Master Python Ci Ncia De Dados Com Tutoria Virtual Ia
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Master Python Ci Ncia De Dados Com Tutoria Virtual Ia
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Author : Diego Rodrigues
language : pt-BR
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Ci Ncia De Dados Com Tutoria Virtual Ia written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine adquirir um livro completo e, de bônus, receber acesso a uma Tutoria Virtual assistida por IA 24/7 para personalizar a sua jornada de aprendizagem, fixação de conhecimentos e mentoria para o desenvolvimento e implementação de projetos reais... ... Bem-vindo à Revolução do Aprendizado Personalizado com Tutoria Virtual Assistida por IA! Descubra "Master Python: Ciência de Dados - Dos Fundamentos às Aplicações Avançadas com Tutoria Virtual IA", o guia essencial para profissionais e entusiastas que desejam dominar a ciência de dados com Python. Este manual inovador, escrito por Diego Rodrigues, um autor renomado com mais de 140 títulos publicados em seis idiomas, combina conteúdo de alta qualidade com a tecnologia avançada do IAGO, um tutor virtual desenvolvido e hospedado na plataforma OpenAI. O livro começa com uma introdução abrangente à ciência de dados, destacando a importância da área e o papel crucial que Python desempenha. A seguir, aborda os fundamentos de Python, cobrindo sintaxe básica, estruturas de dados e controle de fluxo, preparando uma base sólida para os capítulos subsequentes. Você aprenderá técnicas essenciais de manipulação e limpeza de dados usando bibliotecas como Pandas e NumPy, garantindo que seus dados estejam prontos para análise. Em seguida, explorará a análise exploratória de dados (EDA) com ferramentas como Matplotlib e Seaborn para descobrir padrões e insights valiosos. A visualização de dados é aprofundada com o uso de Plotly para criar gráficos interativos e Dash para desenvolver dashboards dinâmicos. O livro avança para machine learning, introduzindo conceitos básicos e tipos de aprendizado, seguidos pela preparação de dados e implementação de modelos com Scikit-Learn. Técnicas de regressão linear e polinomial são explicadas em detalhe, juntamente com a avaliação de desempenho dos modelos. Você também mergulhará no machine learning avançado com capítulos sobre classificação, clustering e redução de dimensionalidade. Técnicas de processamento de linguagem natural (NLP) são abordadas, utilizando bibliotecas como NLTK e SpaCy. A seção de deep learning cobre desde redes neurais básicas até aplicações avançadas com TensorFlow e Keras, incluindo redes neurais convolucionais (CNNs) e redes neurais recorrentes (RNNs). O livro ainda explora big data, ensinando como trabalhar com grandes volumes de dados usando Hadoop e Spark com Python. Finaliza com um guia completo sobre como conduzir um projeto de ciência de dados do início ao fim e discute a ética e a responsabilidade na ciência de dados, abordando práticas recomendadas e regulamentações. Aproveite o Valor Promocional de Lançamento por Tempo Limitado! Este livro completo foi cuidadosamente estruturado para atender às suas necessidades e superar suas expectativas, garantindo que você esteja preparado para enfrentar os desafios e aproveitar as oportunidades na área de ciência de dados. Abrangendo desde os fundamentos da ciência de dados até as aplicações mais avançadas, você aprenderá a utilizar Python para manipulação e análise de dados, visualização de dados, machine learning, deep learning, big data e muito mais. Abra a amostra do livro e descubra como acessar ao seleto clube dos profissionais de tecnologias de vanguarda. Aproveite essa oportunidade única e conquiste seus objetivos! TAGS: AWS IBM CISCO AZURE GOOGLE Python ciência dados manipulação análise visualização Pandas NumPy Matplotlib Seaborn Plotly Dash machine learning deep learning Scikit-Learn TensorFlow Keras big data Hadoop Spark análise exploratória de dados EDA modelos de regressão classificação clustering NLP processamento linguagem natural redes neurais convolucionais CNNs recorrentes RNNs aprendizado supervisionado aprendizado não supervisionado aprendizado por reforço transformação digital análise preditiva inteligência artificial Diego Rodrigues ciência aplicada projetos reais tutoria virtual OpenAI IAGO automação tarefas modelagem predição insights tecnológicos análise de comportamento inovação técnica desenvolvimento profissional técnicas avançadas SQL séries temporais redes sociais visualização interativa storytelling programação em Python ética privacidade regulamentações segurança cibernética coleta de dados tratamento de dados engenharia análise estatística visualização em tempo real relatórios automatizados data-driven decision making exploratory data analysis best practices cleaning transformation integration feature engineering pipeline desenvolvimento modelos implementação modelos validação avaliação de modelos otimização de modelos interpretação resultados comunicação de resultados transformação preditivos aplicações industriais análise negócios ferramentas open-source algoritmos de machine learning Python para data science aprendizado profundo aprendizado por transferência aprendizado contínuo sistemas recomendadores personalização conteúdos análise big data estrutura de dados cloud computing visualização 3D PySpark análise sentimentos predição churn análise de mercado análise financeira IoT data science para saúde smart cities análise risco segurança de dados ciências sociais visualização geoespacial análise de imagem reconhecimento padrões reconhecimento voz Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR
Master Python Engenharia De Dados Com Tutoria Virtual Ia
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Author : Diego Rodrigues
