Remote or local, instructor-led live Artificial Intelligence (AI) training courses demonstrate through hands-on practice how to implement AI solutions for solving real-world problems.
AI training is available as "remote live training" or "onsite live training". Remote live training is carried out by way of an interactive, remote desktop. Onsite live Artificial Intelligence (AI) training can be carried out locally on customer premises in Portugal or in NobleProg corporate training centers in Portugal.
NobleProg -- Your Local Training Provider
Testimonials
I enjoyed the advises given by the trainer about how to use the tools. This is something that can't be got from the internet and are very useful.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
It was easy to follow.
HP Printing and Computing Solutions, Sociedad Limitada Unipe
Course: Computer Vision with OpenCV
the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge
Course: Introduction to R with Time Series Analysis
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course: Advanced Deep Learning
It is one on one. I can ask a lot of question and also ask the trainner to repeat when I was not clear about some stuff.
Course: Insurtech: A Practical Introduction for Managers
The informal exchanges we had during the lectures really helped me deepen my understanding of the subject
Explore
Course: Deep Reinforcement Learning with Python
The trainer's patience
European Space Agency (ESA/ESTEC)
Course: Getting Started with Quantum Computing and Q#
It is showing many methods with pre prepared scripts- very nicely prepared materials & easy to traceback
Kamila Begej - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
I like that training was focused on examples and coding. I thought that it is impossible to pack so much content into three days of training, but I was wrong. Training covered many topics and everything was done in a very detailed manner (especially tuning of model's parameters - I didn't expected that there will be a time for this and I was gratly surprised).
Bartosz Rosiek - GE Medical Systems Polska Sp. Zoo
Course: Machine Learning – Data science
the last day. generation part
Accenture Inc
Course: Python for Natural Language Generation
The topics referring to NLG. The team was able to learn something new in the end with topics that were interesting but it was only in the last day. There were also more hands on activities than slides which was good.
Accenture Inc
Course: Python for Natural Language Generation
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
The remote classroom setting worked very well
Trimac Management Services LP
Course: Introduction to R with Time Series Analysis
Additional materials. Theoretical preparation.
ING Bank Śląski
Course: Using Computer Network ToolKit (CNTK)
Machine Translated
Great knowledge of the teacher as well as the willingness and ability to share it. Inspiration to search for applications of artificial intelligence algorithms
ING Bank Śląski
Course: Using Computer Network ToolKit (CNTK)
Machine Translated
I like examples to explain
AUO友达光电（苏州）有限公司
Course: OptaPlanner in Practice
Machine Translated
Human identification and circuit board bad point detection
王 春柱 - 中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Machine Translated
Demonstrate
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Machine Translated
About face area.
中移物联网
Course: Deep Learning for NLP (Natural Language Processing)
Alternation theory / practice effective!
CIRAD
Course: Introduction to Machine Learning with MATLAB
Machine Translated
Progressive presentation and application of methods
Aurélien Briffaz - CIRAD
Course: Introduction to Machine Learning with MATLAB
Machine Translated
Availability and adaptability, answers to questions
Jean-Michel MEOT - CIRAD
Course: Introduction to Machine Learning with MATLAB
Machine Translated
Issues discussed, exercises carried out (examples), atmosphere of training, contact with the trainer, location.
Wojskowe Zakłady Uzbrojenia S.A. w Grudziądzu
Course: Octave not only for programmers
Machine Translated
A lot of practical tips
Pawel Dawidowski - ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
A lot of information related to the implementation of solutions
Michał Smolana - ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
A multitude of practical tips and knowledge of the lecturer from a wide range of AI / IT / SQL / IoT issues.
ABB Sp. z o.o.
Course: Deep Learning with TensorFlow
Machine Translated
It is one on one. I can ask a lot of question and also ask the trainner to repeat when I was not clear about some stuff.
Course: Insurtech: A Practical Introduction for Managers
I like that it focuses more on the how-to of the different text summarization methods
Course: Text Summarization with Python
AI Course Outlines in Portugal
Course Name
Duration
Overview
Course Name
Duration
Overview
35 hours
Overview
This instructor-led, live training (onsite or remote) is aimed at professionals who wish to start a career in Data Science.
By the end of this training, participants will be able to:
- Install and configure Python and MySql.
- Understand what Data Science is and how it can add value to virtually any business.
- Learn the fundamentals of coding in Python
- Learn supervised and unsupervised Machine Learning techniques, and how to implement them 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.
By the end of this training, participants will be able to:
- Install and configure Python and MySql.
- Understand what Data Science is and how it can add value to virtually any business.
- Learn the fundamentals of coding in Python
- Learn supervised and unsupervised Machine Learning techniques, and how to implement them 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.
14 hours
Overview
This course covers AI (emphasizing Machine Learning and Deep Learning) in Automotive Industry. It helps to determine which technology can be (potentially) used in multiple situation in a car: from simple automation, image recognition to autonomous decision making.
14 hours
Overview
In this instructor-led, live training, we go over the principles of neural networks and use OpenNN to implement a sample application.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
Format of the course
- Lecture and discussion coupled with hands-on exercises.
