Os cursos de treinamento em Python ao vivo e treinados demonstram, através da prática prática, vários aspectos da linguagem de programação Python Alguns dos tópicos abordados incluem os fundamentos da programação Python, programação avançada de Python, Python para automação de testes, scripts Python e automação, e Python para aplicativos de Análise de Dados e Big Data em áreas como Finanças, Bancos e Seguros Os cursos de treinamento NobleProg em Python também abrangem cursos iniciantes e avançados sobre o uso de bibliotecas e estruturas em Python para Machine Learning e Deep Learning O treinamento em Python está disponível como "treinamento ao vivo no local" ou "treinamento remoto ao vivo" Treinamento ao vivo no local pode ser realizado localmente nas instalações do cliente em Portugal ou nos centros de treinamento corporativo da NobleProg em Portugal O treinamento ao vivo remoto é realizado por meio de uma área de trabalho remota e interativa NobleProg seu provedor de treinamento local.
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Declaração de Clientes
Eu preferi o exercício e aprender sobre os cantos e recantos de Python
Connor Brierley-Green
Curso: Python Programming
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Joey tem um entusiasmo contagiante sobre programação. E ele foi muito bom em se adaptar às nossas necessidades e interesses na hora.
Randy Enkin
Curso: Python Programming
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Muitos exemplos me facilitaram a entender.
Lingmin Cao
Curso: Python Programming
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O conhecimento abrangente do guia para todas as nossas perguntas deu respostas esmagadoras minhas expectativas ... O palestrante conduz grandes discussões ... Ele não falta paciência ...
Łukasz Matulewicz
Curso: Programowanie w języku Python
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Grande conhecimento do palestrante, uma variedade de ferramentas e uma abordagem prática do tema
Magdalena Stupak
Curso: Programowanie w języku Python
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grande conhecimento do treinador, como traduzir
Renata Cylejowska
Curso: Programowanie w języku Python
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Fato que personalização foi levada a sério.
jurgen linsen
Curso: Python Programming
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Como eu era o único participante, o treinamento poderia ser adaptado às minhas necessidades.
Kevin THIERRY
Curso: Web Development with Web2Py
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Eu gostei dos exercícios.
Office for National Statistics
Curso: Natural Language Processing with Python
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Eu gostei do útil e muito gentil.
Natalia Machrowicz
Curso: Python Programming
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Nós fizemos exercícios práticos (os roteiros que escrevemos podem ser usados em nosso trabalho diário). Isso tornou o curso muito interessante. Eu também gostei da maneira como o treinador compartilhou seu conhecimento. Ele fez isso de uma maneira muito acessível.
Malwina Sawa
Curso: Python Programming
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Muito boa abordagem para memorizar / repetir os tópicos principais. Muito bom "warm-up" exercícios.
Curso: Python Programming
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* Exercícios agradáveis. * Rapidamente mudou-se para tópicos mais avançados. * Trainer foi amigável e fácil de se levar. * Curso personalizado para necessidades de equipe.
Matthew Lucas
Curso: Python Programming
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Gostei da felixibilidade para adicionar tópicos específicos no curso / lições.
Marc Ammann
Curso: Python Programming
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Todos gostam
蒙 李
Curso: Machine Learning Fundamentals with Python
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o treinador olhou e ajudou cada pessoa individualmente
Szymon Wolny
Curso: Programowanie w języku Python
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Um conjunto de exercícios ideal para o assunto. Exercícios fáceis e "com uma estrela"
Motorola Solutions Systems Polska Sp. z o.o
Curso: Programowanie w języku Python
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Boa teoria / exercícios de equilíbrio, ajustando o nível de palestras para ouvintes cada vez mais experientes, uma grande vantagem para usar o Jupiter Notebook e mostrar a teoria na prática. Eu também gostei de coletar feedback anônimo após a primeira parte do treino no dia seguinte, tudo foi preparado de acordo com nossas sugestões e mesmo que já estivesse muito bom, então foi ainda melhor :) Motoro
Motorola Solutions Systems Polska Sp. z o.o
Curso: Programowanie w języku Python
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compromisso do professor, preparação, abordagem dos ouvintes, disposição para explicar todas as ambiguidades
Małgorzata Konior
Curso: Programowanie w języku Python
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Que o líder se aproxima de todos, mesmo quando ele não pede ajuda e verifica o nível do exercício.
