Python Schulungen

Python Schulungen

Python Programming Language courses

Python Schulungsübersicht

ID Name Dauer Übersicht
566871 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
566832 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
359543 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
359631 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
25052 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)
24074 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

Spezialangebote

Course Ort Schulungsdatum Kurspreis (Fernkurs/Schulungsraum)
Tomcat München Mo, 2016-09-05 09:30 2723EUR / 3373EUR
Introduction to Deep Learning Potsdam Mi, 2016-10-19 09:30 4277EUR / 4927EUR

Kommende Kurse

CourseSchulungsdatumKurspreis (Fernkurs/Schulungsraum)
Python Programming - Frankfurt am MainDi, 2016-09-13 09:303650EUR / 4450EUR
Programming for Biologists - PotsdamDi, 2016-09-13 09:303690EUR / 4490EUR
Python Schulung, Python boot camp, Python Abendkurse, Python Wochenendkurse , Python Seminare,Python Kurs, Python Seminar, Python Privatkurs, Python Training, Python Coaching

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