Szkolenia Python

Szkolenia Python

Python Programming Language courses

Testi...Client Testimonials

Python Programming

I preferred the exercise and learning about the nooks and crannies of Python

Connor Brierley-Green - Natural Resources Canada

Python Programming

Joey has an infectious enthusiasm about programming. And he was very good at adapting to our needs and interests on the fly.

Randy Enkin - Natural Resources Canada

Python Programming

Many examples made me easy to understand.

Lingmin Cao - Natural Resources Canada

Programowanie w języku Python

Wszechstronna wiedza prowadzącego - na wszystkie nasze pytania dawał odpowiedzi przerastające moje oczekiwania... Wykładowca świetnie prowadzi dyskusje... Nie brakuje mu cierpliwości...

Łukasz Matulewicz - DGS Poland Sp. z o.o.; brightONE Sp. z o.o.

Programowanie w języku Python

Duża wiedzą prowadzącego, różnorodność narzędzi i praktyczne podejście do tematu

Magdalena Stupak - DGS Poland Sp. z o.o.; brightONE Sp. z o.o.

Programowanie w języku Python

duza wiedza trenera, sposob tlumaczenia

Renata Cylejowska - DGS Poland Sp. z o.o.; brightONE Sp. z o.o.

Python Programming

fact that customisation was taken seriously

jurgen linsen - BVBA 7pines

Natural Language Processing with Python

I did like the exercises

- Office for National Statistics

Python Programming

Helpful and very kind.

Natalia Machrowicz - MEELOGIC CONSULTING POLSKA SP Z O O

Python Programming

We did practical exercises (the scripts we wrote can be used in our everyday work). It made the course very interesting.
I also liked the way the trainer shared his knowledge. He did it in a very accessible way.

Malwina Sawa - MEELOGIC CONSULTING POLSKA SP Z O O

A practical introduction to Data Analysis and Big Data

Willingness to share more

Balaram Chandra Paul - MOL Information Technology Asia Limited

Python Programming

Very good approach to memorize/repeat the key topics. Very nice "warm-up" exercises.

Python Programming

* Enjoyable exercises.
* Quickly moved into more advanced topics.
* Trainer was friendly and easy to get on with.
* Customized course for needs of team.

