Lokale, instruktorierte Live-Python-Trainingskurse demonstrieren durch praktisches Üben verschiedene Aspekte der Programmiersprache Python Einige der behandelten Themen umfassen die Grundlagen der Python-Programmierung, erweiterte Python-Programmierung, Python für Testautomatisierung, Python-Skripting und Automatisierung und Python für Datenanalyse und Big Data-Anwendungen in Bereichen wie Finanzen, Bankwesen und Versicherungen NobleProg Python Trainingskurse umfassen auch Anfänger- und Fortgeschrittenenkurse in der Verwendung von Python-Bibliotheken und Frameworks für Machine Learning und Deep Learning Python-Training ist als "Live-Training vor Ort" oder "Fern-Live-Training" verfügbar Onsite Live Training kann vor Ort bei Kunden durchgeführt werden Schweiz oder in NobleProg Corporate Trainingszentren in Schweiz Remote-Live-Training wird über einen interaktiven Remote-Desktop durchgeführt NobleProg Ihr lokaler Trainingsanbieter.
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Erfahrungsberichte
Ich bevorzuge die Übung und lerne etwas über die Ecken und Winkel von Python
Connor Brierley-Green
Kurs: Python Programming
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Joey hat eine ansteckende Begeisterung für das Programmieren. Und er war sehr gut darin, sich spontan auf unsere Bedürfnisse und Interessen einzustellen.
Randy Enkin
Kurs: Python Programming
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Viele Beispiele haben mich leicht verständlich gemacht.
Lingmin Cao
Kurs: Python Programming
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Die umfassende Kenntnis des Leitfadens zu all unseren Fragen gab Antworten, die meine Erwartungen übertrafen ... Der Dozent führt großartige Diskussionen ... Ihm fehlt es nicht an Geduld ...
Łukasz Matulewicz
Kurs: Programowanie w języku Python
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Große Kenntnisse des Dozenten, Vielfalt der Werkzeuge und praktische Herangehensweise an das Thema
Magdalena Stupak
Kurs: Programowanie w języku Python
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großes Wissen des Trainers, wie man übersetzt
Renata Cylejowska
Kurs: Programowanie w języku Python
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Tatsache, dass die Anpassung ernst genommen wurde.
jurgen linsen
Kurs: Python Programming
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Da ich der einzige Teilnehmer war, konnte das Training an meine Bedürfnisse angepasst werden.
Kevin THIERRY
Kurs: Web Development with Web2Py
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Ich mochte die Übungen.
Office for National Statistics
Kurs: Natural Language Processing with Python
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Ich mochte die hilfsbereiten und sehr nett.
Natalia Machrowicz
Kurs: Python Programming
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Wir haben praktische Übungen gemacht (die Skripte, die wir geschrieben haben, können in unserer täglichen Arbeit verwendet werden). Es machte den Kurs sehr interessant. Ich mochte auch, wie der Trainer sein Wissen teilte. Er hat es sehr zugänglich gemacht.
Malwina Sawa
Kurs: Python Programming
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Sehr guter Ansatz zum Einprägen / Wiederholen der wichtigsten Themen. Sehr schöne Aufwärmübungen.
Kurs: Python Programming
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* Angenehme Übungen. * Schnell in fortgeschrittenere Themen umgezogen. * Trainer war freundlich und leicht zu verstehen. * Maßgeschneiderter Kurs für die Bedürfnisse des Teams.
Matthew Lucas
Kurs: Python Programming
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Ich habe die Flexibilität genossen, bestimmte Themen in den Kurs / Unterricht aufzunehmen.
Marc Ammann
Kurs: Python Programming
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Alle mögen es
蒙 李
Kurs: Machine Learning Fundamentals with Python
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Der Trainer hat jede Person einzeln betrachtet und ihnen geholfen
Szymon Wolny
Kurs: Programowanie w języku Python
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Eine Reihe von Übungen, die ideal zum Thema passen. Übungen einfach und "mit einem Stern"
Motorola Solutions Systems Polska Sp. z o.o
Kurs: Programowanie w języku Python
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Gute Balance-Theorie / Übungen, Anpassung des Niveaus der Vorträge an weniger erfahrene Hörer, ein sehr großes Plus für die Verwendung von Jupiter Notebook und die praktische Umsetzung der Theorie. Ich sammelte auch gerne anonymes Feedback nach dem ersten Teil des Trainings am nächsten Tag wurde alles nach unseren Vorschlägen vorbereitet und obwohl es schon sehr gut war, dann war es noch besser :) Mot
Motorola Solutions Systems Polska Sp. z o.o
Kurs: Programowanie w języku Python
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Engagement des Lehrers, Vorbereitung, Annäherung an die Zuhörer, Bereitschaft, alle Unklarheiten zu erklären
Małgorzata Konior
Kurs: Programowanie w języku Python
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Dass der Leiter sich allen nähert, auch wenn er nicht um Hilfe ruft und das Niveau der Übung überprüft.
