I preferred the exercise and learning about the nooks and crannies of Python
Connor Brierley-Green - Natural Resources Canada
Python Schulungen
Python Programming Language courses
Erfahrungsberichte
Python Schulungsübersicht
| Code | Name | Dauer | Übersicht |
|---|---|---|---|
| pythonautomation | Python: Automate the boring stuff | 14 hours | 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 |
| python_nltk | Natural Language Processing with Python | 28 hours | 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 |
| mlfsas | Machine Learning Fundamentals with Scala and Apache Spark | 14 hours | 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 |
| kivy | Kivy: Building Android Apps with Python | 7 hours | 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. |
| pythonmultipurpose | Advanced Python | 28 hours | 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 |
| pythonprog | Python Programmierung | 28 hours | In diesem Kurs können Sie die Programmiersprache Python erlernen. Der Schwerpunkt des Kurses liegt dabei auf den Grundlagen der Sprache und zentralen Programmbibliotheken. Der Kurs besteht zur Hälfte aus Theorie, zur Hälfte aus praktischen Übungen. Er ist sowohl für Programmierer als auch Nichtprogrammierer geeignet. Einführung in die Programmiersprache Python Programme in Python schreiben und ausführen Bildschirmausgabe Eingabe von der Tastatur Datentypen für Zahlen und Text Arithmetische Operationen Übungen Programmstrukturen Einrückung von Programmblöcken Verzweigungen mit if Schleifen mit for und while Übungen Sequenzen Strings Listen Tupel Dictionaries Kommandozeilenparameter Übungen Funktionen Was sind Funktionen? Parameter und Rückgabewerte Vordefinierte Funktionen Rekursion Übungen Module Module in Python Importieren von Modulen Unit Tests für einzelne Module Pakete Übungen Behandlung von Ausnahmen Ausnahmen (Exceptions) Arten von Exceptions Abfangen mit try.. except Exceptions erzeugen Übungen Dateien verwalten Arten von Dateien Dateien öffnen Dateien lesen Dateien schreiben Übungen Manipulation von Strings Funktionen zum manipulieren von Strings Reguläre Ausdrücke Übungen Datenbankzugriff in Python MySQL (alternativ: MongoDB) Die SQL-Schnittstelle in Python Daten auswählen, einfügen und löschen Übungen Webseiten mit Flask erstellen HTML CSS Das Webframework Flask Übungen |
| progbio | Programmieren in Python für Biologen | 28 hours | Dieser Kurs richtet sich an: Wissenschaftler, die mit biologischen Daten arbeiten. Forscher, die Routineaufgaben automatisieren möchten. Biologen, die Ihre Arbeit mit einfachen Programmen verstärken möchten ohne gleich Vollzeitprogrammierer zu werden. Manager, die ein Grundverständnis für die Arbeit von Programmierern erlangen möchten. Am Ende des Kurses werden die Teilnehmer in der Lage sein kurze Programme selbständig zu schreiben, um biologische Daten zu analysieren und zu manipulieren. Einführung in die Programmiersprache Python Warum Python? Python als Werkzeug für Biologen Die Kommandozeile iPython Ihr erstes Programm Skripte in Python Module importieren Arbeiten mit Sequenzen von DNA, RNA und Proteinen Muster in Sequenzen finden Transkription und Translation Sequenzalignments verarbeiten Biopython Biologische Datenformate lesen FASTA Genbank Bäume NGS-Daten 3D-Strukturen Formate umwandeln Bioinformatische Analysetools verwenden Lokale Programme starten Web Services verwenden BLAST Automatische Pipelines erstellen Tabellarische Daten Tabellen lesen und schreiben Daten aus MS Excel / OpenOffice importieren Sortieren nach mehreren Kriterien Suchen in grossen Dateien Filtern von Duplikaten Statistische Analyse Durchschnitt, Standardabweichung und Median berechnen Chi-Quadrat-Tests Die Schnittstelle von Python zu R Datenvisualisierung Scatterplots generieren Säulen-, Balken-, und Kuchendiagramme erstellen Die Fläche unter einer Kurve berechnen (Area Under Curve, AUC) |
