Szkolenia Python
Python is a high-level programming language famous for its clear syntax and code readability.
NobleProg onsite live Python training courses demonstrate through hands-on practice various aspects of the Python programming language. Some of the topics covered include the fundamentals of Python programming, advanced Python programming, Python for test automation, Python scripting and automation, and Python for Data Analysis and Big Data applications in areas such as Finance, Banking and Insurance.
NobleProg Python training courses also cover beginning and advanced courses in the use of Python libraries and frameworks for Machine Learning and Deep Learning.
Python training is available in various formats, including onsite live training and live instructor-led training using an interactive, remote desktop setup. Local Python training can be carried out live on customer premises or in NobleProg local training centers.
Opinie uczestników
Plany Szkoleń Python
| Kod | Nazwa | Czas trwania | Charakterystyka kursu |
|---|---|---|---|
| textsum | Text Summarization with Python | 14 godz. | 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 |
| mlfinancepython | Machine Learning for Finance (with Python) | 21 godz. | 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 |
| ooppython | Learn Object-Oriented Programming with Python | 14 godz. | 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 |
| sparkpython | Python and Spark for Big Data (PySpark) | 21 godz. | Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python. In this instructor-led, live training, 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 circumstances Use different tools and techniques for big data analysis using PySpark Audience Developers IT Professionals Data Scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| progbio | Programming for Biologists | 28 godz. | 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 programming. 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. |
| drlpython | Deep Reinforcement Learning with Python | 21 godz. | 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 |
| restfulapi | Designing RESTful APIs | 14 godz. | APIs (Application Programming Interface) allow for your application to connect with other applications. In this instructor-led, live training, participants will learn how to write high-quality APIs as they build and secure a backend API server. By the end of this training, participants will be able to: Choose from a number of frameworks for building APIs Understand and model the APIs published by companies such as Google and Facebook Create and publish their own Restful APIs for public consumption Secure their APIs through token-based authentication Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Note To customize this course for other languages, such as PHP, Javascript, etc., please contact us to arrange |
| mlfunpython | Machine Learning Fundamentals with Python | 14 godz. | 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. |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28 godz. | 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 |
| pythonautomation | Python: Automate the boring stuff | 14 godz. | This instructor-led training 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. Audience Non-programmers wishing to learn programming with Python Professionals and company teams wishing to optimize their office productivity Managers wishing to automate tedious processes and workflows Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| python_nltk | Natural Language Processing with Python | 28 godz. | 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. |
| pythoncomputervision | Computer Vision with Python | 14 godz. | 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 |
| flask | Web application development with Flask | 14 godz. | This practical course is addressed to Python developers that want to create and maintain their first web applications. It is also addressed to people who are already familiar with other web frameworks such as Django or Web2py, and want to learn how using a microframework (i.e. a framework which glues together third-party libraries instead of providing a self-contained universal solution) changes the process. A significant part of the course is devoted not to Flask itself (it's tiny), but to third-party libraries and tools often used in Flask projects. |
| mlfsas | Machine Learning Fundamentals with Scala and Apache Spark | 14 godz. | The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the Scala 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. |
| pythonbigdata | Analyzing Big Financial Data with Python | 35 godz. | 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 |
| pythonprog1 | Programowania w języku Python - szybki kurs | 7 godz. | |
| datapyth | Data Analysis in Python using Pandas and Numpy | 14 godz. | Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. |
| microservicespython | Developing Microservices with Python | 7 godz. | 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 |
| ooprog | Programowanie obiektowe | 21 godz. | Szkolenie skierowane jest dla osób chcących zapoznać się z możliwościami programowania obiektowego oraz realizacją paradygmatu w wybranym języku: C#, Java, Groovy, Scala lub PHP. |
