Local, instructor-led live Python training courses demonstrate through hands-on practice various aspects of the Python programming language. Some of the topics covered include the fundamentals of Python programming, advanced Python programming, Python for test automation, Python scripting and automation, and Python for Data Analysis and Big Data applications in areas such as Finance, Banking and Insurance.
NobleProg Python training courses also cover beginning and advanced courses in the use of Python libraries and frameworks for Machine Learning and Deep Learning.
Python training is available as "onsite live training" or "remote live training". Onsite live Python training can be carried out locally on customer premises in Thailand or in NobleProg corporate training centers in Thailand. Remote live training is carried out by way of an interactive, remote desktop.
NobleProg -- Your Local Training Provider
I liked everything from the preparation and presentation to trainer interaction.
Fahad Malalla - Tatweer Petroleum
Course: Python Programming
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
Course: Programming 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
Course: Programming for Biologists
I preferred the exercise and learning about the nooks and crannies of Python.
Connor Brierley-Green
Course: 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
Course: Python Programming
Many examples made me easy to understand.
Lingmin Cao
Course: Python Programming
Fact that customization was taken seriously.
jurgen linsen
Course: Python Programming
As I was the only participant the training could be adapted to my needs.
Kevin THIERRY
Course: Web Development with Web2Py
I did like the exercises.
Office for National Statistics
Course: Natural Language Processing with Python
I liked the helpful and very kind.
Natalia Machrowicz
Course: 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
Course: Python Programming
Very good approach to memorize/repeat the key topics. Very nice “warm-up” exercises.
Course: 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
Course: Python Programming
I enjoyed the felixibility to add specific topics into the course / lessons.
Marc Ammann
Course: Python Programming
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course: Python for Advanced Machine Learning
I really liked everything!.
UBS Business Solutions Poland Sp. z o.o.
Course: Python Programming
I generally liked the personalized help.
UBS Business Solutions Poland Sp. z o.o.
Course: Python Programming
Everything was fine!.
UBS Business Solutions Poland Sp. z o.o.
Course: Python Programming
It was good to talk about it with someone (I did on-line courses before).
UBS Business Solutions Poland Sp. z o.o.
Course: Python Programming
Expertise of the teacher, his ability to solve ad-hoc problems given by the participants. A well prepared the scope of the syllabus.
UBS Business Solutions Poland Sp. z o.o.
Course: Python Programming
Training in a small group is ideal as more focus can be put on items of specific interest.
HSCIC
Course: Unit Testing with Python
I liked the customized, in-house file processing and data analysis.
Glycom A/S
Course: Data Analysis in Python using Pandas and Numpy
The case studies helped us understand how we can apply Python in the industry. Really appreciated the trainer's help during the exercises.
Rajiv Dhingra - TCS
Course: Python Programming
As we are PHP developers, he understood the situation and allowed us to slowly map things between. I liked the examples and the humor he added.
Soumya Tyagi - TCS
Course: Python Programming
I enjoyed the that we have used our own data as examples.
Glycom A/S
Course: Data Analysis in Python using Pandas and Numpy
the training is not presentation styled. We were coding with he trainer.
Bhutan Telecom
Course: Web Development with Django
I mostly enjoyed everything.
Thukten Dendup - Bhutan Telecom
Course: Web Development with Django
Its a new experience, a new framework and looking forward to do something using the lesson learnt in the classes.
Jigme - Bhutan Telecom
Course: Web Development with Django
The trainer was sharing real word experiences, it's nice to learn from real professional.
Fednot
Course: Python Programming
The trainer was excellent, He was always ready to answer my questions and share as much knowledge as he could.
Fahad Malalla - Tatweer Petroleum
Course: Advanced Python
1:1 very intensive but learnt a lot.
Karen Dyke - BT
Course: Python: Automate the Boring Stuff
I mostly enjoyed the subject.
Proximus
Course: Python Programming
The way the exercises were organized : all on own tempo and Antonio there to help you further.