Agnieszka Bielak
Curso: Programowanie w języku Python
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O treinador apresentou teorias muito curtas sobre um determinado problema e imediatamente fomos praticar. Uma ótima maneira de sair dos cartões, o que dá ao treinador informações sobre quanto tempo gastar em uma determinada tarefa e quem mais tem problemas com a solução. Mot
Motorola Solutions Systems Polska Sp. z o.o
Curso: Programowanie w języku Python
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Que mesmo que alguém não pedisse isso, mas você podia ver que ele não estava avançando com a tarefa, Krzysztof surgiu e foi capaz de aconselhar habilmente M
Motorola Solutions Systems Polska Sp. z o.o
Curso: Programowanie w języku Python
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A maneira de conduzir, os exercícios, em geral, tudo gostava, estou muito feliz por ter chegado a um tal formador
Maksym Kolodiy
Curso: Programowanie w języku Python
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Exemplos reais de exercícios
Motorola Solutions Systems Polska Sp. z o.o
Curso: Programowanie w języku Python
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Disponibilidade de material de treinamento (Jupyter), criado de forma contínua, atualizando o notebook, dependendo das perguntas que caíram durante o curso. Dispandindo dúvidas, responde a todas as perguntas.
Motorola Solutions Systems Polska Sp. z o.o
Curso: Programowanie w języku Python
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Acessibilidade e uma maneira interessante de entregar material didático.
Motorola Solutions Systems Polska Sp. z o.o
Curso: Programowanie w języku Python
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Trabalhe em arquivos xlsx e csv
Łukasz Olczyk
Curso: Python: Automate the boring stuff
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Abrangente cobertura de tópicos de aprendizado de máquina, particularmente redes neurais. Desmistificou muito do tópico.
Sacha Nandlall
Curso: Python for Advanced Machine Learning
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Gostei do processamento interno e análise de dados personalizados.
Glycom A/S
Curso: Data Analysis in Python using Pandas and Numpy
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Os estudos de caso nos ajudaram a entender como podemos aplicar o Python na indústria. Realmente apreciei a ajuda do treinador durante os exercícios
Rajiv Dhingra - TCS
Curso: Python Programming
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Como somos desenvolvedores de PHP , ele entendeu a situação e nos permitiu mapear as coisas lentamente. Gostei dos exemplos e do humor que ele acrescentou.
Soumya Tyagi - TCS
Curso: Python Programming
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Eu gostei do que usamos nossos próprios dados como exemplos.
Glycom A/S
Curso: Data Analysis in Python using Pandas and Numpy
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Tudo
MTU Aero Engines Polska Sp. z o. o.
Curso: Programowanie w języku Python
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Questões interessantes
MTU Aero Engines Polska Sp. z o. o.
Curso: Programowanie w języku Python
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Variedade de questões preparadas e exemplos
MTU Aero Engines Polska Sp. z o. o.
Curso: Programowanie w języku Python
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o treinamento não é apresentação estilizada. Nós estávamos codificando com ele treinador ..
Bhutan Telecom
Curso: Web Development with Django
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Eu principalmente gostei do tudo.
Thukten Dendup - Bhutan Telecom
Curso: Web Development with Django
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É uma nova experiência, um novo quadro e ansioso para fazer algo usando a lição aprendida nas aulas.
Jigme - Bhutan Telecom
Curso: Web Development with Django
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Eu realmente gostei dos muitos laboratórios e práticas.
Vivian Feng - Destination Canada
Curso: Data Analysis with SQL, Python and Spotfire
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Os exercícios / laboratórios foram adaptados às nossas próprias necessidades organizacionais.
Destination Canada
Curso: Data Analysis with SQL, Python and Spotfire
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Eu geralmente gostei do assunto.
Destination Canada
Curso: Data Analysis with SQL, Python and Spotfire
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O treinador estava compartilhando experiências reais, é bom aprender com um profissional de verdade.