Matthew Lucas - NAGRA MEDIA UK LTD

Python Programming

felixibility to add specific topics into the course / lessons

Marc Ammann - Sunrise Communications AG

Plany Szkoleń Python

Kod Nazwa Czas trwania Charakterystyka kursu
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 To request a customized course outline for this training, please contact us.
pythontextml Python: Machine learning with text 14 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 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 To request a customized course outline for this training, please contact us.
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 To request a customized course outline for this training, please contact us.
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, finance, 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.   Introduction     Python versatility: from data analysis to web crawling Python data structures and operations     Integers and floats     Strings and bytes     Tuples and lists     Dictionaries and ordered dictionaries     Sets and frozen sets     Data frame (pandas)     Conversions Object-oriented programming with Python     Inheritance     Polymorphism     Static classes     Static functions     Decorators     Other Data Analysis with pandas     Data cleaning     Using vectorized data in pandas     Data wrangling     Sorting and filtering data     Aggregate operations     Analyzing time series Data visualization     Plotting diagrams with matplotlib     Using matplotlib from within pandas     Creating quality diagrams     Visualizing data in Jupyter notebooks     Other visualization libraries in Python Vectorizing Data in Numpy     Creating Numpy arrays     Common operations on matrices     Using ufuncs     Views and broadcasting on Numpy arrays     Optimizing performance by avoiding loops     Optimizing performance with cProfile Processing Big Data with Python     Building and supporting distributed applications with Python     Data storage: Working with SQL and NoSQL databases     Distributed processing with Hadoop and Spark     Scaling your applications Python for finance     Packages, libraries and APIs for financial processing         Zipline         PyAlgoTrade         Pybacktest         quantlib         Python APIs Extending Python (and vice versa) with other languages     C#     Java     C++     Perl     Others Python multi-threaded programming     Modules     Synchronizing     Prioritizing UI programming with Python     Framework options for building GUIs in Python         Tkinter         Pyqt Python for maintenance scripting     Raising and catching exceptions correctly     Organizing code into modules and packages     Understanding symbol tables and accessing them in code     Picking a testing framework and applying TDD in Python Python for the web     Packages for web processing     Web crawling     Parsing HTML and XML     Filling web forms automatically Closing remarks
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 discussed to appreciate the real-world uses of NLG 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 To request a customized course outline for this training, please contact us.
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 To request a customized course outline for this training, please contact us.  
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 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 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 To request a customized course outline for this training, please contact us.
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 Introduction to Python Controlling the flow of your program Working with lists Working with the dictionary data type Manipulating strings Pattern matching with regular expressions Reading, writing and managing files Debugging your code Pulling information from the internet (web scraping) Working with Excel, Word, and PDF Documents Working with CSV and JSON Keeping time Scheduling tasks Launching programs Sending emails and other messages Manipulating images GUI Automation Closing remarks
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. Why web frameworks are needed Overview of available Python web frameworks Installation of Flask Routing requests to view functions Serving static files Rendering templates with Jinja2 Loops and conditionals Template inheritance Macros in templates Flat pages with Flask-Flatpages HTML5 Boilerplate as a starting point Producing JSON Issuing redirects Application context and Request context Dealing with file uploads with Flask-Uploads Structuring a complex application: how to avoid circular imports Structuring a complex application: Blueprints Commonly used ORMs: SQLAlchemy and Peewee Database migrations Form validation with WTForms and Flask-WTF Sending email with Flask-Mail User session management with Flask-Login and Flask-User The admin interface created by Flask-Admin Internationalization with Flask-BabelEx Preprocessing of frontend files with Flask-lesscss and Flask-Assets Deploying Flask applications into production