Proximus
Course: Python Programming
I liked the sufficient and very detailed reading materials and examples (slides).
HC Consumer Finance Philippines, Inc.
Course: Python Programming
I genuinely liked the na.
HC Consumer Finance Philippines, Inc.
Course: Python Programming
The comprehensive knowledge of the guide to all our questions gave answers overwhelming my expectations ... The lecturer conducts great discussions ... He does not lack patience ...
Łukasz Matulewicz
Course: Python Programming
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Great knowledge of the lecturer, diversity of tools and practical approach to the topic
Magdalena Stupak
Course: Python Programming
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great knowledge of the trainer, how to translate
Renata Cylejowska
Course: Python Programming
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All like it
蒙 李
Course: Machine Learning Fundamentals with Python
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the trainer looked at and helped each person individually
Szymon Wolny
Course: Python Programming
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A set of exercises ideally suited to the subject. Exercises easy and "with a star"
Motorola Solutions Systems Polska Sp. z o.o
Course: Python Programming
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Good balance theory / exercises, adjusting the level of lectures to listeners less and more experienced, a very big plus for using Jupiter Notebook and showing the theory in practice. I also liked to collect anonymous feedback after the first part of the training the next day everything was prepared according to our suggestions and even though it was already very good, it was even better later :)
Motorola Solutions Systems Polska Sp. z o.o
Course: Python Programming
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commitment of the teacher, preparation, approach to listeners, willingness to explain all ambiguities
Małgorzata Konior
Course: Python Programming
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That the leader approaches everyone, even when he does not call for help and checks the level of the exercise.
Agnieszka Bielak
Course: Python Programming
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The trainer presented a very short theories about a given issue and we immediately went to practice. A nice way of hanging out cards, which gives the trainer information about how much time he has to spend on a given task, and who else has problems with the solution.
Motorola Solutions Systems Polska Sp. z o.o
Course: Python Programming
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That even if someone did not ask for it, but you could see that he was not moving forward with the task, Krzysztof came up and was able to advise skilfully
Motorola Solutions Systems Polska Sp. z o.o
Course: Python Programming
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The way of conducting, the exercises, all in all it all liked, I'm very happy that I came to such a trainer
Maksym Kolodiy
Course: Python Programming
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Real examples of exercises
Motorola Solutions Systems Polska Sp. z o.o
Course: Python Programming
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Availability of training materials (Jupyter), created on an ongoing basis updating the notebook depending on the questions that fell during the course. Dispelling doubts, answers to all questions.
Motorola Solutions Systems Polska Sp. z o.o
Course: Python Programming
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Accessibility and an interesting way of delivering teaching materials.
Motorola Solutions Systems Polska Sp. z o.o
Course: Python Programming
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Work on xlsx and csv files
Łukasz Olczyk
Course: Python: Automate the Boring Stuff
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All
MTU Aero Engines Polska Sp. z o. o.
Course: Python Programming
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Interesting issues
MTU Aero Engines Polska Sp. z o. o.
Course: Python Programming
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Variety of prepared issues and examples
MTU Aero Engines Polska Sp. z o. o.
Course: Python Programming
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emphasis on examples with encoding "on the projector" is definitely on + for Tom.
ADVA OPTICAL NETWORKING SP. ZO O.
Course: Advanced Python
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Very good approach to memorize/repeat the key topics. Very nice “warm-up” exercises.