Fednot
Curso: Python Programming
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O treinador foi excelente, Ele estava sempre pronto para responder minhas perguntas e compartilhar o máximo de conhecimento que pudesse.
Fahad Malalla - Tatweer Petroleum
Curso: Advanced Python
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1: 1 muito intensivo, mas aprendi muito.
Karen Dyke - BT
Curso: Python: Automate the Boring Stuff
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ênfase em exemplos com codificação "no projetor" é definitivamente em + para Tom.
ADVA OPTICAL NETWORKING SP. ZO O.
Curso: Advanced Python
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Eu apreciei principalmente o assunto.
Proximus
Curso: Python Programming
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A forma como os exercícios foram organizados: todos no próprio ritmo e Antonio lá para ajudá-lo ainda mais.
Proximus
Curso: Python Programming
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Eu gostei dos materiais de leitura suficientes e muito detalhados e exemplos (slides).
HC Consumer Finance Philippines, Inc.
Curso: Python Programming
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Eu realmente gostei do nd.
HC Consumer Finance Philippines, Inc.
Curso: Python Programming
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O que mais gosto no treinamento é que tudo no contorno do curso é algo que será útil para nossos projetos.
Joanna Marie Escueta - Aarki, Inc.
Curso: Python Programming
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Muito boa abordagem para memorizar / repetir os tópicos principais. Muito bom "warm-up" exercícios.
Curso: Python Programming
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Programa de curso Python
Nome do Curso
Duração
Visão geral
Nome do Curso
Duração
Visão geral
28 horas
Visão geral
Este curso foi concebido para aqueles que desejam aprender a linguagem de programação Python. A ênfase está na linguagem Python, nas bibliotecas principais, bem como na seleção das melhores e mais úteis bibliotecas desenvolvidas pela comunidade Python. Python dirige negócios e é usado por cientistas em todo o mundo - é uma das linguagens de programação mais populares.
O curso pode ser entregue usando Python 2.7.x ou 3.x, com exercícios práticos fazendo uso do poder total de ambas as versões do idioma. Este curso pode ser entregue em qualquer sistema operacional (todos os sabores do UNIX, incluindo Linux e Mac OS X, bem como o Microsoft Windows).
Os exercícios práticos constituem cerca de 70% do tempo do curso e cerca de 30% são demonstrações e apresentações. Podem ser feitas discussões e perguntas ao longo do curso.
Nota: o treinamento pode ser adaptado a necessidades específicas mediante solicitação prévia antes da data do curso proposto.
O curso pode ser entregue usando Python 2.7.x ou 3.x, com exercícios práticos fazendo uso do poder total de ambas as versões do idioma. Este curso pode ser entregue em qualquer sistema operacional (todos os sabores do UNIX, incluindo Linux e Mac OS X, bem como o Microsoft Windows).
Os exercícios práticos constituem cerca de 70% do tempo do curso e cerca de 30% são demonstrações e apresentações. Podem ser feitas discussões e perguntas ao longo do curso.
Nota: o treinamento pode ser adaptado a necessidades específicas mediante solicitação prévia antes da data do curso proposto.
28 horas
Visão geral
Este é um curso prático, que mostra por que a programação é uma ferramenta poderosa no contexto da solução de problemas biológicos. Durante o curso, os participantes aprenderão a Python programação Python , uma linguagem amplamente considerada tanto poderosa quanto fácil de usar. Este curso pode ser considerado uma demonstração de como a bioinformática melhora a vida dos biólogos.
O curso é projetado e direcionado para pessoas sem formação em ciência da computação que desejam aprender a programar.
Este curso é adequado para:
- Pesquisadores que lidam com dados biológicos.
- Cientistas que gostariam de aprender como automatizar tarefas diárias e analisar dados.
- Gerentes que desejam aprender como a programação melhora os fluxos de trabalho e a condução de projetos.
Ao final do curso, os participantes poderão escrever programas curtos, que permitirão manipular, analisar e lidar com dados biológicos e apresentar resultados em formato gráfico.
O curso é projetado e direcionado para pessoas sem formação em ciência da computação que desejam aprender a programar.
Este curso é adequado para:
- Pesquisadores que lidam com dados biológicos.