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 Introduction to Selenium with Python     Python vs Java for writing test scripts Installation and setup Selecting a Python IDE or editor Overview of Selenium architecture     Selenium IDE     Selenium WebDriver     Selenium Grid Python scripting essentials for test automation Working with Selenium Webdriver The anatomy of a web application Locating page elements through Page Objects Creating a unit test Accessing a database Developing a test framework Running test suites against multiple browsers Working with SeleniumGrid Troubleshooting Closing remarks
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. Introduction to Data Analysis and Big Data What makes Big Data "big"? Velocity, Volume, Variety, Veracity (VVVV) Limits to traditional Data Processing Distributed Processing Statistical Analysis Types of Machine Learning Analysis Data Visualization Languages used for Data Analysis R language Why R for Data Analysis? Data manipulation, calculation and graphical display Python Why Python for Data Analysis? Manipulating, processing, cleaning, and crunching data Approaches to Data Analysis Statistical Analysis Time Series analysis Forecasting with Correlation and Regression models Inferential Statistics (estimating) Descriptive Statistics in Big Data sets (e.g. calculating mean) Machine Learning Supervised vs unsupervised learning Classification and clustering Estimating cost of specific methods Filtering Natural Language Processing Processing text Understaing meaning of the text Automatic text generation Sentiment analysis / Topic analysis Computer Vision Acquiring, processing, analyzing, and understanding images Reconstructing, interpreting and understanding 3D scenes Using image data to make decisions Big Data infrastructure Data Storage Relational databases (SQL) MySQL Postgres Oracle Non-relational databases (NoSQL) Cassandra MongoDB Neo4js Understanding the nuances Hierarchical databases Object-oriented databases Document-oriented databases Graph-oriented databases Other Distributed Processing Hadoop HDFS as a distributed filesystem MapReduce for distributed processing Spark All-in-one in-memory cluster computing framework for large-scale data processing Structured streaming Spark SQL Machine Learning libraries: MLlib Graph processing with GraphX Scalability Public cloud AWS, Google, Aliyun, etc. Private cloud OpenStack, Cloud Foundry, etc. Auto-scalability Choosing the right solution for the problem The future of Big Data Closing remarks
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. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Machine Learning with Python Choice of libraries Add-on tools Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means
mlbankingpython Machine Learning for Banking (with Python) 28 godz. 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. Deep learning techniques are covered in the latter part of the course. Python will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete live team projects. Introduction Difference between statistical learning (statistical analysis) and machine learning Adoption of machine learning technology and talent by finance and banking companies Different Types of Machine Learning Supervised learning vs unsupervised learning Iteration and evaluation Bias-variance trade-off Combining supervised and unsupervised learning (semi-supervised learning) Machine Learning Languages and Toolsets Open source vs proprietary systems and software Python vs R vs Matlab Libraries and frameworks Machine Learning Case Studies Consumer data and big data Assessing risk in consumer and business lending Improving customer service through sentiment analysis Detecting identity fraud, billing fraud and money laundering Hands-on: Python for Machine Learning Preparing the Development Environment Obtaining Python machine learning libraries and packages Working with scikit-learn and PyBrain How to Load Machine Learning Data Databases, data warehouses and streaming data Distributed storage and processing with Hadoop and Spark Exported data and Excel Modeling Business Decisions with Supervised Learning Classifying your data (classification) Using regression analysis to predict outcome Choosing from available machine learning algorithms Understandind decision tree algorithms Understanding random forest algorithms Model evaluation Exercise Regression Analysis Linear regression Generalizations and Nonlinearity Exercise Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercise Hands-on: Building an Estimation Model Assessing lending risk based on customer type and history Evaluating the performance of Machine Learning Algorithms Cross-validation and resampling Bootstrap aggregation (bagging) Exercise Modeling Business Decisions with Unsupervised Learning K-means clustering Challenges of unsupervised learning Beyond K-means Exercise Hands-on: Building a Recommendation System Analyzing past customer behavior to improve new service offerings Introduction to Neural Networks and Deep Learning Layers and nodes Convolutional neural networks Recurrent neural networks Multilayer perceptrons Frameworks: Theano, TensorFlow, Keras Exercise Hands-on: Building an AI system Monitoring big data to detect money laundering and billing fraud Extending your company's capabilities Developing models in the cloud Accelerating machine learning with GPU Beyond machine learning: Artificial Intelligence (AI) Applying neural networks for computer vision, voice recognition and text analysis Closing Remarks
