Online or onsite, 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 "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live Python training can be carried out locally on customer premises in Tuxtla Gutierrez or in NobleProg corporate training centers in Tuxtla Gutierrez.
NobleProg -- Your Local Training Provider
Tuxtla Gutiérrez - Santa Elena
Prolongacion del Anillo de Circunvalación Sur No. 248, Tuxtla Gutiérrez, mexico, 29060
It is equipped with everything you need to have a great experience. Our team will support you at all times.
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.
The aim of this course is to provide general 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.
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.
This instructor-led, live training in Tuxtla Gutierrez begins with a discussion of BDD and how the Behave framework can be used to carry out BDD testing for web applications. Participants are given ample opportunity to interact with the instructor and peers while implementing the concepts and tactics learned in this hands-on, practice-based lab environment.
By the end of this training, participants will have a firm understanding of BDD and Behave, as well as the necessary practice to implement these techniques and tools in real-world test scenarios.
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
In this instructor-led, live training in Tuxtla Gutierrez, 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.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at business analysts who wish to automate trade with algorithmic trading, Python, and R.
By the end of this training, participants will be able to:
Employ algorithms to buy and sell securities at specialized increments rapidly.
Reduce costs associated with trade using algorithmic trading.
Automatically monitor stock prices and place trades.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at data scientists and data analysts who wish to program in R and Python for outlier detection.
By the end of this training, participants will be able to:
Identify whether data is an anomaly or is an expected value.
Implement algorithms for anomaly detection.
Use various techniques and methods to detect anomalies.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at GIS analysts who wish to automate repetitive tasks in GIS processes.
By the end of this training, participants will be able to:
Build GIS applications using Python and ArcGIS tools.
Develop with the ArcGIS package ArcPy, using Python.
Apply the ArcGIS modules for map automation using object classes in Python.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at biologists who wish to use Biopython.
By the end of this training, participants will be able to:
Integrate biological concepts with information technologies to solve research problems.
Mine information from large datasets of biological origin.
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.
This instructor-led, live training Tuxtla Gutierrez (online or onsite) is aimed at developers or DevOps engineers who wish to build automation pipelines using CI/CD practices with Python.
By the end of this training, participants will be able to:
Set up the necessary development environment to start building CI/CD pipelines with Python.
Build automated pipelines for testing and publishing Python packages using Travis-CI.
Automate the deployment of containerized applications with Docker and Heroku.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at developers who wish to use CUDA to build Python applications that run in parallel on NVIDIA GPUs.
By the end of this training, participants will be able to:
Use the Numba compiler to accelerate Python applications running on NVIDIA GPUs.
Create, compile and launch custom CUDA kernels.
Manage GPU memory.
Convert a CPU based application into a GPU-accelerated application.
This instructor-led, live training (online or onsite) 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.
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
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
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at data scientists who wish to use TensorFlow to analyze potential fraud data.
By the end of this training, participants will be able to:
Create a fraud detection model in Python and TensorFlow.
Build linear regressions and linear regression models to predict fraud.
Develop an end-to-end AI application for analyzing fraud data.
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
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.
This instructor-led, live training in (online or onsite) is aimed at data scientists who wish to use GANs and variational autoencoders to generate new, synthetic instances of images, videos, and audio.
By the end of this training, participants will be able to:
Build a GAN using machine learning libraries in Python.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at engineers who wish to deploy a speedy, resource-light Gunicorn server to run Python web applications.
By the end of this training, participants will be able to:
Install and configure Gunicorn.
Understand the building blocks for deploying a Python web application
Explain Gunicorn's role and how it compares to Java's Servlet API.
Integrate Gunicorn with a variety of Python web frameworks.
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.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at data engineers, data scientists, and programmers who wish to use Apache Kafka features in data streaming with Python.
By the end of this training, participants will be able to use Apache Kafka to monitor and manage conditions in continuous data streams using Python programming.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at software engineers who wish to develop advanced deep learning neural-networks and model using Keras and Python.
By the end of this training, participants will be able to:
Apply deep learning with supervised or unsupervised learning methods.
Develop, train, and implement concurrent neural networks and recurrent neural networks.
Use Keras and Python to build deep learning models to solve problems involving images, text, sound, and more.
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at data scientists who wish to program in Python and R for KNIME.
By the end of this training, participants will be able to:
Plan, build, and deploy machine learning models in KNIME.
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
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 banking 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.
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
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
This instructor-led, live training in Tuxtla Gutierrez (online or onsite) is aimed at network engineers who wish to maintain, manage, and design computer networks with Python.
By the end of this training, participants will be able to:
Optimize and leverage Paramiko, Netmiko, Napalm, Telnet, and pyntc for network automation with Python.
Master multi-threading and multiprocessing in network automation.
