computers categories.
This book constitutes the refereed proceedings of the 25th International Conference on Applications of Evolutionary Computation, EvoApplications 2022, held as part of Evo*2022, in April 2022, co-located with the Evo*2022 events EuroGP, EvoCOP, and EvoMUSART. The 46 revised full papers presented in this book were carefully reviewed and selected from 67 submissions.
Handbook Of Research On Technological Advances Of Library And Information Science In Industry 5 0
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Author : Holland, Barbara Jane
language : en
Publisher: IGI Global
Release Date : 2022-12-29
Handbook Of Research On Technological Advances Of Library And Information Science In Industry 5 0 written by Holland, Barbara Jane and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-29 with Language Arts & Disciplines categories.
Industry 5.0 is advancing the collaboration between humans and machines and is finding value through connected virtual and human experiences. This technological revolution benefits numerous fields; however, the library and information science industry in particular can evolve and expand by embracing Industry 5.0. The Handbook of Research on Technological Advances of Library and Information Science in Industry 5.0 considers the current research and future trends of technological advances in library and information science related to Industry 5.0. Covering key topics such as robotics, smart libraries, augmented reality, digital libraries, and artificial intelligence, this major reference work is ideal for librarians, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.
Python Data Science Handbook
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Author : Jake VanderPlas
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-11-21
Python Data Science Handbook written by Jake VanderPlas and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-11-21 with Computers categories.
For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python. With this handbook, you’ll learn how to use: IPython and Jupyter: provide computational environments for data scientists using Python NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python Matplotlib: includes capabilities for a flexible range of data visualizations in Python Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
The Python Quick Syntax Reference
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Author : Gregory Walters
language : en
Publisher: Apress
Release Date : 2014-02-28
The Python Quick Syntax Reference written by Gregory Walters and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-02-28 with Computers categories.
The Python Quick Syntax Reference is the "go to" book that contains an easy to read and use guide to Python programming and development. This condensed code and syntax reference presents the Python language in a well-organized format designed to be used time and again. You won't find jargon, bloated samples, case studies, or history of Hello World and computer theory in this handy reference. This Python syntax reference is packed with useful information and is a must-have for any Python developer.
Python Data Mining Quick Start Guide
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Author : Nathan Greeneltch
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-25
Python Data Mining Quick Start Guide written by Nathan Greeneltch and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-25 with Computers categories.
Explore the different data mining techniques using the libraries and packages offered by Python Key FeaturesGrasp the basics of data loading, cleaning, analysis, and visualizationUse the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data miningYour one-stop guide to build efficient data mining pipelines without going into too much theoryBook Description Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle. What you will learnExplore the methods for summarizing datasets and visualizing/plotting dataCollect and format data for analytical workAssign data points into groups and visualize clustering patternsLearn how to predict continuous and categorical outputs for dataClean, filter noise from, and reduce the dimensions of dataSerialize a data processing model using scikit-learn’s pipeline featureDeploy the data processing model using Python’s pickle moduleWho this book is for Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.
Data Science Quick Reference Manual Exploratory Data Analysis Metrics Models
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Author : Mario A. B. Capurso
language : en
Publisher: Mario Capurso
Release Date :
Data Science Quick Reference Manual Exploratory Data Analysis Metrics Models written by Mario A. B. Capurso and has been published by Mario Capurso this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Third of a series of books, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. Since this text uses Orange for the application aspects, it describes its installation and widgets. Then it considers the concept of model, its life cycle and the relationship with measures and metrics. The measures of localization, dispersion, asymmetry, correlation, similarity, distance are then described. The test and score metrics used in machine learning, those relating to texts and documents, the association metrics between items in a shopping cart, the relationship between objects, similarity between sets and between graphs, similarity between time series are considered. As a preliminary activity to the modeling phase, the Exploration Data Analysis is deepened in terms of questions, process, techniques and types of problems. For each type of problem, the recommended graphs, the methods of interpreting the results and their implementation in Orange are considered. The text is accompanied by supporting material and you can download the samples in Orange and the test data.
Data Science Quick Reference Manual Advanced Machine Learning And Deployment
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Author : Mario A. B. Capurso
language : en
Publisher: Mario Capurso
Release Date :
Data Science Quick Reference Manual Advanced Machine Learning And Deployment written by Mario A. B. Capurso and has been published by Mario Capurso this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.