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.Overview of Python packages related to NLP   Introduction to NLP (examples in Python of course) Simple Text Manipulation Searching Text Counting Words Splitting Texts into Words Lexical dispersion Processing complex structures Representing text in Lists Indexing Lists Collocations Bigrams Frequency Distributions Conditionals with Words Comparing Words (startswith, endswith, islower, isalpha, etc...) Natural Language Understanding Word Sense Disambiguation Pronoun Resolution Machine translations (statistical, rule based, literal, etc...) Exercises NLP in Python in examples Accessing Text Corpora and Lexical Resources Common sources for corpora Conditional Frequency Distributions Counting Words by Genre Creating own corpus Pronouncing Dictionary Shoebox and Toolbox Lexicons Senses and Synonyms Hierarchies Lexical Relations: Meronyms, Holonyms Semantic Similarity Processing Raw Text Priting struncating extracting parts of string accessing individual charaters searching, replacing, spliting, joining, indexing, etc... using regular expressions detecting word patterns stemming tokenization normalization of text Word Segmentation (especially in Chinese) Categorizing and Tagging Words Tagged Corpora Tagged Tokens Part-of-Speech Tagset Python Dictionaries Words to Propertieis mapping Automatic Tagging Determining the Category of a Word (Morphological, Syntactic, Semantic) Text Classification (Machine Learning) Supervised Classification Sentence Segmentation Cross Validation Decision Trees Extracting Information from Text Chunking Chinking Tags vs Trees Analyzing Sentence Structure Context Free Grammar Parsers Building Feature Based Grammars Grammatical Features Processing Feature Structures Analyzing the Meaning of Sentences Semantics and Logic Propositional Logic First-Order Logic Discourse Semantics  Managing Linguistic Data  Data Formats (Lexicon vs Text) Metadata
mlbankingr Machine Learning for Banking (with R) 28 godz. 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. R will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete live team projects. Introduction Difference between statistical learning (statistical analysis) and machine learning Adoption of machine learning technology by finance and banking companies Different Types of Machine Learning. Supervised learning vs unsupervised learning Iteration and evaluation Bias-variance trade-off Combining supervised and unsupervised learning (semi-supervised learning) Machine Learning Languages and Toolsets. Open source vs proprietary systems and software R vs Python vs Matlab Libraries and frameworks Machine Learning Case Studies Consumer data and big data Assessing risk in consumer and business lending Improving customer service through sentiment analysis Detecting identity fraud, billing fraud and money laundering Introduction to R Installing the RStudio IDE Loading R packages Data structures Vectors Factors Lists Data Frames Matrixes and Arrays How to Load Machine Learning Data Databases, data warehouses and streaming data Distributed storage and processing with Hadoop and Spark Importing data from a database Importing data from Excel and CSV Modeling Business Decisions with Supervised Learning Classifying your data (classification) Using regression analysis to predict outcome Choosing from available machine learning algorithms Understandind decision tree algorithms Understanding random forest algorithms Model evaluation Exercise Regression Analysis Linear regression Generalizations and Nonlinearity Exercise Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercise Hands-on: Building an Estimation Model Assessing lending risk based on customer type and history Evaluating the performance of Machine Learning Algorithms Cross-validation and resampling Bootstrap aggregation (bagging) Exercise Modeling Business Decisions with Unsupervised Learning. K-means clustering Challenges of unsupervised learning Beyond K-means Exercise Hands-on: Building a Recommendation System. Analyzing past customer behavior to improve new service offerings Extending your company's capabilities Developing models in the cloud Accelerating machine learning with additional GPUs Beyond machine learning: Artificial Intelligence (AI) Applying Deep Learning neural networks for computer vision, voice recognition and text analysis Closing Remarks
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. Introduction to Applied Machine Learning Statistical learning vs. Machine learning Iteration and evaluation Bias-Variance trade-off Machine Learning with Python Choice of libraries Add-on tools Regression Linear regression Generalizations and Nonlinearity Exercises Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercises Cross-validation and Resampling Cross-validation approaches Bootstrap Exercises Unsupervised Learning K-means clustering Examples Challenges of unsupervised learning and beyond K-means
