Python Training Courses

Python Training

Python Programming Language courses

Testi...Client Testimonials

Python programming

I liked everything from the preparation and presentation to trainer interaction

Fahad Malalla - Tatweer Petroleum

Introductory R for Biologists

What did you like the most about the training?:

I liked the fact that we were all the time busy programming, so I had to focus the whole time.

Katarzyna Hutnik - University of Oxford, Department of Oncology

Introductory R for Biologists

What did you like the most about the training?:

I think the trainer was brilliant.

A fully qualified teacher with training experience.

Enric Domingo - University of Oxford, Department of Oncology

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

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 Course Outlines

Code Name Duration Overview
pythonadvml Python for Advanced Machine Learning 21 hours 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.
pythontextml Python: Machine learning with text 14 hours 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.
pythonprog Python Programming 28 hours 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 Running Python code Using Python Development Tools (IDEs and command line tools) Working with Python and iPython shells as well as iPython Notebook Data Types and Operations Integers and floats Strings and bytes Tuples and lists Dictionaries and ordered dictionaries Sets and frozen sets Organizing and Distributing Code Creating modules and packages Distributing code to repositories Object Oriented and Functional Programming Creating and using functions and classes Modifying functions and classes with decorators Introducing meta-classes Error Handling and Testing Handling and raising exceptions Writing and executing tests (doc tests and unit tests) Checking code coverage by tests Working with Files and Directories Accessing different types of files and file handling principles Creating, reading, updating and deleting files (including regular text files, csv, as well as Microsoft Word and Microsoft Excel files) Extracting data from text files using Regular Expressions Creating and deleting directories, listing and searching for files Accessing Databases Selecting, inserting, updating and deleting data Generic database API based on SQLite 3, PostgreSQL and MySQL Using the Object Relational Mapper (SQLAlchemy) Working with NoSQL databases Conquering The Web Retrieving web pages Parsing HTML and XML Filling web forms automatically Creating web applications in Python
progbio Programming for Biologists 28 hours 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
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
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
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
BigData_ A practical introduction to Data Analysis and Big Data 35 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, 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/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 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
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
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
openface OpenFace: Creating Facial Recognition Systems 14 hours 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.
restfulapi Designing RESTful APIs 14 hours 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.

Upcoming Courses

CourseCourse DateCourse Price [Remote / Classroom]
Programming for Biologists - VA, Reston - Sunrise Valley Mon, Nov 6 2017, 9:30 am$7160 / $9990

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