A helyi, oktatott élő Python tanfolyamok a handson gyakorlat segítségével demonstrálják a Python programnyelvének különböző aspektusait A témák között szerepelnek a Python programozás alapjai, a fejlett Python programozás, Python a tesztautomatizáláshoz, a Python scripting és az automatizálás, valamint a Python for Data Analysis és Big Data alkalmazások olyan területeken, mint a pénzügy, bankügyletek és biztosítás A NobleProg Python tanfolyamok kiterjednek a Python könyvtárak és a gépi tanulás és a mély tanulás keretrendszerének kezdeti és továbbképzésére is A Python képzés elérhető "helyszíni élő képzésként" vagy "távoli élő képzésként" A helyszíni élő képzés helyi szinten valósulhat meg az ügyfél telephelyén Magyarország vagy a NobleProg vállalati oktatóközpontjaiban Magyarország A távoli élőképzés interaktív, távoli asztal segítségével történik NobleProg Az Ön Helyi Képzési Szolgáltatója.
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Vélemények
Inkább a testmozgást és a Python zümmögéseinek és áttöréseinek megismerését részesítettem előnyben
Connor Brierley-Green
Kurzus: Python Programming
Machine Translated
Joey fertőző lelkesedéssel rendelkezik a programozással kapcsolatban. És nagyon jól tudta igazítani igényeinket és érdekeinket.
Randy Enkin
Kurzus: Python Programming
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Számos példa könnyű megérteni.
Lingmin Cao
Kurzus: Python Programming
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A kérdésekre vonatkozó útmutató átfogó ismerete mindenre kiterjedő választ adott a várakozásaimnak ... Az előadó nagy vitákat folytat ... Nem hiányzik a türelme ... Matul
Łukasz Matulewicz
Kurzus: Programowanie w języku Python
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A tanár nagy tudása, az eszközök sokfélesége és a téma gyakorlati megközelítése Magdale
Magdalena Stupak
Kurzus: Programowanie w języku Python
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a tréner nagyszerű ismerete, a fordítás
Renata Cylejowska
Kurzus: Programowanie w języku Python
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Tény, hogy a testreszabást komolyan vették.
jurgen linsen
Kurzus: Python Programming
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Mivel én voltam az egyetlen résztvevő, a képzés az igényeimhez igazodhatott.
Kevin THIERRY
Kurzus: Web Development with Web2Py
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Tetszett a gyakorlatok.
Office for National Statistics
Kurzus: Natural Language Processing with Python
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Tetszett a segítőkész és nagyon kedves.
Natalia Machrowicz
Kurzus: Python Programming
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Gyakorlati gyakorlatokat végeztünk (az általunk írt szkripteket mindennapi munkánkban felhasználhatjuk). Nagyon érdekes volt a tanfolyam. Szerettem úgy is, ahogy az edző megosztotta tudását. Nagyon könnyen elérhető módon csinálta.
Malwina Sawa
Kurzus: Python Programming
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Nagyon jó megközelítés a legfontosabb témák memorizálására / megismételésére. Nagyon szép "bemelegítő" gyakorlatok.
Kurzus: Python Programming
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* Élvezetes gyakorlatok. * Gyorsan bekerült a fejlettebb témákba. * A tréner barátságos és könnyen kezelhető. * Testre szabott kurzus a csapat igényeihez.
Matthew Lucas
Kurzus: Python Programming
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Éreztem a felkészülést arra, hogy konkrét témákat vegyek fel a kurzusba.
Marc Ammann
Kurzus: Python Programming
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Mindenki tetszik
蒙 李
Kurzus: Machine Learning Fundamentals with Python
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az edző minden egyes személyre nézett és segített S
Szymon Wolny
Kurzus: Programowanie w języku Python
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A téma számára ideális megoldások. Gyakorlatok egyszerűek és "csillaggal" Mot
Motorola Solutions Systems Polska Sp. z o.o
Kurzus: Programowanie w języku Python
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Jó egyensúlyelmélet / gyakorlatok, az előadások szintjének a kevésbé tapasztalt hallgatókhoz való igazítása, egy nagyon nagy plusz a Jupiter Notebook használatához, és az elmélet bemutatása a gyakorlatban. Szerettem anonim visszajelzést gyűjteni a képzés első részéről is, másnap mindent elkészítettünk javaslataink szerint, és bár nagyon jó volt, akkor még jobb volt :)
Motorola Solutions Systems Polska Sp. z o.o
Kurzus: Programowanie w języku Python
Machine Translated
a tanár elkötelezettsége, előkészítése, a hallgatók hozzáállása, a félreértések magyarázata
Małgorzata Konior
Kurzus: Programowanie w języku Python
Machine Translated
Hogy a vezető mindenkihez közelít, még akkor is, ha nem hív segítségért és ellenőrzi a gyakorlat szintjét. Agniesz
Agnieszka Bielak
Kurzus: Programowanie w języku Python
Machine Translated
A tréner nagyon rövid elméleteket adott egy adott kérdésről, és azonnal elmentünk a gyakorlatba. Szép módja a kártyák felakasztására, amely tájékoztatja az oktatót arról, hogy mennyi időt töltött egy adott feladatra, és ki más problémákkal szembesül a megoldással.