| BigData_ | A Practical Introduction to Data Analysis and Big Data | 35 godz. | Participants who complete this training will gain a practical, real-world understanding of Big Data and its related technologies, methodologies and tools. Participants will have the opportunity to put this knowledge into practice through hands-on exercises. Group interaction and instructor feedback make up an important component of the class. The course starts with an introduction to elemental concepts of Big Data, then progresses into the programming languages and methodologies used to perform Data Analysis. Finally, we discuss the tools and infrastructure that enable Big Data storage, Distributed Processing, and Scalability. Audience Developers / programmers IT consultants Format of the course Part lecture, part discussion, hands-on practice and implementation, occasional quizing to measure progress. |
| tableaupython | Tableau with Python | 14 godz. | 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 |
| wppyth | Wzorce projektowe w Python | 14 godz. | Grupa docelowa: Technical Team Leader, Software Developer Cel szkolenia: Celem szkolenia jest nabycie umiejętności projektowania zawansowanych struktur programistycznych / projektowych w języku Python. |
| openface | OpenFace: Creating Facial Recognition Systems | 14 godz. | OpenFace is Python and Torch based open-source, real-time facial recognition software based on Google’s FaceNet research. In this instructor-led, live training, participants will learn how to use OpenFace's components to create and deploy a sample facial recognition application. By the end of this training, participants will be able to: Work with OpenFace's components, including dlib, OpenVC, Torch, and nn4 to implement face detection, alignment, and transformation. Apply OpenFace to real-world applications such as surveillance, identity verification, virtual reality, gaming, and identifying repeat customers, etc. Audience Developers Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| iotpython | Programming for IoT with Python | 14 godz. | 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. |
| pythonprog | Programowanie w języku Python | 28 godz. | 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 Python 2.7.x or 3.x, with practical exercises making use of the full power of both versions of the language. 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. |
| pythonadvml | Python for Advanced Machine Learning | 21 godz. | In this instructor-led, live training, 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 Audience Developers Analysts Data scientists Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonexcel | Python for Excel | 14 godz. | Python is a high-level programming language famous for its clear syntax and code readability. Excel is a spreadsheet application developed by Microsoft which is widely used in many industries. Adding Python to Excel makes it a powerful tool for data analytics. In this instructor-led, live training, 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 Audience Developers Programmers 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. |
| 3627 | Wprowadzenie do programowania | 35 godz. | Celem szkolenia jest przedstawienie podstaw związanych z programowaniem od podstaw składni po powszechne paradygmaty programowania. Szkolenie poparte jest przykładami opartymi o języki programowania takie jak: C, Java, Python, Scala, C#, Closure i JavaScript. Podczas szkolenia uczestnicy zdobywają ogólną wiedzę zarówno ze wzorców programowania, dobrych praktyk, powszechnie stosowanych rozwiązań jak i przegląd realizacji omawianych tematów przez różne platformy. Każde z zagadnień omówionych podczas szkolenia ilustrowane jest przykładami zarówno najbardziej podstawowymi, jak i bardziej zaawansowanymi i opartymi o rzeczywiste problemy. |
| pythontextml | Python: Machine Learning with Text | 21 godz. | 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 |
| chatbotpython | Building Chatbots in Python | 21 godz. | ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions. In this instructor-led, live training, participants will learn how to build chatbots in Python. By the end of this training, participants will be able to: Understand the fundamentals of building chatbots Build, test, deploy, and troubleshoot various chatbots using Python Audience Developers 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. |
| kivy | Kivy: Building Android Apps with Python | 7 godz. | Kivy is an open-source cross-platform graphical user interface library written in Python, which allows multi-touch application development for a wide selection of devices. In this instructor-led, live training participants will learn how to install and deploy Kivy on different platforms, customize and manipulate widgets, schedule, trigger and respond to events, modify graphics with multi-touching, resize the screen, package apps for Android, and more. By the end of this training, participants will be able to Relate the Python code and the Kivy language Have a solid understanding of how Kivy works and makes use of its most important elements such as, widgets, events, properties, graphics, etc. Seamlessly develop and deploy Android apps based on different business and design requirements Audience Programmers or developers with Python knowledge who want to develop multi-touch Android apps using the Kivy framework Android developers with Python knowledge Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| nlg | Python for Natural Language Generation | 21 godz. | 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 |