- Cientistas que gostariam de aprender como automatizar tarefas diárias e analisar dados.
- Gerentes que desejam aprender como a programação melhora os fluxos de trabalho e a condução de projetos.
Ao final do curso, os participantes poderão escrever programas curtos, que permitirão manipular, analisar e lidar com dados biológicos e apresentar resultados em formato gráfico.
14 horas
Visão geral
O objetivo deste curso é fornecer uma proficiência básica na aplicação de métodos de Machine Learning na prática. Através do uso da Python programação Python e de suas várias bibliotecas, e com base em vários exemplos práticos, este curso ensina como usar os blocos de construção mais importantes do Machine Learning , como tomar decisões de modelagem de dados, interpretar as saídas dos algoritmos e validar os resultados.
Nosso objetivo é fornecer a você as habilidades para entender e usar as ferramentas mais fundamentais da caixa de ferramentas Machine Learning confiança e evitar as armadilhas comuns dos aplicativos da Data Science .
Nosso objetivo é fornecer a você as habilidades para entender e usar as ferramentas mais fundamentais da caixa de ferramentas Machine Learning confiança e evitar as armadilhas comuns dos aplicativos da Data Science .
28 horas
Visão geral
Este curso introduz linguistas ou programadores à NLP em Python. Durante este curso nós utilizaremos principalmente nltk.org (Natural Language Tool Kit), mas também utilizaremos outras bibliotecas relevantes e convenientes para NLP. Atualmente podemos coduzir este curso em Python 2.x ou Python 3.x. Exemplos estão em Ingles ou Mandarin. Outros idiomas podem também ser disponibilizados previo acordo antecipado.
14 horas
Visão geral
Pandas é um pacote Python que fornece estruturas de dados para trabalhar com dados estruturados (tabulares, multidimensionais, potencialmente heterogêneos) e séries temporais.
14 horas
Visão geral
In this instructor-led, live training in Portugal participants combine the power of Python with Selenium to automate the testing of a sample web application. By combining theory with practice in a live lab environment, participants will gain the knowledge and practice needed to automate their own web testing projects using Python and Selenium.
28 horas
Visão geral
In this instructor-led, live training in Portugal, participants will learn advanced Python programming techniques, including how to apply this versatile language to solve problems in areas such as distributed applications, data analysis and visualization, UI programming and maintenance scripting.
14 horas
Visão geral
This instructor-led, live training in Portugal is based on the popular book, "Automate the Boring Stuff with Python", by Al Sweigart. It is aimed at beginners and covers essential Python programming concepts through practical, hands-on exercises and discussions. The focus is on learning to write code to dramatically increase office productivity.
By the end of this training, participants will know how to program in Python and apply this new skill for:
- Automating tasks by writing simple Python programs.
- Writing programs that can do text pattern recognition with "regular expressions".
- Programmatically generating and updating Excel spreadsheets.
- Parsing PDFs and Word documents.
- Crawling web sites and pulling information from online sources.
- Writing programs that send out email notifications.
- Use Python's debugging tools to quickly resolve bugs.
- Programmatically controlling the mouse and keyboard to click and type for you.
By the end of this training, participants will know how to program in Python and apply this new skill for:
- Automating tasks by writing simple Python programs.
- Writing programs that can do text pattern recognition with "regular expressions".
- Programmatically generating and updating Excel spreadsheets.
- Parsing PDFs and Word documents.
- Crawling web sites and pulling information from online sources.
- Writing programs that send out email notifications.
- Use Python's debugging tools to quickly resolve bugs.
- Programmatically controlling the mouse and keyboard to click and type for you.
28 horas
Visão geral
The aim of this course is to provide general proficiency in applying Machine Learning methods in practice. Through the use of the Python programming language and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.
21 horas
Visão geral
In this instructor-led, live training in Portugal, participants will learn the most relevant and cutting-edge machine learning techniques in Python as they build a series of demo applications involving image, music, text, and financial data.
By the end of this training, participants will be able to:
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
By the end of this training, participants will be able to:
- Implement machine learning algorithms and techniques for solving complex problems.