Natural language generation (NLG) refers to the production of natural language text or speech by a computer.
In this instructor-led, live training, participants will learn how to use Python to produce high-quality natural language text by building their own NLG system from scratch. Case studies will also be examined and the relevant concepts will be applied to live lab projects for generating content.
By the end of this training, participants will be able to:
Use NLG to automatically generate content for various industries, from journalism, to real estate, to weather and sports reporting
Select and organize source content, plan sentences, and prepare a system for automatic generation of original content
Understand the NLG pipeline and apply the right techniques at each stage
Understand the architecture of a Natural Language Generation (NLG) system
Implement the most suitable algorithms and models for analysis and ordering
Pull data from publicly available data sources as well as curated databases to use as material for generated text
Replace manual and laborious writing processes with computer-generated, automated content creation
Audience
Developers
Data scientists
Format of the course
Part lecture, part discussion, exercises and heavy hands-on practice
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Testimonials (25)
The course was straight forward, intuitive, easy to follow, the exercises covered the subjects discussed beforehand.
Alexandru - BRD
Course - Python Programming Fundamentals
Lots of examples of different cases, and materials which will be useful in future as I try to apply lessons to my work applications. Also, it was great that there were tasks to do at home in between lessons, as it gives the opportunity to pick up on the parts of the previous lesson, with which I struggled. If I had a question during a lesson, the teacher would gladly help and explain the problem, and the teacher had good expertise on all questions that were asked.
Raivis - Gravity Team
Course - Python: Automate the Boring Stuff
The trainer showed that he has a good understanding of the subject.
Marino - EQUS - The University of Queensland
Course - Machine Learning with Python – 2 Days
very comprehensive in regards to doing data analysis using python
Mervin Lau - MINDEF
Course - Python for Data Analysis
Everything, great trainer.
Michał Rawicki
Course - Unit Testing with Python
Machine Translated
Very interactive with various examples, with a good progression in complexity between the start and the end of the training.
Jenny - Andheo
Course - GPU Programming with CUDA and Python
Knowledge, substantive content, contact with others, willingness to help even with trivial problems, ability to interest listeners.
Michał
Course - Python for Excel
Machine Translated
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
flexibility of approach to the client. the trainer was able to prepare issues that were of interest to the training participants.
Mirosław - CREDIT SUISSE (POLAND)
Course - Python Programming - 4 days
Machine Translated
Practical examples and wider context given.
James - Mitsubishi Electric R&D Centre Europe BV (MERCE-UK)
Course - IoT Programming with Python
I liked that it was practical. Loved to apply the theoretical knowledge with practical examples.
Aurelia-Adriana - Allianz Services Romania
Course - Python and Spark for Big Data (PySpark)
examples based on our data
Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
The fact that we were able to take with us most of the information/course/presentation/exercises done, so that we can look over them and perhaps redo what we didint understand first time or improve what we already did.
Raul Mihail Rat - Accenture Industrial SS
Course - Python, Spark, and Hadoop for Big Data
I'm looking forward to having a training again with Any, he was really good, I'm just a GIS guy, but Any made everything clear to me, he explained complex processes in layman's terms. Keep it up, thank you.
Lwazi Qhingana - South African National Roads Agency (SANRAL) SOC Ltd
Course - Python for Geographic Information System (GIS)
Plenty of examples - and the trainer willing to bend backwards to help us with topics we were weaker in.
Wei Lit Teoh - HP Singapore (Private) Ltd.
Course - Advanced Python - 4 Days
The accesibilit of the trainer and the ability to communicate very effective,
Ciprian Ilie - Institutul National de Sanatate Publica
Course - Programming for Biologists
Content and example. virtual computer is helpful (my version of anaconda does not have Dash library yet)
Jennifer Ni - AllianceBernstein
Course - Python with Plotly and Dash
Many different examples and topics has been covered, from basic investigation to login management and dynamic page management.
Daniele Tagliaferro - Creditsafe Italia Srl
Course - Web Scraping with Python
That it was applying real company data.
Trainer had a very good approach by making trainees participate and compete
Jimena Esquivel - Zakład Usługowy Hakoman Andrzej Cybulski
Course - Applied AI from Scratch in Python
Trainer was very knowlegable and very open to feedback on what pace to go through the content and the topics we covered. I gained alot from the training and feel like I now have a good grasp of image manipulation and some techniques for building a good training set for an image classification problem.
Anthea King - WesCEF
Course - Computer Vision with Python
Trainer was accommodative. And actually quite encouraging for me to take up the course.
Grace Goh - DBS Bank Ltd
Course - Python in Data Science
I genuinely enjoyed the lots of labs and practices.
Vivian Feng - Destination Canada
Course - Data Analysis with SQL, Python and Spotfire
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
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