| pytest | Unit Testing with Python | 21 godz. | Unit testing is a testing approach that tests individual units of source code by modifying their properties or triggering an event to confirm whether the outcome is as expected. PyTest is a full-featured, API-independent, flexible, and extensible testing framework with an advanced, full-bodied fixture model. In this instructor-led, live training, 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 Audience Software testers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
| python_nlp | Natural Language Processing with Deep Dive in Python and NLTK | 35 godz. | 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. |
| seleniumpython | Selenium with Python for Test Automation | 14 godz. | Selenium is an open source library for automating web application testing across multiple browsers. Selenium interacts with a browser as people do: by clicking links, filling out forms and validating text. It is the most popular tool for web application test automation. Selenium is built on the WebDriver framework and has excellent bindings for numerous scripting languages, including Python. In this training 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. Audience Testers and Developers Format of the course Part lecture, part discussion, heavy hands-on practice |
| mlbankingpython_ | Machine Learning for Banking (with Python) | 21 godz. | 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 |
| behave | Behave: BDD with Python (Cucumber/Gherkin for Python) | 7 godz. | Behave is an open-source, Python-based BDD framework for writing tests in a natural language style. BDD, or Behavior Driven Development, is an agile software development technique that encourages collaboration among developers, QA and non-technical business people in a software project. This training begins with a discussion of BDD and how the Behave framework can be used to carry out BDD testing for web applications. Participants are given ample opportunity to interact with the instructor and peers while implementing the concepts and tactics learned in this hands-on, practice-based lab environment. By the end of this training, participants will have a firm understanding of BDD and Behave, as well as the necessary practice to implement these techniques and tools in real-world test scenarios. Audience Testers and Developers Format of the course Heavy emphasis on hands-on practice. Most of the concepts are learned through samples, exercises and hands-on development. |
| pythonfinance | Python Programming for Finance | 35 godz. | 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. |
| pythonmultipurpose | Advanced Python | 28 godz. | In this instructor-led training, 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. Audience Developers Format of the course Part lecture, part discussion, exercises and heavy hands-on practice Notes If you wish to add, remove or customize any section or topic within this course, please contact us to arrange. |
| dlfornlp | Deep Learning for NLP (Natural Language Processing) | 28 godz. | Deep Learning for NLP allows a machine to learn simple to complex language processing. Among the tasks currently possible are language translation and caption generation for photos. DL (Deep Learning) is a subset of ML (Machine Learning). Python is a popular programming language that contains libraries for Deep Learning for NLP. In this instructor-led, live training, participants will learn to use Python libraries for NLP (Natural Language Processing) as they create an application that processes a set of pictures and generates captions. By the end of this training, participants will be able to: Design and code DL for NLP using Python libraries Create Python code that reads a substantially huge collection of pictures and generates keywords Create Python Code that generates captions from the detected keywords Audience Programmers with interest in linguistics Programmers who seek an understanding of NLP (Natural Language Processing) Format of the course Part lecture, part discussion, exercises and heavy hands-on practice |
Najbliższe szkolenia
| Szkolenie | Data Kursu | Cena szkolenia [Zdalne / Stacjonarne] |
|---|---|---|
| Programowania w języku Python - szybki kurs - Łódź, ul. Tatrzańska 11 | wt., 2018-05-08 09:00 | 1000PLN / 1500PLN |
| Python: Machine Learning with Text - Lublin, ul. Spadochroniarzy 9 | śr., 2018-05-09 09:00 | 4990PLN / 5990PLN |
| Machine Learning Fundamentals with Python - Częstochowa, ul. Wały Dwernickiego 117/121 | czw., 2018-05-10 09:00 | 3330PLN / 4080PLN |
| Deep Learning for Finance (with Python) - Białystok, ul. Malmeda 1 | pon., 2018-05-14 09:00 | 6650PLN / 7900PLN |
| Data Analysis in Python using Pandas and Numpy - Warszawa, ul. Złota 3/11 | pon., 2018-05-14 09:00 | 1990PLN / 2740PLN |
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