- Apply deep learning and semi-supervised learning to applications involving image, music, text, and financial data.
- Push Python algorithms to their maximum potential.
- Use libraries and packages such as NumPy and Theano.
21 horas
Visão geral
In this instructor-led, live training, participants will learn how to use the right machine learning and NLP (Natural Language Processing) techniques to extract value from text-based data.
By the end of this training, participants will be able to:
- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Solve text-based data science problems with high-quality, reusable code
- Apply different aspects of scikit-learn (classification, clustering, regression, dimensionality reduction) to solve problems
- Build effective machine learning models using text-based data
- Create a dataset and extract features from unstructured text
- Visualize data with Matplotlib
- Build and evaluate models to gain insight
- Troubleshoot text encoding errors
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
Natural language generation (NLG) refers to the production of natural language text or speech by a computer.
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
- Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
- Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
- Understand the NLG pipeline and apply the right techniques at each stage
- Understand the architecture of a Natural Language Generation (NLG) system
- Implement the most suitable algorithms and models for analysis and ordering
- Pull data from publicly available data sources as well as curated databases to use as material for generated text
- Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
In this instructor-led, live training in Portugal, participants will learn how to use PyTest to write short, maintainable tests that are elegant, expressive and readable.
By the end of this training, participants will be able to:
- Write readable and maintainable tests without the need for boilerplate code.
- Use the fixture model to write small tests.
- Scale tests up to complex functional testing for applications, packages, and libraries.
- Understand and apply PyTest features such as hooks, assert rewriting and plug-ins.
- Reduce test times by running tests in parallel and across multiple processors.
- Run tests in a continuous integration environment, together with other utilities such as tox, mock, coverage, unittest, doctest and Selenium.
- Use Python to test non-Python applications.
By the end of this training, participants will be able to:
- Write readable and maintainable tests without the need for boilerplate code.
- Use the fixture model to write small tests.
- Scale tests up to complex functional testing for applications, packages, and libraries.
- Understand and apply PyTest features such as hooks, assert rewriting and plug-ins.
- Reduce test times by running tests in parallel and across multiple processors.
- Run tests in a continuous integration environment, together with other utilities such as tox, mock, coverage, unittest, doctest and Selenium.
- Use Python to test non-Python applications.
35 horas
Visão geral
By the end of the training the delegates are expected to be sufficiently equipped with the essential python concepts and should be able to sufficiently use NLTK to implement most of the NLP and ML based operations. The training is aimed at giving not just an executional knowledge but also the logical and operational knowledge of the technology therein.
21 horas
Visão geral
Machine Learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the banking industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
35 horas
Visão geral
Python is a programming language that has gained huge popularity in the financial industry. Adopted by the largest investment banks and hedge funds, it is being used to build a wide range of financial applications ranging from core trading programs to risk management systems.
In this instructor-led, live training, participants will learn how to use Python to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
- Understand the fundamentals of the Python programming language
- Download, install and maintain the best development tools for creating financial applications in Python
- Select and utilize the most suitable Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate, deploy, and optimize a Python application
Audience
- Developers
- Analysts
- Quants
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
In this instructor-led, live training, participants will learn how to use Python to develop practical applications for solving a number of specific finance related problems.
By the end of this training, participants will be able to:
- Understand the fundamentals of the Python programming language
- Download, install and maintain the best development tools for creating financial applications in Python
- Select and utilize the most suitable Python packages and programming techniques to organize, visualize, and analyze financial data from various sources (CSV, Excel, databases, web, etc.)
- Build applications that solve problems related to asset allocation, risk analysis, investment performance and more
- Troubleshoot, integrate, deploy, and optimize a Python application
Audience
- Developers
- Analysts
- Quants
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- This training aims to provide solutions for some of the principle problems faced by finance professionals. However, if you have a particular topic, tool or technique that you wish to append or elaborate further on, please please contact us to arrange.
14 horas
Visão geral
In Python Machine Learning, the Text Summarization feature is able to read the input text and produce a text summary. This capability is available from the command-line or as a Python API/Library. One exciting application is the rapid creation of executive summaries; this is particularly useful for organizations that need to review large bodies of text data before generating reports and presentations.