21 hours
Overview
Course is dedicated for those who would like to know an alternative program to the commercial MATLAB package. The three-day training provides comprehensive information on moving around the environment and performing the OCTAVE package for data analysis and engineering calculations. The training recipients are beginners but also those who know the program and would like to systematize their knowledge and improve their skills. Knowledge of other programming languages is not required, but it will greatly facilitate the learners' acquisition of knowledge. The course will show you how to use the program in many practical examples.
28 hours
Overview
OpenCV (Open Source Computer Vision Library: http://opencv.org) is an open-source BSD-licensed library that includes several hundreds of computer vision algorithms.
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
Audience
This course is directed at engineers and architects seeking to utilize OpenCV for computer vision projects
14 hours
Overview
This instructor-led, live training in Portugal (onsite or remote) is aimed at software engineers who wish to program in Python with OpenCV 4 for deep learning.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
By the end of this training, participants will be able to:
- View, load, and classify images and videos using OpenCV 4.
- Implement deep learning in OpenCV 4 with TensorFlow and Keras.
- Run deep learning models and generate impactful reports from images and videos.
14 hours
Overview
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
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
14 hours
Overview
The Apache OpenNLP library is a machine learning based toolkit for processing natural language text. It supports the most common NLP tasks, such as language detection, tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing and coreference resolution.
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to create models for processing text based data using OpenNLP. Sample training data as well customized data sets will be used as the basis for the lab exercises.
By the end of this training, participants will be able to:
- Install and configure OpenNLP
- Download existing models as well as create their own
- Train the models on various sets of sample data
- Integrate OpenNLP with existing Java applications
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
7 hours
Overview
In this instructor-led, live training, participants will learn how to set up and use OpenNMT to carry out translation of various sample data sets. The course starts with an overview of neural networks as they apply to machine translation. Participants will carry out live exercises throughout the course to demonstrate their understanding of the concepts learned and get feedback from the instructor.
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice
By the end of this training, participants will have the knowledge and practice needed to implement a live OpenNMT solution.
Source and target language samples will be pre-arranged per the audience's requirements.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice
21 hours
Overview
This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.
21 hours
Overview
This classroom based training session will explore NLP techniques in conjunction with the application of AI and Robotics in business. Delegates will undertake computer based examples and case study solving exercises using Python
21 hours
Overview
PaddlePaddle (PArallel Distributed Deep LEarning) is a scalable deep learning platform developed by Baidu.
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
In this instructor-led, live training, participants will learn how to use PaddlePaddle to enable deep learning in their product and service applications.
By the end of this training, participants will be able to:
- Set up and configure PaddlePaddle
- Set up a Convolutional Neural Network (CNN) for image recognition and object detection
- Set up a Recurrent Neural Network (RNN) for sentiment analysis
- Set up deep learning on recommendation systems to help users find answers
- Predict click-through rates (CTR), classify large-scale image sets, perform optical character recognition(OCR), rank searches, detect computer viruses, and implement a recommendation system.
Audience
- Developers
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
Pattern Matching is a technique used to locate specified patterns within an image. It can be used to determine the existence of specified characteristics within a captured image, for example the expected label on a defective product in a factory line or the specified dimensions of a component. It is different from "Pattern Recognition" (which recognizes general patterns based on larger collections of related samples) in that it specifically dictates what we are looking for, then tells us whether the expected pattern exists or not.
Format of the Course
- This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
Format of the Course
- This course introduces the approaches, technologies and algorithms used in the field of pattern matching as it applies to Machine Vision.
21 hours
Overview
PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
Audience
This course is directed at developers and data scientists who want to create predictive engines for any machine learning task.
14 hours
Overview
R is an open-source free programming language for statistical computing, data analysis, and graphics. R is used by a growing number of managers and data analysts inside corporations and academia. R has a wide variety of packages for data mining.
14 hours
Overview
This instructor-led, live training in Portugal (onsite or remote) is aimed at data scientists who wish to use Pandas to preform predictive analysis with machine learning.
By the end of this training, participants will be able to:
- Perform data wrangling in Python.
- Conduct ETL operations for machine learning.
- Create data visualizations with Pandas
By the end of this training, participants will be able to:
- Perform data wrangling in Python.
- Conduct ETL operations for machine learning.
- Create data visualizations with Pandas
21 hours
Overview
In this instructor-led, live training in Portugal, 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.
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.
14 hours
Overview
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
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
14 hours
Overview
This classroom based training session will contain presentations and computer based examples and case study exercises to undertake with relevant neural and deep network libraries
21 hours
Overview
This course has been designed for people interested in extracting meaning from written English text, though the knowledge can be applied to other human languages as well.
The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...
For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
The course will cover how to make use of text written by humans, such as blog posts, tweets, etc...
For example, an analyst can set up an algorithm which will reach a conclusion automatically based on extensive data source.
21 hours
Overview
It is estimated that unstructured data accounts for more than 90 percent of all data, much of it in the form of text. Blog posts, tweets, social media, and other digital publications continuously add to this growing body of data.