Agnieszka Bielak
Kurs: Programowanie w języku Python
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Der Trainer stellte sehr kurze Theorien zu einem bestimmten Thema vor und wir gingen sofort in die Praxis. Eine nette Art, Karten aufzuhängen, die dem Trainer Informationen darüber geben, wie viel Zeit er für eine bestimmte Aufgabe aufwenden muss und wer sonst Probleme mit der Lösung hat.
Motorola Solutions Systems Polska Sp. z o.o
Kurs: Programowanie w języku Python
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Dass, auch wenn jemand nicht darum gebeten hat, aber man merkt, dass er die Aufgabe nicht vorantreibt, kam Krzysztof und konnte geschickt beraten
Motorola Solutions Systems Polska Sp. z o.o
Kurs: Programowanie w języku Python
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Die Art zu leiten, die Übungen, alles in allem hat mir gefallen, ich bin sehr froh, dass ich zu solch einem Trainer gekommen bin
Maksym Kolodiy
Kurs: Programowanie w języku Python
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Echte Beispiele für Übungen
Motorola Solutions Systems Polska Sp. z o.o
Kurs: Programowanie w języku Python
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Verfügbarkeit von Schulungsmaterialien (Jupyter), die ständig aktualisiert werden, um das Notebook je nach den während des Kurses gestellten Fragen zu aktualisieren. Zweifel zerstreuen, Antworten auf alle Fragen.
Motorola Solutions Systems Polska Sp. z o.o
Kurs: Programowanie w języku Python
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Zugänglichkeit und eine interessante Art der Bereitstellung von Unterrichtsmaterialien.
Motorola Solutions Systems Polska Sp. z o.o
Kurs: Programowanie w języku Python
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Arbeite an xlsx- und csv-Dateien
Łukasz Olczyk
Kurs: Python: Automate the boring stuff
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Vertiefte Darstellung von Themen des maschinellen Lernens, insbesondere neuronaler Netze. Entmystifizierte viel von dem Thema.
Sacha Nandlall
Kurs: Python for Advanced Machine Learning
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Ich mochte die maßgeschneiderte, interne Dateiverarbeitung und Datenanalyse.
Glycom A/S
Kurs: Data Analysis in Python using Pandas and Numpy
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Die Fallstudien haben uns geholfen zu verstehen, wie wir Python in der Industrie anwenden können. Wirklich dankbar für die Hilfe des Trainers während der Übungen
Rajiv Dhingra - TCS
Kurs: Python Programming
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Da wir PHP Entwickler sind, hat er die Situation verstanden und uns erlaubt, die Dinge langsam aufeinander abzustimmen. Ich mochte die Beispiele und den Humor, den er hinzufügte.
Soumya Tyagi - TCS
Kurs: Python Programming
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Mir hat es gefallen, dass wir unsere eigenen Daten als Beispiele benutzt haben.
Glycom A/S
Kurs: Data Analysis in Python using Pandas and Numpy
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Alles
MTU Aero Engines Polska Sp. z o. o.
Kurs: Programowanie w języku Python
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Interessante Fragen
MTU Aero Engines Polska Sp. z o. o.
Kurs: Programowanie w języku Python
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Vielzahl von vorbereiteten Ausgaben und Beispielen
MTU Aero Engines Polska Sp. z o. o.
Kurs: Programowanie w języku Python
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das Training ist nicht präsentationsgestylt. Wir haben mit dem Trainer programmiert.
Bhutan Telecom
Kurs: Web Development with Django
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Ich habe das alles am meisten genossen.
Thukten Dendup - Bhutan Telecom
Kurs: Web Development with Django
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Es ist eine neue Erfahrung, ein neuer Rahmen und ich freue mich darauf, etwas zu tun, indem ich die in den Klassen gelernte Lektion benutze.
Jigme - Bhutan Telecom
Kurs: Web Development with Django
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Ich habe die vielen Labore und Praktiken wirklich genossen.