Course: Python Programming
| Code | Name | Duration | Overview |
|---|---|---|---|
| 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. |
| drlpython | Deep Reinforcement Learning with Python | 21 hours | Deep Reinforcement Learning refers to the ability of an "artificial agent" to learn by trial-and-error and rewards-and-punishments. An artificial agent aims to emulate a human's ability to obtain and construct knowledge on its own, directly from raw inputs such as vision. To realize reinforcement learning, deep learning and neural networks are used. Reinforcement learning is different from machine learning and does not rely on supervised and unsupervised learning approaches. In this instructor-led, live training, participants will learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent. By the end of this training, participants will be able to: - Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning - Apply advanced Reinforcement Learning algorithms to solve real-world problems - Build a Deep Learning Agent Audience - Developers - Data Scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonbigdata | Analyzing Big Financial Data with Python | 35 hours | Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn how to use Python for quantitative finance. By the end of this training, participants will be able to: - Understand the fundamentals of Python programming - Use Python for financial applications including implementing mathematical techniques, stochastics, and statistics - Implement financial algorithms using performance Python Audience - Developers - Quantitative analysts Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| pythoncomputervision | Computer Vision with Python | 14 hours | Computer Vision is a field that involves automatically extracting, analyzing, and understanding useful information from digital media. Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn the basics of Computer Vision as they step through the creation of set of simple Computer Vision application using Python. By the end of this training, participants will be able to: - Understand the basics of Computer Vision - Use Python to implement Computer Vision tasks - Build their own face, object, and motion detection systems Audience - Python programmers interested in Computer Vision Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforbankingwithpython | Deep Learning for Banking (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for banking using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in banking - Use Python, Keras, and TensorFlow to create deep learning models for banking - Build their own deep learning credit risk model using Python Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| dlforfinancewithpython | Deep Learning for Finance (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for finance using Python as they step through the creation of a deep learning stock price prediction model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning - Learn the applications and uses of deep learning in finance - Use Python, Keras, and TensorFlow to create deep learning models for finance - Build their own deep learning stock price prediction model using Python Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| microservicespython | Building Microservices with Python | 7 hours | Microservices refer to an application architecture style that promotes the use of independent, self-contained programs. Python is a dynamic high-level programming language that is ideal for both scripting as welll as application development. Python's expansive library of open source tools and frameworks make it a practical choice for building microservices. In this instructor-led, live training, participants will learn the fundamentals of microservices as they step through the creation of a microservice using Python. By the end of this training, participants will be able to: - Understand the basics of building microservices - Learn how to use Python to build microservices - Learn how to use Docker to deploy Python based microservices Audience - Developers - Programmers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| pygis | Python for Geographic Information System (GIS) | 21 hours | A geographic information system (GIS) is a system designed to capture, store, manipulate, analyze, manage, and present spatial or geographic data. The acronym GIS is sometimes used for geographic information science (GIScience) to refer to the academic discipline that studies geographic information systems and is a large domain within the broader academic discipline of geoinformatics. The use of Python with GIS has substantially increased over the last two decades, particularly with the introduction of Python 2.0 series in 2000, which included many new programming features that made the language much easier to deploy. Since that time, Python has not only been utilized within commercial GIS such as products by Esri but also open source platforms, including as part of QGIS and GRASS. In fact, Python today is by far the most widely used language by GIS users and programmers. This program covers the usage of Python and its advance libraries like geopandas, pysal, bokeh and osmnx to implement your own GIS features. The program also covers introductory modules around ArcGIS API, and QGIS toolboox. |