mlbankingpython_ Machine Learning for Banking (with Python) 21 godz. 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. Python will be used as the programming language. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete live team projects. Introduction Difference between statistical learning (statistical analysis) and machine learning Adoption of machine learning technology and talent by finance and banking companies Different Types of Machine Learning Supervised learning vs unsupervised learning Iteration and evaluation Bias-variance trade-off Combining supervised and unsupervised learning (semi-supervised learning) Machine Learning Languages and Toolsets Open source vs proprietary systems and software Python vs R vs Matlab Libraries and frameworks Machine Learning Case Studies Consumer data and big data Assessing risk in consumer and business lending Improving customer service through sentiment analysis Detecting identity fraud, billing fraud and money laundering Hands-on: Python for Machine Learning Preparing the Development Environment Obtaining Python machine learning libraries and packages Working with scikit-learn and PyBrain How to Load Machine Learning Data Databases, data warehouses and streaming data Distributed storage and processing with Hadoop and Spark Exported data and Excel Modeling Business Decisions with Supervised Learning Classifying your data (classification) Using regression analysis to predict outcome Choosing from available machine learning algorithms Understandind decision tree algorithms Understanding random forest algorithms Model evaluation Exercise Regression Analysis Linear regression Generalizations and Nonlinearity Exercise Classification Bayesian refresher Naive Bayes Logistic regression K-Nearest neighbors Exercise Hands-on: Building an Estimation Model Assessing lending risk based on customer type and history Evaluating the performance of Machine Learning Algorithms Cross-validation and resampling Bootstrap aggregation (bagging) Exercise Modeling Business Decisions with Unsupervised Learning K-means clustering Challenges of unsupervised learning Beyond K-means Exercise Hands-on: Building a Recommendation System Analyzing past customer behavior to improve new service offerings Extending your company's capabilities Developing models in the cloud Accelerating machine learning with GPU Beyond machine learning: Artificial Intelligence (AI) Applying Deep Learning neural networks for computer vision, voice recognition and text analysis Closing Remarks
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. Potrzeba programowania obiektowego Próba odzwierciedlenia realnego świata w programowaniu Pocztątki i ewolucja programowania obiektowego Programowanie obiektowe w aspekcie reguł KISS i DRY Klasy i obiekty Idee i byty w filozofii Platona Rola klas Realizacja klas za pomoc obiektów Klasy, właściwości i metody statyczne Konwencje nazewnictwa i kodowania Nazewnictwo klas Nazewnictwo właściwości i metod Nazewnictwo pakietów i folderów Pozostałe konwencje Struktura klasy Właściwości/pola jako opis stanu obiektu Akcesory Metody jako realizacja funkcjonalności Modelowanie i graficzna reprezentacja klas Analiza wymagań Modelowanie struktury klas i relacji Opisywanie obiektów biznesowych Diagramy EER Diagram klas UML Paradygmaty programowania obiektowego Pojęcie paradygmatu Hermetyzacja Abstrakcja Dziedziczenie Polimorfizm Projektowanie i realizacja warstwy abstrakcji Klasy abstrakcyjne Interfejsy Cechy -Traits Klazy zagnieżdżone Klasy generyczne Tworzenie obiektów Sposoby tworzenia obiektów Rola konstruktora Wzorzec Fabryki Zależności między klasami Agregacja Kompozycja Rozdział zależności - Decoupling Wstrzykiwanie zależności, kontenery DIC Wzorzec Mediatora Organizacja kodu Projektowanie kodu wielokrotnego użycia Struktura folderów Przestrzenie nazw, pakiety, moduły Programowanie obiektowe a wydajność Rezerwacja pamięci dla obiektów Garbage Collector Jawne usuwanie obiektów, destruktory Praca z referencjami Mechanizm refleksji Obszar zastosować refleksji Pozyskiwanie informacji o klasach i obiektach Znaczenie refleksji w tworzeniu dokumentacji i testowaniu oprogramowania Obsługa błędów Możliwe modele obsługi błędów Obiektowy model obsługi błędów Rola wyjątków i klasa Exception, rzucanie i przechwytywanie wyjtków Blok try-catch-final, zagnieżdżanie bloku Antywzorce programowania obiektowego Nadmierna odpowiedzialność klas, wzorzec Delegacji Silne zależności Singleton i potencjalne problemy Anemic Domain Model Pozostałe antywzorce
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. Podstawy teoretyczne wzorców projektowych Historia wzorców projektowych Podział wzorców projektowych Wzorce (teoria i ćwiczenia) - Creational Design Patterns Abstract Factory Builder Factory Method Object Pool Prototype Singleton Wzorce (teoria i ćwiczenia) - Structural Design Patterns Adapter Bridge Composite Decorator Facade Flyweight Private Class Data Proxy Wzorce (teoria i ćwiczenia) - Behavioral Design Patterns Chain of responsibility Command Interpreter Iterator Mediator Memento Null Object Observer State Strategy Template method Visitor Wzorce złożone MVC (Model - View - Controller) MVP (Model - View - Presenter) MVVM (Model -View -View Model) Symulacja Projektowania Architektury - Hands On Labs Opracowanie Architektury Sytemu w grupach na bazie podanego Business Case