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
This instructor-led, live course centers around extracting insights and meaning from this data. Utilizing the R Language and Natural Language Processing (NLP) libraries, we combine concepts and techniques from computer science, artificial intelligence, and computational linguistics to algorithmically understand the meaning behind text data. Data samples are available in various languages per customer requirements.
By the end of this training participants will be able to prepare data sets (large and small) from disparate sources, then apply the right algorithms to analyze and report on its significance.
Format of the Course
- Part lecture, part discussion, heavy hands-on practice, occasional tests to gauge understanding
35 hours
Overview
This instructor-led, live training in Portugal (onsite or remote) is aimed at engineers who wish to evaluate the approaches and tools available today to make an intelligent decision on the path forward in adopting MLOps within their organization.
By the end of this training, participants will be able to:
- Install and configure various MLOps frameworks and tools.
- Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
- Prepare, validate and version data for use by ML models.
- Understand the components of an ML Pipeline and the tools needed to build one.
- Experiment with different machine learning frameworks and servers for deploying to production.
- Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
By the end of this training, participants will be able to:
- Install and configure various MLOps frameworks and tools.
- Assemble the right kind of team with the right skills for constructing and supporting an MLOps system.
- Prepare, validate and version data for use by ML models.
- Understand the components of an ML Pipeline and the tools needed to build one.
- Experiment with different machine learning frameworks and servers for deploying to production.
- Operationalize the entire Machine Learning process so that it's reproduceable and maintainable.
21 hours
Overview
This instructor-led, live training (onsite or remote) is aimed at data scientists who wish to go beyond building ML models and optimize the ML model creation, tracking, and deployment process.
By the end of this training, participants will be able to:
- Install and configure MLflow and related ML libraries and frameworks.
- Appreciate the importance of trackability, reproducability and deployability of an ML model
- Deploy ML models to different public clouds, platforms, or on-premise servers.
- Scale the ML deployment process to accommodate multiple users collaborating on a project.
- Set up a central registry to experiment with, reproduce, and deploy ML models.
By the end of this training, participants will be able to:
- Install and configure MLflow and related ML libraries and frameworks.
- Appreciate the importance of trackability, reproducability and deployability of an ML model
- Deploy ML models to different public clouds, platforms, or on-premise servers.
- Scale the ML deployment process to accommodate multiple users collaborating on a project.
- Set up a central registry to experiment with, reproduce, and deploy ML models.
14 hours
Overview
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.
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.
14 hours
Overview
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.
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.
14 hours
Overview
The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform 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.
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.
7 hours
Overview
This training course is for people that would like to apply basic Machine Learning techniques in practical applications.
Audience
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
Audience
Data scientists and statisticians that have some familiarity with machine learning and know how to program R. The emphasis of this course is on the practical aspects of data/model preparation, execution, post hoc analysis and visualization. The purpose is to give a practical introduction to machine learning to participants interested in applying the methods at work
Sector specific examples are used to make the training relevant to the audience.
14 hours
Overview
In this instructor-led, live training, participants will learn how to use the iOS Machine Learning (ML) technology stack as they step through the creation and deployment of an iOS mobile app.
By the end of this training, participants will be able to:
- Create a mobile app capable of image processing, text analysis and speech recognition
- Access pre-trained ML models for integration into iOS apps
- Create a custom ML model
- Add Siri Voice support to iOS apps
- Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
- Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
By the end of this training, participants will be able to:
- Create a mobile app capable of image processing, text analysis and speech recognition
- Access pre-trained ML models for integration into iOS apps
- Create a custom ML model
- Add Siri Voice support to iOS apps
- Understand and use frameworks such as coreML, Vision, CoreGraphics, and GamePlayKit
- Use languages and tools such as Python, Keras, Caffee, Tensorflow, sci-kit learn, libsvm, Anaconda, and Spyder
Audience
- Developers
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
14 hours
Overview
This classroom based training session will explore machine learning techniques, with computer based examples and case study solving exercises using a relevant programme languauge
14 hours
Overview
This instructor-led, live training (onsite or remote) is aimed at machine learning engineers who wish to use Azure Machine Learning and Azure DevOps to facilitate MLOps practices.
By the end of this training, participants will be able to:
- Build reproducible workflows and machine learning models.
- Manage the machine learning lifecycle.
- Track and report model version history, assets, and more.
- Deploy production ready machine learning models anywhere.
By the end of this training, participants will be able to:
- Build reproducible workflows and machine learning models.
- Manage the machine learning lifecycle.
- Track and report model version history, assets, and more.
- Deploy production ready machine learning models anywhere.
Other countries
Consulting in Portugal
Online Artificial Intelligence courses, Weekend Artificial Intelligence (AI) courses, Evening Artificial Intelligence (AI) training, AI boot camp, AI instructor-led, Weekend AI training, Evening Artificial Intelligence (AI) courses, AI coaching, AI instructor, Artificial Intelligence trainer, Artificial Intelligence training courses, AI classes, Artificial Intelligence on-site, Artificial Intelligence private courses, Artificial Intelligence (AI) one on one training
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