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
- Use a command-line tool that summarizes text.
- Design and create Text Summarization code using Python libraries.
- Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn to use Python to create a simple application that auto-generates a summary of input text.
By the end of this training, participants will be able to:
- Use a command-line tool that summarizes text.
- Design and create Text Summarization code using Python libraries.
- Evaluate three Python summarization libraries: sumy 0.7.0, pysummarization 1.0.4, readless 1.0.17
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 horas
Visão geral
Object-Oriented Programming (OOP) is a programming paradigm based around the concept of objects. OOP is more data-focused rather than logic-focused. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn how to get started with Object-Oriented Programming using Python.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of Object-Oriented Programming
- Understand the OOP syntax in Python
- Write their own object-oriented program in Python
Audience
- Beginners who would like to learn about Object-Oriented Programming
- Developers interested in learning OOP in Python
- Python programmers interested in learning OOP
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to get started with Object-Oriented Programming using Python.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of Object-Oriented Programming
- Understand the OOP syntax in Python
- Write their own object-oriented program in Python
Audience
- Beginners who would like to learn about Object-Oriented Programming
- Developers interested in learning OOP in Python
- Python programmers interested in learning OOP
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications.
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry.
Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects.
By the end of this training, participants will be able to:
- Understand the fundamental concepts in machine learning
- Learn the applications and uses of machine learning in finance
- Develop their own algorithmic trading strategy using machine learning with Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
Python é uma linguagem de programação de alto nível famosa por sua sintaxe clara e com legibilidade de código. O Spark é um mecanismo de processamento de dados usado na consulta, análise e transformação de big data. O PySpark permite que os usuários façam interface com o Spark com o Python.
Neste treinamento os participantes aprenderão como usar o Python e o Spark juntos para analisar big data enquanto trabalham em exercícios práticos.
No final deste treinamento, os participantes serão capazes de:
- Aprender a usar o Spark com Python para analisar Big Data
- Trabalhar em exercícios que imitam as circunstâncias do mundo real
- Usar diferentes ferramentas e técnicas para análise de big data usando o PySpark
Público
Desenvolvedores
Profissionais de TI
Cientistas de dados
Formato do curso
Palestra, discussão, exercícios e prática
Neste treinamento os participantes aprenderão como usar o Python e o Spark juntos para analisar big data enquanto trabalham em exercícios práticos.
No final deste treinamento, os participantes serão capazes de:
- Aprender a usar o Spark com Python para analisar Big Data
- Trabalhar em exercícios que imitam as circunstâncias do mundo real
- Usar diferentes ferramentas e técnicas para análise de big data usando o PySpark
Público
Desenvolvedores
Profissionais de TI
Cientistas de dados
Formato do curso
Palestra, discussão, exercícios e prática
35 horas
Visão geral
Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn how to use Python for quantitative finance.
By the end of this training, participants will be able to:
- Understand the fundamentals of Python programming
- Use Python for financial applications including implementing mathematical techniques, stochastics, and statistics
- Implement financial algorithms using performance Python
Audience
- Developers
- Quantitative analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use Python for quantitative finance.
By the end of this training, participants will be able to:
- Understand the fundamentals of Python programming
- Use Python for financial applications including implementing mathematical techniques, stochastics, and statistics
- Implement financial algorithms using performance Python
Audience
- Developers
- Quantitative analysts
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 horas
Visão geral
Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility.
In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.
By the end of this training, participants will be able to:
- Understand the basics of Computer Vision
- Use Python to implement Computer Vision tasks
- Build their own face, object, and motion detection systems
Audience
- Python programmers interested in Computer Vision
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python.
By the end of this training, participants will be able to:
- Understand the basics of Computer Vision
- Use Python to implement Computer Vision tasks
- Build their own face, object, and motion detection systems
Audience
- Python programmers interested in Computer Vision
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 horas
Visão geral
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in banking
- Use Python, Keras, and TensorFlow to create deep learning models for banking
- Build their own deep learning credit risk model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
28 horas
Visão geral
Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability.
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use Python, Keras, and TensorFlow to create deep learning models for finance
- Build their own deep learning stock price prediction model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model.