| 3627 | Introduction to Programming | 35 hours | The purpose of the training is to provide a basis for programming from the ground up to the general syntax of programming paradigms. The training is supported by examples based on programming languages such as C, Java, Python, Scala, C #, Closure and JavaScript. During the training, participants gain a general understanding of both the programming patterns, best practices, commonly used design and review of the implementation of these topics through various platforms. Each of the issues discussed during the course are illustrated with examples of both the most basic and more advanced and based on real problems. Introduction What is programming and why should devote his attention History of programming Opportunity to automate tasks using the software The role of the programmer and the computer in the enterprise Programming today the development of the current market trends Declarative and imperative programming. How or What? Turing machine Consolidation, compilation and interpretation "on the fly". Reminder issues of logic and Boolean algebra predicates logical sentences tautologies Boolean algebra The first program structurally functionally object And how else? Simple types Representation of strings Integers Floating-point numbers Boolean Type Null A blank or Uninitialized Strong and weak typing Data structures Concepts FIFO and FILO Stacks Queues Declaring arrays and lists Indexing Maps Records Trees Operators Assignment Operators. Arithmetic operators. comparison Operators And a comparison of the values in different languages Bitwise Concatenation Increment and decrement operators The most common errors Controlling the program The if, if else instructions Goto instructions, discuss the problems of application. The switch The for loop, for-in The while loop, do-while foreach loop Stopping loop Creating a reusable code Functional Programming Object-Oriented Programming Functional programming paradigms What is the function of Function and procedure Fundamentals of lambda calculus Function Arguments Returning values Functions as arguments Anonymous functions Closures Recursion The paradigms of object-oriented programming Representation of entities from the real world entities in philosophy, ontology Deciding what you want to object, or other types of Declaration of classes Creating instances of classes Fields, a state of the object Methods, as the behavior of an object abstraction Encapsulation Inheritance polymorphism Association and aggregation Delegation and separation of relationships between objects Modules, packages and libraries Sharing API The modeling of the system as classes and objects Describing and programming relationships between classes Program from a business perspective Good programming practice Pitfalls and common errors High-level code in the interpretation of low-level Code optimization KISS principle DRY principle Principle Worse is Better Separation abstraction of implementation Methods of error detection logic programs Conventions godowania Commenting the code Software Metrics Overview of these technologies and languages The area of application of these languages The main features of language Prospects for development The future direction of development: algorithmic, optimization of code, implementing patterns, design patterns, architectural patterns, analytical standards Reduction of the control structure - the use of artificial intelligence and automated decision-making Which platform to choose? Individual consultations |
| BigData_ | A practical introduction to Data Analysis and Big Data | 28 hours | 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, heavy 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 (crash course) Why R for Data Analysis? Data manipulation, calculation and graphical display Python (crash course) 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/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 Search Engines ElasticSearch Solr Scalability Public cloud AWS, Google, Aliyun, etc. Private cloud OpenStack, Cloud Foundry, etc. Auto-scalability Choosing right solution for the problem The future of Big Data Closing remarks |
| seleniumpython | Selenium with Python for test automation | 14 hours | 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 |
| mlfunpython | Machine Learning Fundamentals with Python | 14 hours | 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 |
Kommende Kurse
| Course | Schulungsdatum | Kurspreis (Fernkurs / Schulungsraum) |
|---|---|---|
| Introduction to Programming - Bern | Mo, 2017-10-09 09:30 | 6300EUR / 7250EUR |
| Machine Learning Fundamentals with Python - Basel | Mo, 2017-10-09 09:30 | 1490EUR / 1990EUR |
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Python Schulung, Python boot camp, Python Abendkurse, Python Wochenendkurse
, Python Seminar,Python Kurs, Python Lehrer
, Python Seminare, Python Coaching, Python Training