Motorola Solutions Systems Polska Sp. z o.o
Kurzus: Programowanie w języku Python
Machine Translated
Még akkor is, ha valaki nem kérte, de látta, hogy nem lépett előre a feladattal, Krzysztof felállt, és képes volt ügyesen tanácsot adni Motoro
Motorola Solutions Systems Polska Sp. z o.o
Kurzus: Programowanie w języku Python
Machine Translated
A vezetés módja, a gyakorlatok, mindent egybevéve tetszett, nagyon boldog vagyok, hogy egy ilyen edzőhöz jöttem Ma
Maksym Kolodiy
Kurzus: Programowanie w języku Python
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Valódi gyakorlati példák
Motorola Solutions Systems Polska Sp. z o.o
Kurzus: Programowanie w języku Python
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A képzési anyagok elérhetősége (Jupyter), amelyet folyamatosan fejlesztettek ki a jegyzetfüzet frissítésével a tanfolyam során felmerült kérdések függvényében. Kételyek eloszlatása, minden kérdés megválaszolása.
Motorola Solutions Systems Polska Sp. z o.o
Kurzus: Programowanie w języku Python
Machine Translated
Megközelíthetőség és érdekes módja a tananyagok átadásának. - Mo
Motorola Solutions Systems Polska Sp. z o.o
Kurzus: Programowanie w języku Python
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Az xlsx és csv fájlok feldolgozása
Łukasz Olczyk
Kurzus: Python: Automate the boring stuff
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A gépi tanulási témák részletes ismertetése, különösen a neurális hálózatok. A téma nagy részét feldúsította.
Sacha Nandlall
Kurzus: Python for Advanced Machine Learning
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Tetszett a testreszabott, in-house fájlfeldolgozás és adatelemzés.
Glycom A/S
Kurzus: Data Analysis in Python using Pandas and Numpy
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Az esettanulmányok segítettek megérteni a Python alkalmazását az iparban. Nagyon értékeltem az oktató segítségét a gyakorlatok során
Rajiv Dhingra - TCS
Kurzus: Python Programming
Machine Translated
Mivel PHP fejlesztők vagyunk, megértette a helyzetet, és megengedte, hogy lassan térképezzünk a dolgok között. Tetszett a példák és a humor, amelyet hozzátette.
Soumya Tyagi - TCS
Kurzus: Python Programming
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Élveztem, hogy példáinkat felhasználva saját adatainkat használjuk.
Glycom A/S
Kurzus: Data Analysis in Python using Pandas and Numpy
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Mindent
MTU Aero Engines Polska Sp. z o. o.
Kurzus: Programowanie w języku Python
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Érdekes kérdések
MTU Aero Engines Polska Sp. z o. o.
Kurzus: Programowanie w języku Python
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Különféle felkészített kérdések és példák
MTU Aero Engines Polska Sp. z o. o.
Kurzus: Programowanie w języku Python
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a képzés nem bemutató stílus. Kollégáztunk edzővel.
Bhutan Telecom
Kurzus: Web Development with Django
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Leginkább a mindent élveztem.
Thukten Dendup - Bhutan Telecom
Kurzus: Web Development with Django
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Ez egy új élmény, egy új keret, és várom, hogy tegyen valamit az órákon tanult leckék segítségével.
Jigme - Bhutan Telecom
Kurzus: Web Development with Django
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Igazán élveztem a laborokat és gyakorlatokat.
Vivian Feng - Destination Canada
Kurzus: Data Analysis with SQL, Python and Spotfire
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A gyakorlatokat / laborokat a saját szervezeti igényeinkhez igazítottuk.
Destination Canada
Kurzus: Data Analysis with SQL, Python and Spotfire
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Általában szerettem a témát.
Destination Canada
Kurzus: Data Analysis with SQL, Python and Spotfire
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Az edző valódi szószerző tapasztalatokat osztott meg, jó megtanulni az igazi profitől.
Fednot
Kurzus: Python Programming
Machine Translated
A tréner kiváló volt, mindig kész volt megválaszolni kérdéseimet, és annyi tudást osztott meg, amennyit csak tudott.
Fahad Malalla - Tatweer Petroleum
Kurzus: Advanced Python
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1: 1 nagyon intenzív, de sokat tanultam.
Karen Dyke - BT
Kurzus: Python: Automate the Boring Stuff
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a "a kivetítőre" kódolt példákra való hangsúlyozás feltétlenül + a Tom számára.
ADVA OPTICAL NETWORKING SP. ZO O.
Kurzus: Advanced Python
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Én leginkább élveztem a témát.
Proximus
Kurzus: Python Programming
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Ahogy a gyakorlatokat szervezték: minden a saját tempóját, és Antonio ott, hogy segítsen tovább.
Proximus
Kurzus: Python Programming
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Tetszett a megfelelő és nagyon részletes olvasóanyagok és példák (csúszda).
HC Consumer Finance Philippines, Inc.
Kurzus: Python Programming
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Én valóban tetszett a na.
HC Consumer Finance Philippines, Inc.
Kurzus: Python Programming
Machine Translated
Amit szeretek a legjobban a képzés, hogy mindent során a vázlatot, hogy valami hasznos lesz a projektjeinket.
Joanna Marie Escueta - Aarki, Inc.
Kurzus: Python Programming
Machine Translated
Nagyon jó megközelítés a legfontosabb témák memorizálására / megismételésére. Nagyon szép "bemelegítő" gyakorlatok.
Kurzus: Python Programming
Machine Translated
Python Course Outlines
Kurzusnév
Időtartalma
Összefoglaló
Kurzusnév
Időtartalma
Összefoglaló
28 hours
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data.
14 hours
Összefoglaló
In this instructor-led, live training in Magyarország 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
Összefoglaló
In this instructor-led, live training in Magyarország, 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 hours
Összefoglaló
This instructor-led, live training in Magyarország 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.
21 hours
Összefoglaló
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
Összefoglaló
In this instructor-led, live training in Magyarország, 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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
In this instructor-led, live training in Magyarország, 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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
In this instructor-led, live training in Magyarország, 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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
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
Összefoglaló
In this instructor-led, live training in Magyarország (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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