I corsi di formazione Python in diretta locale e istruttori dimostrano attraverso handson vari aspetti del linguaggio di programmazione Python Alcuni degli argomenti trattati includono i fondamenti della programmazione Python, la programmazione Python avanzata, Python per l'automazione dei test, lo scripting e l'automazione Python e Python per l'analisi dei dati e le applicazioni Big Data in settori come Finanza, Banche e Assicurazioni I corsi di formazione NobleProg Python coprono anche corsi iniziali e avanzati sull'uso delle librerie e dei framework Python per Machine Learning e Deep Learning L'addestramento di Python è disponibile come "allenamento dal vivo in loco" o "allenamento dal vivo a distanza" La formazione on-site in loco può essere svolta localmente presso la sede del cliente in Italia o nei centri di formazione aziendale NobleProg in Italia La formazione in remoto dal vivo viene effettuata tramite un desktop remoto interattivo NobleProg Il tuo fornitore di formazione locale.
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Recensioni
Ho preferito l'esercizio e conoscere gli angoli e le fessure di Python
Connor Brierley-Green
Corso: Python Programming
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Joey ha un entusiasmo contagioso sulla programmazione. Ed è stato molto bravo ad adattarsi al meglio ai nostri bisogni e interessi.
Randy Enkin
Corso: Python Programming
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Molti esempi mi hanno reso facile da capire.
Lingmin Cao
Corso: Python Programming
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La conoscenza completa della guida a tutte le nostre domande ha dato risposte travolgenti le mie aspettative ... Il docente conduce grandi discussioni ... Non gli manca la pazienza ...
Łukasz Matulewicz
Corso: Programowanie w języku Python
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Ottima conoscenza del docente, diversità di strumenti e approccio pratico all'argomento
Magdalena Stupak
Corso: Programowanie w języku Python
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grande conoscenza del formatore, come tradurre
Renata Cylejowska
Corso: Programowanie w języku Python
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Fatto che la personalizzazione è stata presa sul serio.
jurgen linsen
Corso: Python Programming
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Poiché ero l'unico partecipante, la formazione poteva essere adattata alle mie esigenze.
Kevin THIERRY
Corso: Web Development with Web2Py
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Mi sono piaciuti gli esercizi.
Office for National Statistics
Corso: Natural Language Processing with Python
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Mi è piaciuto l'utile e molto gentile.
Natalia Machrowicz
Corso: Python Programming
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Abbiamo fatto esercizi pratici (le sceneggiature che abbiamo scritto possono essere utilizzate nel nostro lavoro quotidiano). Ha reso il corso molto interessante. Mi è piaciuto anche il modo in cui l'allenatore ha condiviso le sue conoscenze. Lo ha fatto in un modo molto accessibile.
Malwina Sawa
Corso: Python Programming
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Ottimo approccio per memorizzare / ripetere gli argomenti chiave. Esercizi di "riscaldamento" molto belli.
Corso: Python Programming
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* Esercizi piacevoli. * Passato rapidamente a argomenti più avanzati. * Il trainer è stato cordiale e facile da gestire. * Corso personalizzato per esigenze di squadra.
Matthew Lucas
Corso: Python Programming
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Mi è piaciuta la possibilità di aggiungere argomenti specifici nel corso / lezioni.
Marc Ammann
Corso: Python Programming
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Tutto come
蒙 李
Corso: Machine Learning Fundamentals with Python
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l'allenatore ha guardato e aiutato ciascuna persona individualmente
Szymon Wolny
Corso: Programowanie w języku Python
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Una serie di esercizi ideali per il soggetto. Esercizi facili e "con una stella"
Motorola Solutions Systems Polska Sp. z o.o
Corso: Programowanie w języku Python
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Buona teoria / esercizi di equilibrio, adeguando il livello delle lezioni agli ascoltatori meno esperti e più esperti, un grande vantaggio per l'utilizzo di Quaderno di Giove e visualizzazione pratica della teoria. Mi è anche piaciuto raccogliere feedback anonimi dopo la prima parte dell'allenamento il giorno dopo tutto è stato preparato secondo i nostri suggerimenti e anche se era già molto buono, allora era ancora meglio :)
Motorola Solutions Systems Polska Sp. z o.o
Corso: Programowanie w języku Python
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impegno dell'insegnante, preparazione, approccio agli ascoltatori, disponibilità a spiegare tutte le ambiguità
Małgorzata Konior
Corso: Programowanie w języku Python
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Che il leader si avvicini a tutti, anche quando non richiede aiuto e controlla il livello dell'esercizio.