| tableaupython | Tableau with Python | 14 hours | Tableau is a business intelligence and data visualization tool. Python is a widely used programming language which provides support for a wide variety of statistical and machine learning techniques. Tableau's data visualization power and Python's machine learning capabilities, when combined, help developers rapidly build advanced data analytics applications for various business use cases. In this instructor-led, live training, participants will learn how to combine Tableau and Python to carry out advanced analytics. Integration of Tableau and Python will be done via the TabPy API. By the end of this training, participants will be able to: - Integrate Tableau and Python using TabPy API - Use the integration of Tableau and Python to analyze complex business scenarios with few lines of Python code Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| iotpython | Programming for IoT with Python | 14 hours | Internet of Things (IoT) is a network infrastructure that connects physical objects and software applications wirelessly, allowing them to communicate with each other and exchange data via network communications, cloud computing, and data capture. Python is a high-level programming language recommended for IoT due to its clear syntax and large community support. In this instructor-led, live training, participants will learn how to program IoT solutions with Python. By the end of this training, participants will be able to: - Understand the fundamentals of IoT architecture - Learn the basics of using Raspberry Pi - Install and configure Python on Raspberry Pi - Learn the benefits of using Python in programming IoT systems - Build, test, deploy, and troubleshoot an IoT system using Python and Raspberry Pi Audience - Developers - Engineers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice Note - To request a customized training for this course, please contact us to arrange. |
| mlfinancepython | Machine Learning for Finance (with Python) | 21 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Python is a programming language famous for its clear syntax and readability. It offers an excellent collection of well-tested libraries and techniques for developing machine learning applications. In this instructor-led, live training, participants will learn how to apply machine learning techniques and tools for solving real-world problems in the finance industry. Participants first learn the key principles, then put their knowledge into practice by building their own machine learning models and using them to complete a number of team projects. By the end of this training, participants will be able to: - Understand the fundamental concepts in machine learning - Learn the applications and uses of machine learning in finance - Develop their own algorithmic trading strategy using machine learning with Python Audience - Developers - Data scientists Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| pythonexcel | Python for Excel | 14 hours | Python is a high-level programming language famous for its clear syntax and code readability. Excel is a spreadsheet application developed by Microsoft which is widely used in many industries. Adding Python to Excel makes it a powerful tool for data analytics. In this instructor-led, live training, participants will learn how to combine the capabilities of Python and Excel. By the end of this training, participants will be able to: - Install and configure packages for integrating Python and Excel - Read, write, and manipulate Excel files using Python - Call Python functions from Excel Audience - Developers - Programmers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice Note - To request a customized training for this course, please contact us to arrange. |
| chatbotpython | Building Chatbots in Python | 21 hours | ChatBots are computer programs that automatically simulate human responses via chat interfaces. ChatBots help organizations maximize their operations efficiency by providing easier and faster options for their user interactions. In this instructor-led, live training, participants will learn how to build chatbots in Python. By the end of this training, participants will be able to: - Understand the fundamentals of building chatbots - Build, test, deploy, and troubleshoot various chatbots using Python Audience - Developers Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice Note - To request a customized training for this course, please contact us to arrange. |
| dlfortelecomwithpython | Deep Learning for Telecom (with Python) | 28 hours | Machine learning is a branch of Artificial Intelligence wherein computers have the ability to learn without being explicitly programmed. Deep learning is a subfield of machine learning which uses methods based on learning data representations and structures such as neural networks. Python is a high-level programming language famous for its clear syntax and code readability. In this instructor-led, live training, participants will learn how to implement deep learning models for telecom using Python as they step through the creation of a deep learning credit risk model. By the end of this training, participants will be able to: - Understand the fundamental concepts of deep learning. - Learn the applications and uses of deep learning in telecom. - Use Python, Keras, and TensorFlow to create deep learning models for telecom. - Build their own deep learning customer churn prediction model using Python. Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| appaipy | Applied AI from Scratch in Python | 28 hours | This is a 4 day course introducing AI and it's application using the Python programming language. There is an option to have an additional day to undertake an AI project on completion of this course. |
| mongodbpython | MongoDB for Python Developers | 14 hours | This instructor-led, live training (onsite or remote) is aimed at developers wishing to learn how to use MongoDB as the database for Python their applications. By the end of this training, participants will be able to: - Install and configure MongoDB - Understand the difference between accessing a NoSQL document database and a traditional relational databases' (e.g., MySQL) - Query a MongoDB database from within Python - Create and write data to a MongoDB databasse - Understand MongoDB's "data processing pipeline" - Perform real-time analytics and statistical analysis - Generate reports for dashboarding - Implement exception handling in Python application Format of the Course: - Interactive lecture and discussion - Lots of exercises and practice - Hands-on implementation in a live-lab environment Course Customization Options: - To request a customized training for this course, please contact us to arrange. |