Vivian Feng - Destination Canada
Kurs: Data Analysis with SQL, Python and Spotfire
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Die Übungen / Labore waren auf unsere eigenen organisatorischen Bedürfnisse zugeschnitten.
Destination Canada
Kurs: Data Analysis with SQL, Python and Spotfire
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Das Thema hat mir generell gefallen.
Destination Canada
Kurs: Data Analysis with SQL, Python and Spotfire
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Der Trainer hat echte Worterfahrungen ausgetauscht, es ist schön, von echten Profis zu lernen.
Fednot
Kurs: Python Programming
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Der Trainer war ausgezeichnet. Er war immer bereit, meine Fragen zu beantworten und so viel Wissen zu teilen, wie er konnte.
Fahad Malalla - Tatweer Petroleum
Kurs: Advanced Python
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1: 1 sehr intensiv, aber viel gelernt.
Karen Dyke - BT
Kurs: Python: Automate the Boring Stuff
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Der Schwerpunkt auf Beispielen mit Codierung "auf dem Projektor" liegt definitiv auf + für Tom.
ADVA OPTICAL NETWORKING SP. ZO O.
Kurs: Advanced Python
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Mir hat das Thema meistens gefallen.
Proximus
Kurs: Python Programming
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Die Art und Weise, wie die Übungen organisiert wurden: Alle auf eigenem Tempo und Antonio dort, um Ihnen weiter zu helfen.
Proximus
Kurs: Python Programming
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Ich mochte die ausreichenden und sehr detaillierten Lesematerialien und Beispiele (Dias).
HC Consumer Finance Philippines, Inc.
Kurs: Python Programming
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Ich mochte die na wirklich.
HC Consumer Finance Philippines, Inc.
Kurs: Python Programming
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Was mir an der Ausbildung am meisten gefällt, ist, dass alles in der Kursskizze etwas ist, was für unsere Projekte nützlich sein wird.
Joanna Marie Escueta - Aarki, Inc.
Kurs: Python Programming
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Sehr guter Ansatz zum Einprägen / Wiederholen der wichtigsten Themen. Sehr schöne Aufwärmübungen.
Kurs: Python Programming
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Python Kurspläne
Name des Kurses
Dauer
Überblick
Name des Kurses
Dauer
Überblick
28 Stunden
Überblick
In diesem Kurs können Sie die Programmiersprache Python erlernen. Der Schwerpunkt des Kurses liegt dabei auf den Grundlagen der Sprache und zentralen Programmbibliotheken. Der Kurs besteht zur Hälfte aus Theorie, zur Hälfte aus praktischen Übungen. Er ist sowohl für Programmierer als auch Nichtprogrammierer geeignet.
28 Stunden
Überblick
Dieser Kurs richtet sich an:
- Wissenschaftler, die mit biologischen Daten arbeiten.
- Forscher, die Routineaufgaben automatisieren möchten.
- Biologen, die Ihre Arbeit mit einfachen Programmen verstärken möchten ohne gleich Vollzeitprogrammierer zu werden.
- Manager, die ein Grundverständnis für die Arbeit von Programmierern erlangen möchten.
Am Ende des Kurses werden die Teilnehmer in der Lage sein kurze Programme selbständig zu schreiben, um biologische Daten zu analysieren und zu manipulieren.
- Wissenschaftler, die mit biologischen Daten arbeiten.
- Forscher, die Routineaufgaben automatisieren möchten.
- Biologen, die Ihre Arbeit mit einfachen Programmen verstärken möchten ohne gleich Vollzeitprogrammierer zu werden.
- Manager, die ein Grundverständnis für die Arbeit von Programmierern erlangen möchten.
Am Ende des Kurses werden die Teilnehmer in der Lage sein kurze Programme selbständig zu schreiben, um biologische Daten zu analysieren und zu manipulieren.
14 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
14 Stunden
Überblick
In this instructor-led, live training in Schweiz 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 Stunden
Überblick
In this instructor-led, live training in Schweiz, 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 Stunden
Überblick
This instructor-led, live training in Schweiz 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 Stunden
Überblick
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 Stunden
Überblick
In this instructor-led, live training in Schweiz, 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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
In this instructor-led, live training in Schweiz, 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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
In this instructor-led, live training in Schweiz, 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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
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 Stunden
Überblick
In this instructor-led, live training in Schweiz (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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09-12-2021 09:30
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21 hours
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Python Schulung, Python boot camp, Python Abendkurse, Python Wochenendkurse, Python Kurs, Python Training, Python Seminar, Python Seminare, Python Privatkurs, Python Coaching, Python Lehrer
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