Agnieszka Bielak
Corso: Programowanie w języku Python
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Il formatore ha presentato una brevissima teoria su un dato problema e siamo subito andati a fare pratica. Un bel modo di appendere le carte, che fornisce all'istruttore informazioni su quanto tempo deve dedicare a un dato compito e su chi ha problemi con la soluzione.
Motorola Solutions Systems Polska Sp. z o.o
Corso: Programowanie w języku Python
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Che anche se qualcuno non lo chiedesse, ma si vedeva che non stava andando avanti con il compito, Krzysztof si avvicinò e fu in grado di consigliare abilmente
Motorola Solutions Systems Polska Sp. z o.o
Corso: Programowanie w języku Python
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Il modo di condurre, gli esercizi, tutto sommato mi è piaciuto, sono molto contento di essere venuto da un allenatore del genere
Maksym Kolodiy
Corso: Programowanie w języku Python
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Veri esempi di esercizi
Motorola Solutions Systems Polska Sp. z o.o
Corso: Programowanie w języku Python
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Disponibilità di materiali di formazione (Jupyter), creato su base continuativa aggiornando il notebook a seconda delle domande che sono cadute durante il corso. Dispelling dubbi, risposte a tutte le domande.
Motorola Solutions Systems Polska Sp. z o.o
Corso: Programowanie w języku Python
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Accessibilità e un modo interessante di fornire materiali didattici.
Motorola Solutions Systems Polska Sp. z o.o
Corso: Programowanie w języku Python
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Lavora su file xlsx e csv
Łukasz Olczyk
Corso: Python: Automate the boring stuff
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Approfondita trattazione di argomenti di apprendimento automatico, in particolare reti neurali. Demistificato molto dell'argomento.
Sacha Nandlall
Corso: Python for Advanced Machine Learning
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Mi è piaciuta l'elaborazione dei file personalizzata e in-house e l'analisi dei dati.
Glycom A/S
Corso: Data Analysis in Python using Pandas and Numpy
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I case study ci hanno aiutato a capire come applicare Python nel settore. Ho davvero apprezzato l'aiuto del trainer durante gli esercizi
Rajiv Dhingra - TCS
Corso: Python Programming
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Dato che siamo sviluppatori PHP , ha capito la situazione e ci ha permesso di mappare lentamente le cose tra di loro. Mi sono piaciuti gli esempi e l'umorismo che ha aggiunto.
Soumya Tyagi - TCS
Corso: Python Programming
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Mi è piaciuto il fatto che abbiamo usato i nostri dati come esempi.
Glycom A/S
Corso: Data Analysis in Python using Pandas and Numpy
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Tutto
MTU Aero Engines Polska Sp. z o. o.
Corso: Programowanie w języku Python
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Questioni interessanti
MTU Aero Engines Polska Sp. z o. o.
Corso: Programowanie w języku Python
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Varietà di problemi ed esempi preparati
MTU Aero Engines Polska Sp. z o. o.
Corso: Programowanie w języku Python
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la formazione non è in stile presentazione. Stavamo codificando con l'allenatore ...
Bhutan Telecom
Corso: Web Development with Django
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Mi è piaciuto soprattutto il tutto.
Thukten Dendup - Bhutan Telecom
Corso: Web Development with Django
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È una nuova esperienza, una nuova struttura e non vedo l'ora di fare qualcosa usando la lezione appresa nelle lezioni ..