By the end of this training, participants will be able to:
- Understand the fundamental concepts of deep learning
- Learn the applications and uses of deep learning in finance
- Use Python, Keras, and TensorFlow to create deep learning models for finance
- Build their own deep learning stock price prediction model using Python
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7 horas
Visão geral
Microservices refer to an application architecture style that promotes the use of independent, self-contained programs. Python is a dynamic high-level programming language that is ideal for both scripting as welll as application development. Python's expansive library of open source tools and frameworks make it a practical choice for building microservices.
In this instructor-led, live training, participants will learn the fundamentals of microservices as they step through the creation of a microservice using Python.
By the end of this training, participants will be able to:
- Understand the basics of building microservices
- Learn how to use Python to build microservices
- Learn how to use Docker to deploy Python based microservices
Audience
- Developers
- Programmers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of microservices as they step through the creation of a microservice using Python.
By the end of this training, participants will be able to:
- Understand the basics of building microservices
- Learn how to use Python to build microservices
- Learn how to use Docker to deploy Python based microservices
Audience
- Developers
- Programmers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics.
The use of Python with GIS has substantially increased over the last two decades, particularly with the introduction of Python 2.0 series in 2000, which included many new programming features that made the language much easier to deploy. Since that time, Python has not only been utilized within commercial GIS such as products by Esri but also open source platforms, including as part of QGIS and GRASS. In fact, Python today is by far the most widely used language by GIS users and programmers.
This program covers the usage of Python and its advance libraries like geopandas, pysal, bokeh and osmnx to implement your own GIS features. The program also covers introductory modules around ArcGIS API, and QGIS toolboox.
The use of Python with GIS has substantially increased over the last two decades, particularly with the introduction of Python 2.0 series in 2000, which included many new programming features that made the language much easier to deploy. Since that time, Python has not only been utilized within commercial GIS such as products by Esri but also open source platforms, including as part of QGIS and GRASS. In fact, Python today is by far the most widely used language by GIS users and programmers.
This program covers the usage of Python and its advance libraries like geopandas, pysal, bokeh and osmnx to implement your own GIS features. The program also covers introductory modules around ArcGIS API, and QGIS toolboox.
14 horas
Visão geral
Tableau is a business intelligence and data visualization tool. Python is a widely used programming language which provides support for a wide variety of statistical and machine learning techniques. Tableau's data visualization power and Python's machine learning capabilities, when combined, help developers rapidly build advanced data analytics applications for various business use cases.
In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API.
By the end of this training, participants will be able to:
- Integrate Tableau and Python using TabPy API
- Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API.
By the end of this training, participants will be able to:
- Integrate Tableau and Python using TabPy API
- Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
21 horas
Visão geral
Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches.
In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.
By the end of this training, participants will be able to:
- Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning
- Apply advanced Reinforcement Learning algorithms to solve real-world problems
- Build a Deep Learning Agent
Audience
- Developers
- Data Scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 horas
Visão geral
Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. Python is a high-level programming language recommended for IoT due to its clear syntax and large community support.
In this instructor-led, live training, participants will learn how to program IoT solutions with Python.
By the end of this training, participants will be able to:
- Understand the fundamentals of IoT architecture
- Learn the basics of using Raspberry Pi
- Install and configure Python on Raspberry Pi
- Learn the benefits of using Python in programming IoT systems
- Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi
Audience
- Developers
- Engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
In this instructor-led, live training, participants will learn how to program IoT solutions with Python.
By the end of this training, participants will be able to:
- Understand the fundamentals of IoT architecture
- Learn the basics of using Raspberry Pi
- Install and configure Python on Raspberry Pi
- Learn the benefits of using Python in programming IoT systems
- Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi
Audience
- Developers
- Engineers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Note
- To request a customized training for this course, please contact us to arrange.
14 horas
Visão geral
In this instructor-led, live training in Portugal (onsite or remote), participants will learn how to combine the capabilities of Python and Excel.
By the end of this training, participants will be able to:
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
By the end of this training, participants will be able to:
- Install and configure packages for integrating Python and Excel.
- Read, write, and manipulate Excel files using Python.
- Call Python functions from Excel.
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