| dataminpython | Data Mining with Python | 14 hours | This instructor-led, live training (onsite or remote) is aimed at data analysts and data scientists who wish to implement more advanced data analytics techniques for data mining using Python. By the end of this training, participants will be able to: - Understand important areas of data mining, including association rule mining, text sentiment analysis, automatic text summarization, and data anomaly detection. - Compare and implement various strategies for solving real-world data mining problems. - Understand and interpret the results. Format of the Course - Interactive lecture and discussion. - Lots of exercises and practice. - Hands-on implementation in a live-lab environment. Course Customization Options - To request a customized training for this course, please contact us to arrange. |
| spacy | Natural Language Processing (NLP) with Python spaCy | 14 hours | This instructor-led, live training (onsite or remote) is aimed at developers and data scientists who wish to use spaCy to process very large volumes of text to find patterns and gain insights. By the end of this training, participants will be able to: - Install and configure spaCy. - Understand spaCy's approach to Natural Language Processing (NLP). - Extract patterns and obtain business insights from large-scale data sources. - Integrate the spaCy library with existing web and legacy applications. - Deploy spaCy to live production environments to predict human behavior. - Use spaCy to pre-process text for Deep Learning Format of the Course - Interactive lecture and discussion. - Lots of exercises and practice. - Hands-on implementation in a live-lab environment. Course Customization Options - To request a customized training for this course, please contact us to arrange. - To learn more about spaCy, please visit: https://spacy.io/ |
| pythonforMatlab | Python for Matlab Users | 14 hours | The Python programming language is becoming more and more popular among Matlab users due to its power and versatility as a data analysis tool as well as a general purpose language. This instructor-led, live training (onsite or remote) is aimed at Matlab users who wish to explore and or transition to Python for data analytics and visualization. By the end of this training, participants will be able to: - Install and configure a Python development environment. - Understand the differences and similarities between Matlab and Python syntax. - Use Python to obtain insights from various datasets. - Convert existing Matlab applications to Python. - Integrate Matlab and Python applications. Format of the Course - Interactive lecture and discussion. - Lots of exercises and practice. - Hands-on implementation in a live-lab environment. Course Customization Options - To request a customized training for this course, please contact us to arrange. |
| sparkpython | Python and Spark for Big Data (PySpark) | 21 hours | Python is a high-level programming language famous for its clear syntax and code readibility. Spark is a data processing engine used in querying, analyzing, and transforming big data. PySpark allows users to interface Spark with Python. In this instructor-led, live training, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises. By the end of this training, participants will be able to: - Learn how to use Spark with Python to analyze Big Data. - Work on exercises that mimic real world circumstances. - Use different tools and techniques for big data analysis using PySpark. Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| ooppython | Learn Object-Oriented Programming with Python | 14 hours | Object-Oriented Programming (OOP) is a programming paradigm based around the concept of objects. OOP is more data-focused rather than logic-focused. Python is a high-level programming language famous for its clear syntax and code readibility. In this instructor-led, live training, participants will learn how to get started with Object-Oriented Programming using Python. By the end of this training, participants will be able to: - Understand the fundamental concepts of Object-Oriented Programming - Understand the OOP syntax in Python - Write their own object-oriented program in Python Audience - Beginners who would like to learn about Object-Oriented Programming - Developers interested in learning OOP in Python - Python programmers interested in learning OOP Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| 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 to program. 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. |
| pythonautomation | Python: Automate the Boring Stuff | 14 hours | This instructor-led, live 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. Format of the course - Part lecture, part discussion, exercises and heavy hands-on practice |
| 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. |
| 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. |
| 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. |
| django | Web Development with Django | 21 hours | Django is a high-level Python Web framework that encourages rapid development and clean, pragmatic design. Audience This course is directed at developers and engineers seeking to incorporate Django in their projects |
| web2py | Web Development with Web2Py | 28 hours | Web2py is a python based free open source full-stack framework for rapid development of fast, scalable, secure and portable database-driven web-based applications. Audience This course is directed at Engineers and Developers using web2py as a framework for web development |
| datapyth | Data Analysis in Python using Pandas and Numpy | 14 hours | Pandas is a Python package that provides data structures for working with structured (tabular, multidimensional, potentially heterogeneous) and time series data. |
| 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 |
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