Jigme - Bhutan Telecom
Corso: Web Development with Django
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Ho davvero apprezzato i numerosi laboratori e le pratiche.
Vivian Feng - Destination Canada
Corso: Data Analysis with SQL, Python and Spotfire
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Gli esercizi / laboratori sono stati adattati alle nostre esigenze organizzative.
Destination Canada
Corso: Data Analysis with SQL, Python and Spotfire
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Mi è generalmente piaciuto l'argomento.
Destination Canada
Corso: Data Analysis with SQL, Python and Spotfire
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Il trainer stava condividendo esperienze di parole reali, è bello imparare da veri professionisti.
Fednot
Corso: Python Programming
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L'allenatore era eccellente, era sempre pronto a rispondere alle mie domande e condividere quante più conoscenze possibili.
Fahad Malalla - Tatweer Petroleum
Corso: Advanced Python
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1: 1 molto intenso ma ho imparato molto.
Karen Dyke - BT
Corso: Python: Automate the Boring Stuff
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enfasi sugli esempi con la codifica "sul proiettore" è decisamente su + per Tom.
ADVA OPTICAL NETWORKING SP. ZO O.
Corso: Advanced Python
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Ho apprezzato molto l'argomento.
Proximus
Corso: Python Programming
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Il modo in cui gli esercizi sono stati organizzati: tutto sul proprio tempo e Antonio lì per aiutarvi ulteriormente.
Proximus
Corso: Python Programming
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Mi è piaciuto il materiale di lettura sufficiente e molto dettagliato ed esempi (diapositive).
HC Consumer Finance Philippines, Inc.
Corso: Python Programming
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Mi è veramente piaciuto il na.
HC Consumer Finance Philippines, Inc.
Corso: Python Programming
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Quello che mi piace di più della formazione è che tutto nel contorno del corso è qualcosa che sarà utile per i nostri progetti.
Joanna Marie Escueta - Aarki, Inc.
Corso: Python Programming
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Ottimo approccio per memorizzare / ripetere gli argomenti chiave. Esercizi di "riscaldamento" molto belli.
Corso: Python Programming
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Schema generale del corso Python
Nome del corso
Durata
Overview
Nome del corso
Durata
Overview
28 hours
Overview
This course is designed for those wishing to learn the Python programming language. The emphasis is on the Python language, the core libraries, as well as on the selection of the best and most useful libraries developed by the Python community. Python drives businesses and is used by scientists all over the world – it is one of the most popular programming languages.
The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).
The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.
Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.
The course can be delivered using the latest Python version 3.x with practical exercises making use of the full power. This course can be delivered on any operating system (all flavours of UNIX, including Linux and Mac OS X, as well as Microsoft Windows).
The practical exercises constitute about 70% of the course time, and around 30% are demonstrations and presentations. Discussions and questions can be asked throughout the course.
Note: the training can be tailored to specific needs upon prior request ahead of the proposed course date.
28 hours
Overview
This is a practical course, which shows why programming is a powerful tool in the context of solving biological problems. During the course participants will be taught the Python programming language, a language widely considered both powerful as well as easy to use. This course might be considered as a demonstration how bioinformatics improves biologists lives.
The course is designed and aimed for people without computer science background who want to learn to program.
This course is suited for:
- Researchers dealing with biological data.
- Scientists who would like to learn how to automate everyday tasks and analyse data.
- Managers who want to learn how programming improves workflows and conducting projects.
By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format.
The course is designed and aimed for people without computer science background who want to learn to program.
This course is suited for:
- Researchers dealing with biological data.
- Scientists who would like to learn how to automate everyday tasks and analyse data.
- Managers who want to learn how programming improves workflows and conducting projects.
By the end of the course, participants will be able to write short programs, which will allow them to manipulate, analyse and deal with biological data and present results in a graphical format.
14 hours
Overview
The aim of this course is to provide a basic 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.