language : pt-BR
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Engenharia De Dados Com Tutoria Virtual Ia written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine você adquirir um livro e, de bônus, receber acesso a uma Tutoria Virtual assistida por IA 24/7 para personalizar a sua jornada de aprendizagem, fixação de conhecimentos e mentoria para o desenvolvimento e implementação de projetos reais... ...Bem-vindo à Revolução do Aprendizado Personalizado com Tutoria Virtual Assistida por IA! Descubra " MASTER PYTHON ENGENHARIA DE DADOS: Dos Fundamentos às Aplicações Avançadas com Tutoria Virtual IA", o guia essencial para profissionais e entusiastas que desejam dominar a engenharia de dados com Python. Este manual inovador, escrito por Diego Rodrigues, um autor com mais de 140 títulos publicados em seis idiomas, combina conteúdo de alta qualidade com a tecnologia avançada do IAGO, um tutor virtual desenvolvido e hospedado na plataforma OpenAI. Características Inovadoras: Personalização do Aprendizado: O IAGO adapta o conteúdo de acordo com seu nível de conhecimento, oferecendo explicações detalhadas e exercícios personalizados. Feedback Imediato: Receba correções e sugestões em tempo real, acelerando seu processo de aprendizado. Interatividade e Engajamento: Interaja com o tutor via texto ou voz, tornando o estudo mais dinâmico e motivador. Mentoria para Desenvolvimento de Projetos: Obtenha orientação prática para desenvolver e implementar projetos reais, aplicando o conhecimento adquirido. Flexibilidade Total: Acesse o tutor em qualquer lugar e a qualquer momento, seja pelo desktop, notebook ou smartphone com acesso web. Aproveite o Valor Promocional de Lançamento por Tempo Limitado! Não perca a oportunidade de transformar sua jornada de aprendizado com um método inovador e eficaz. Este livro foi cuidadosamente estruturado para atender às suas necessidades e superar suas expectativas, garantindo que você esteja preparado para enfrentar os desafios e aproveitar as oportunidades na área de engenharia de dados. Abra a amostra do livro e descubra como acessar ao seleto clube dos profissionais de tecnologias de vanguarda. Aproveite essa oportunidade única e conquiste seus objetivos! TAGS engenharia dados automação data science big Pandas NumPy Dask SQLAlchemy web scraping BeautifulSoup Scrapy APIs ETL DataOps Data Lakes Data Warehouses AWS Google Cloud Microsoft Azure Hadoop Spark machine learning inteligência artificial data pipelines visualização dados Matplotlib Seaborn análise dados bancos relacionais NoSQL MongoDB Apache Airflow Kafka tempo real governança dados segurança compliance mentoria Diego Rodrigues Tableau Power BI Snowflake Informatica Alation Talend Apache Flink Jupyter Notebooks DevOps Databricks Cloudera Hortonworks Teradata IBM Cloud Oracle Cloud Salesforce SAP HANA ElasticSearch Redis Kubernetes Docker Jenkins GitHub GitLab Continuous Integration Continuous Deployment CI/CD transformação digital análise preditiva inteligência negócios IoT Internet das Coisas cidades inteligentes saúde conectada indústria 4.0 fintechs varejo educação marketing inteligência competitiva ciência dados testes automatizados relatórios personalizados eficiência operacional Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR GITHUB
Master Python Cybersecurity Com Tutoria Virtual Ia
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Author : Diego Rodrigues
language : pt-BR
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Cybersecurity Com Tutoria Virtual Ia written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine você adquirir um livro e, de bônus, receber acesso a uma Tutoria Virtual assistida por IA 24/7 para personalizar a sua jornada de aprendizagem, fixação de conhecimentos e mentoria para o desenvolvimento e implementação de projetos reais... ... Bem-vindo à Revolução do Aprendizado Personalizado com Tutoria Virtual Assistida por IA! Descubra "Master Python Cybersecurity: Dos Fundamentos às Aplicações Avançadas com Tutoria Virtual IA", o guia essencial para profissionais e entusiastas que desejam dominar a automação e a cibersegurança com Python. Este manual inovador, escrito por Diego Rodrigues, um autor renomado com mais de 140 títulos publicados em seis idiomas, combina conteúdo de alta qualidade com a tecnologia avançada do IAGO, um tutor virtual desenvolvido e hospedado na plataforma OpenAI. Características Inovadoras: - Personalização do Aprendizado: O IAGO adapta o conteúdo de acordo com seu nível de conhecimento, oferecendo explicações detalhadas e exercícios personalizados. - Feedback Imediato: Receba correções e sugestões em tempo real, acelerando seu processo de aprendizado. - Interatividade e Engajamento: Interaja com o tutor via texto ou voz, tornando o estudo mais dinâmico e motivador. - Mentoria para Desenvolvimento de Projetos: Obtenha orientação prática para desenvolver e implementar projetos reais, aplicando o conhecimento adquirido. - Flexibilidade Total: Acesse o tutor em qualquer lugar e a qualquer momento, seja pelo desktop, notebook ou smartphone com acesso web. Aproveite o Valor Promocional de Lançamento por Tempo Limitado! Não perca a oportunidade de transformar sua jornada de aprendizado com um método inovador e eficaz. Este livro foi cuidadosamente estruturado para atender às suas necessidades e superar suas expectativas, garantindo que você esteja preparado para enfrentar os desafios e aproveitar as oportunidades na área de automação e cibersegurança. Abra a amostra do livro e descubra como acessar ao seleto clube dos profissionais de tecnologias de vanguarda. Aproveite essa oportunidade única e conquiste seus objetivos! TAGS hacking automação segurança cibernética Scapy Requests BeautifulSoup Nmap Metasploit hacking ético testes penetração análise forense