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. Introduction to Python Programming Writing and running Python Programs Outputting to the screen Inputting from the keyboard Data types and int(), float() and str() Arithmetic operations Exercise Program Structures Indentation Conditional statements Looping statements Exercise Sequences Strings Lists Tuples Dictionaries Command line parameters Exercise Functions What are functions Parameters and return values Predefined functions Recursion Exercise Modules Modules Importing modules Unit testing modules Packages Exercise Error Handling Exceptions Exception types try except try except else try finally Raining exceptions Exercise File Handling Types of file File handling principles Opening files Reading files Writing files Exercise String Manipulation String manipulation String manipulation functions Regular expressions Exercise Database Access in Python MySQL Python database access principles Selecting data Inserting data Deleting data Exercise CGI HTML CSS CGI Python CGI Exercise
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. Wprowadzenie Czym jest programowanie i dlaczego warto poświęcić mu uwagę Historia programowania Możliwości automatyzacji zadań za pomocą oprogramowania Rola programisty i komputera w przedsiębiorstwie Programowanie dziś, trendy rozwoju obecnego rynku Programowanie deklaratywne i imperatywne. Jak czy Co? Maszyna Turinga Konsolidacja, kompilacja i interpretowanie "w locie" Przypomnienie zagadnień logiki i algebry Boole'a Predykaty Zdania logiczne Tautologie Algebra Boole'a Pierwszy program Strukturalnie Funkcyjnie Obiektowo I jak jeszcze? Typy proste Reprezentacja ciągów znakowych Liczby całkowite Liczby zmiennoprzecinkowe Typy logiczne Typ Null Wartość pusta czy niezainicjalizowana Silne i słabe typowanie Struktury danych Pojęcia FIFO i FILO Stosy Kolejki Deklaracja tablic i list Indeksowanie Mapy Rekordy Drzewa Operatory Operatory przypisania Operatory arytmetyczne Operatory porównania Porównanie typu i wartości w różnych językach Operatory bitowe Konkatenacja Operatory inkrementacji i dekrementacji Najczęściej popełniane błędy Sterowanie przebiegiem programu Instrukcje if, if else Instrukcja goto, omówienie problemów zastosowania Instrukcja switch Pętla for, for-in Pętla while, do-while Pętla foreach Przerywanie pętli Tworzenie kodu wielokrotnego użycia Programowanie funkcyjne Programowanie obiektowe Paradygmaty programowania funkcyjnego Czym jest funkcja Funkcja a procedura Podstawy rachunku lambda Argumenty funkcji Zwracanie wartości Funkcje jako argumenty Funkcje anonimowe Domknięcia Rekurencja Paradygmaty programowania obiektowego Reprezentacja bytów z realnego świata, byty w filozofii, ontologie i idee, potrzeba objektowości Podejmowanie decyzji co ma być objektem, czyli własne typy Deklaracja klas Tworzenie instancji klas Pola, jako stan obiektu Metody, jako zachowanie obiektu Abstrakcja Hermetyzacja Dziedziczenie Polimorfizm Asocjacja i agregacja Delegacja i rozdzielenie zależności pomiędzy obiektami Moduły, pakiety, biblioteki Udostępnianie API Modelowanie systemu jako klas i obiektów Opisywanie i programowanie relacji pomiędzy klasami Program z perspektywy biznesowej Dobre praktyki programowania Pułapki i najczęściej popełniane błędy Kod wysokopoziomowy w interpretacji niskopoziomowej Optymalizacja kodu Zasada KISS Zasada DRY Zasada Worse is Better Separacja abstrakcji od implementacji Sposoby detekcji błędów logicznych programów Konwencje godowania Komentowanie kodu Metryki oprogramowania Przegląd technologii i omawianych języków Obszar zastosowań omawianych języków Główne cechy języków Perspektywy rozwoju Dalszy kierunek rozwoju: algorytmika, optymalizacja kodu, wzorce implementacyjne, wzorce projektowe, wzorce architektoniczne, wzorce analityczne Redukcja konstrukcji sterujących - zastosowanie algorytmów sztucznej inteligencji i automatyczne podejmowanie decyzji Którą platformę wybrać? Indywidualne konsultacje
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. Introduction to the Python programming language Why Python? Using Python to deal with biological data Working with the iPython shell Your first programme Writing Python scripts Importing modules Working with protein and RNA/DNA sequences Finding motives Transcription and translation in silico Handling sequence alignments Parsing data in different biological formats Parsing FASTA Data format conversions Running biological analyses BLAST Accessing biological web services Dealing with biological 3D structures using Python Python facilitates statistical analysis Visualizing data Creating bar and scatter plots Calculating an Area Under Curve (AUC) Working with .xls and .csv files Importing data from and exporting to MS Excel / OpenOffice Calc Writing .xls and .csv files Using Python to create an automated data processing pipeline
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 To request a customized course outline for this training, please contact us.

Najbliższe szkolenia

SzkolenieData KursuCena szkolenia [Zdalne / Stacjonarne]
Introduction to Programming - Zakopane ul. Jagiellońska 30 pon., 2017-12-11 09:005800PLN / 4650PLN

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Docker - zarządzanie kontenerami Warszawa, ul. Złota 3/11 wt., 2017-12-05 09:00 8910PLN / 3570PLN
Test Automation with Selenium and Jenkins Wrocław, ul.Ludwika Rydygiera 2a/22 śr., 2017-12-06 09:00 7722PLN / 3174PLN
Adobe Premiere Pro Gdańsk, ul. Powstańców Warszawskich 45 czw., 2017-12-07 09:00 3960PLN / 2480PLN
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