28 hours
Overview
This course introduces linguists or programmers to NLP in Python. During this course we will mostly use nltk.org (Natural Language Tool Kit), but also we will use other libraries relevant and useful for NLP. At the moment we can conduct this course in Python 2.x or Python 3.x. Examples are in English or Mandarin (普通话). Other languages can be also made available if agreed before booking.
14 hours
Overview
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
14 hours
Overview
In this instructor-led, live training in Italia 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 hours
Overview
In questo formazione dal vivo con istruttore, i partecipanti impareranno le tecniche avanzate di programmazione Python, tra cui come applicare questo linguaggio versatile per risolvere i problemi in aree come le applicazioni distribuite, l'analisi e la visualizzazione dei dati, la programmazione dell'interfaccia utente e script di manutenzione.
Formato del corso
- Lezione interattiva e discussione.
- Un sacco di esercizi e pratica.
- Implementazione pratica in un ambiente live-lab.
Opzioni di personalizzazione del corso
- Se desideri aggiungere, rimuovere o personalizzare qualsiasi sezione o argomento all'interno di questo corso, contattaci per organizzare.
Formato del corso
- Lezione interattiva e discussione.
- Un sacco di esercizi e pratica.
- Implementazione pratica in un ambiente live-lab.
Opzioni di personalizzazione del corso
- Se desideri aggiungere, rimuovere o personalizzare qualsiasi sezione o argomento all'interno di questo corso, contattaci per organizzare.
14 hours
Overview
Questa formazione dal vivo con istruttore si basa sul popolare libro "Automate the Boring Stuff with Python ", di Al Sweigart. È rivolto ai principianti e copre concetti di programmazione Python essenziali attraverso esercitazioni pratiche e discussioni pratiche. L'obiettivo è imparare a scrivere codice per aumentare notevolmente la produttività dell'ufficio.
Al termine di questa formazione, i partecipanti sapranno programmare in Python e applicare questa nuova abilità per:
- Automatizzare le attività scrivendo semplici programmi Python .
- Scrivere programmi in grado di riconoscere il modello di testo con "espressioni regolari".
- Generazione e aggiornamento programmatico di Excel calcolo Excel .
- Analisi di PDF e documenti Word .
- Scansione di siti Web e acquisizione di informazioni da fonti online.
- Scrittura di programmi che inviano notifiche e-mail.
- Utilizzare gli strumenti di debug di Python per risolvere rapidamente i bug.
- Controllo programmatico del mouse e della tastiera per fare clic e digitare per te.
Formato del corso
- Parte lezione, parte discussione, esercitazioni e esercitazioni pratiche
Al termine di questa formazione, i partecipanti sapranno programmare in Python e applicare questa nuova abilità per:
- Automatizzare le attività scrivendo semplici programmi Python .
- Scrivere programmi in grado di riconoscere il modello di testo con "espressioni regolari".
- Generazione e aggiornamento programmatico di Excel calcolo Excel .
- Analisi di PDF e documenti Word .
- Scansione di siti Web e acquisizione di informazioni da fonti online.
- Scrittura di programmi che inviano notifiche e-mail.
- Utilizzare gli strumenti di debug di Python per risolvere rapidamente i bug.
- Controllo programmatico del mouse e della tastiera per fare clic e digitare per te.
Formato del corso
- Parte lezione, parte discussione, esercitazioni e esercitazioni pratiche
28 hours
Overview
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 hours
Overview
In this instructor-led, live training in Italia, 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 hours
Overview
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 hours
Overview
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 hours
Overview
In this instructor-led, live training in Italia, 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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
In this instructor-led, live training in Italia, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.
By the end of this training, participants will be able to:
- Learn how to use Spark with Python to analyze Big Data.
- Work on exercises that mimic real world cases.
- Use different tools and techniques for big data analysis using PySpark.
By the end of this training, participants will be able to:
- Learn how to use Spark with Python to analyze Big Data.
- Work on exercises that mimic real world cases.
- Use different tools and techniques for big data analysis using PySpark.
35 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
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 hours
Overview
In this instructor-led, live training in Italia (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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