vulnerabilidades segurança redes criptografia ataques cibernéticos proteção dados monitoramento rede auditoria segurança técnicas avançadas defesa cibernética segurança da informação segurança sistemas proteção invasões Diego Rodrigues CyberExtreme malware vírus phishing ataques DDoS inteligência artificial machine learning blockchain DevOps DevSecOps segurança SCADA indústria 4.0 saúde conectada cidades inteligentes análise vulnerabilidades segurança aplicações web SQL Injection XSS CSRF gerenciamento patches atualização software política senhas autenticação multifator MFA criptografia AES RSA ECC segurança cloud AWS Microsoft Azure Google Cloud IBM Cloud Palo Alto Networks Cisco Systems Check Point Symantec McAfee Splunk CrowdStrike Fortinet Tenable Nessus OpenVAS segurança Wi-Fi LTE 5G endpoints APIs osint criptografia repouso baseada risco gerenciamento risco análise logs monitoração contínua resposta a ameaças análise comportamento ferramentas segurança melhores práticas inovação transformação digital big data hack Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR GITHUB
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
Python For Data Analysis
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Author : Wes McKinney
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-09-25
Python For Data Analysis written by Wes McKinney 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 2017-09-25 with Computers categories.
Get complete instructions for manipulating, processing, cleaning, and crunching datasets in Python. Updated for Python 3.6, the second edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupyter in the process. Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub. Use the IPython shell and Jupyter notebook for exploratory computing Learn basic and advanced features in NumPy (Numerical Python) Get started with data analysis tools in the pandas library Use flexible tools to load, clean, transform, merge, and reshape data Create informative visualizations with matplotlib Apply the pandas groupby facility to slice, dice, and summarize datasets Analyze and manipulate regular and irregular time series data Learn how to solve real-world data analysis problems with thorough, detailed examples
Test Driven Development With Python
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Author : Harry Percival
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2017-08-02
Test Driven Development With Python written by Harry Percival 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 2017-08-02 with Computers categories.
By taking you through the development of a real web application from beginning to end, the second edition of this hands-on guide demonstrates the practical advantages of test-driven development (TDD) with Python. You’ll learn how to write and run tests before building each part of your app, and then develop the minimum amount of code required to pass those tests. The result? Clean code that works. In the process, you’ll learn the basics of Django, Selenium, Git, jQuery, and Mock, along with current web development techniques. If you’re ready to take your Python skills to the next level, this book—updated for Python 3.6—clearly demonstrates how TDD encourages simple designs and inspires confidence. Dive into the TDD workflow, including the unit test/code cycle and refactoring Use unit tests for classes and functions, and functional tests for user interactions within the browser Learn when and how to use mock objects, and the pros and cons of isolated vs. integrated tests Test and automate your deployments with a staging server Apply tests to the third-party plugins you integrate into your site Run tests automatically by using a Continuous Integration environment Use TDD to build a REST API with a front-end Ajax interface
Foundations Of Data Science
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Author : Avrim Blum
language : en
Publisher: Cambridge University Press
Release Date : 2020-01-23
Foundations Of Data Science written by Avrim Blum and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-01-23 with Computers categories.
Covers mathematical and algorithmic foundations of data science: machine learning, high-dimensional geometry, and analysis of large networks.
Introduction To Data Science
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Author : Rafael A. Irizarry
language : en
Publisher: CRC Press
Release Date : 2019-11-12
Introduction To Data Science written by Rafael A. Irizarry and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-12 with Mathematics categories.
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. A complete solutions manual is available to registered instructors who require the text for a course.
R For Data Science
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Author : Hadley Wickham
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2016-12-12
R For Data Science written by Hadley Wickham 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 2016-12-12 with Computers categories.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Artificial Intelligence With Python
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Author : Prateek Joshi
language : en
Publisher: Packt Publishing Ltd
Release Date : 2017-01-27
Artificial Intelligence With Python written by Prateek Joshi 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 2017-01-27 with Computers categories.
Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guide! Style and approach This highly practical book will show you how to implement Artificial Intelligence. The book provides multiple examples enabling you to create smart applications to meet the needs of your organization. In every chapter, we explain an algorithm, implement it, and then build a smart application.