Python II Courses Online

Live Instructor Led Online Training Python II courses is delivered using an interactive remote desktop! .

During the course each participant will be able to perform Python II exercises on their remote desktop provided by Qwikcourse.

How do I start learning Python II?

Select among the courses listed in the category that really interests you.

If you are interested in learning the course under this category, click the "Book" button and purchase the course. Select your preferred schedule at least 5 days ahead. You will receive an email confirmation and we will communicate with trainer of your selected course.

Python II Training



Kubeflow Fairing is a Python package that streamlines the process of building, training, and deploying machine learning (ML) models in a hybrid cloud environment. By using Kubeflow Fairing and adding a few lines of code, you can run your ML training job locally or in the cloud, directly from Python code or a Jupyter notebook. After your training job is complete, you can use Kubeflow Fairing to deploy your trained model as a prediction endpoint.

7 hours




ToSpcy is a python package which helps prepocessing dataset for model training in spaCy. It could convert labeled dataset into spaCy format.


from toSpcy.toSpacy import Convertor dataset=['When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously.','Tom is traveling in China'] myConvertor=Convertor() spacydata=myConvertor.toSpacyFormat(dataset) spacydata

[('When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously.', {'entities': [(5, 20, 'p'), (61, 67, 'o'), (71, 75, 'd')]}), ('Tom is traveling in China', {'entities': [(0, 3, 'PER'), (20, 25, 'GEO')]})]


You could also covert the tags into desired labels when instantiating your object by using "taglabels" - a dictionary of tags and corresponding labels: dic_taglabels={'p':'PERSON','o':'ORG'} myConvertor=Convertor(dic_taglabels) spacydata=myConvertor.toSpacyFormat(dataset) spacydata[0] ('When Sebastian Thrun started working on self-driving cars at Google in 2007, few people outside of the company took him seriously.', {'entities': [(5, 20, 'PERSON'), (61, 67, 'ORG'), (71, 75, 'd')]})

7 hours


Fundamentals of Europython2018



Advanced Python Training At EuroPython 2018

Francesco Pierfederici

If you have been using Python for some time already and want to reach new heights in your language mastery, this training session is for you!

Python has a number of features which are extremely powerful but, for some reason are not particularly well known in the community. This makes progressing in our Python knowledge quite hard after we reach an intermediate level. Fear not: this session has you covered! We will look at some advanced features of the Python language including properties, class decorators, the descriptor protocol, annotations, data classes and meta-classes. If time allows we will even delve into the abstract syntax tree (AST) itself. We will use Python 3.7 and strongly recommend that attendees install a reasonably recent version of Python 3 to make the most out of the training.

7 hours




TaskTiger is a Python task queue using Redis. (Interested in working on projects like this? Close is looking for great engineers to join our team) Features TaskTiger forks a subprocess for each task, This comes with several benefits: Memory leaks caused by tasks are avoided since the subprocess is terminated when the task is finished. A hard time limit can be set for each task, after which the task is killed if it hasn't completed. To ensure performance, any necessary Python modules can be preloaded in the parent process. TaskTiger has the option to avoid duplicate tasks in the task queue. In some cases it is desirable to combine multiple similar tasks. For example, imagine a task that indexes objects (e.g. to make them searchable). If an object is already present in the task queue and hasn't been processed yet, a unique queue will ensure that the indexing task doesn't have to do duplicate work.

7 hours


Is learning Python II hard?

In the field of Python II learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.

Is Python II a good field?

For now, there are tremendous work opportunities for various IT fields. Most of the courses in Python II is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.

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