Online or onsite, instructor-led live Machine Learning (ML) training courses demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries. NobleProg ML courses cover different programming languages and frameworks, including Python, R language and Matlab. Machine Learning courses are offered for a number of industry applications, including Finance, Banking and Insurance and cover the fundamentals of Machine Learning as well as more advanced approaches such as Deep Learning.
Machine Learning 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 Machine Learning training can be carried out locally on customer premises in Ciudad Juárez or in NobleProg corporate training centers in Ciudad Juárez.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at intermediate-level developers who wish to learn how to use generative AI with LLMs for various tasks and domains.By the end of this training, participants will be able to:
Explain what generative AI is and how it works.
Describe the transformer architecture that powers LLMs.
Use empirical scaling laws to optimize LLMs for different tasks and constraints.
Apply state-of-the-art tools and methods to train, fine-tune, and deploy LLMs.
Discuss the opportunities and risks of generative AI for society and business.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at beginner-level to intermediate-level data scientists and software developers who wish to learn about Milvus and its practical applications in various AI scenarios.By the end of this training, participants will be able to:
Understand the architecture and features of Milvus.
Implement vector databases in different AI applications.
Perform similarity searches with high accuracy and speed.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at beginner to intermediate-level developers and data scientists who wish to learn the basics of LightGBM and explore advanced techniques.
By the end of this training, participants will be able to:
Install and configure LightGBM.
Understand the theory behind gradient boosting and decision tree algorithms
Use LightGBM for basic and advanced machine learning tasks.
Implement advanced techniques such as feature engineering, hyperparameter tuning, and model interpretation.
Integrate LightGBM with other machine learning frameworks.
This instructor-led, live training in (online or onsite) is aimed at data scientists, machine learning engineers, NLP researchers, and AI enthusiasts who wish to understand the inner workings of GPT models, explore the capabilities of GPT-3 and GPT-4, and learn how to leverage these models for their NLP tasks.
By the end of this training, participants will be able to:
Understand the key concepts and principles behind Generative Pre-trained Transformers.
Comprehend the architecture and training process of GPT models.
Utilize GPT-3 for tasks such as text generation, completion, and translation.
Explore the latest advancements in GPT-4 and its potential applications.
Apply GPT models to their own NLP projects and tasks.
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 course has been created for managers, solutions architects, innovation officers, CTOs, software architects and anyone who is interested in an overview of applied artificial intelligence and the nearest forecast for its development.
What will cities look like in the future? How can Artificial Intelligence (AI) be used to improve city planning? How can AI be used to make cities more efficient, livable, safer and environmentally friendly?
In this instructor-led, live training (onsite or remote), we examine the various technologies that make up AI, as well as the skill sets and mental framework required to put them to use for city planning. We also cover tools and approaches for gathering and organizing relevant data for use in AI, including data mining.
Audience
City planners
Architects
Developers
Transportation officials
Format of the Course
Part lecture, part discussion, and a series of interactive exercises.
Note
To request a customized training for this course, please contact us to arrange.
AI is a collection of technologies for building intelligent systems capable of understanding data and the activities surrounding the data to make "intelligent decisions". For Telecom providers, building applications and services that make use of AI could open the door for improved operations and servicing in areas such as maintenance and network optimization.
In this course we examine the various technologies that make up AI and the skill sets required to put them to use. Throughout the course, we examine AI's specific applications within the Telecom industry.
Audience
Linear algebra is a branch of mathematics that deals with vectors, matrices, and linear transforms. Knowledge of linear algebra helps engineers and developers improve their machine learning capabilities. Understanding linear algebra concepts allows them to better understand the principles behind machine learning techniques and thus solve problems faster.
In this instructor-led, live training, participants will learn the fundamentals of linear algebra as they step through solving a machine learning problem using linear algebra methods.
By the end of this training, participants will be able to:
Understand fundamental linear algebra concepts
Learn the linear algebra skills needed for machine learning
Use linear algebra structures and concepts when working with data, images, algorithms, etc.
Solve a machine learning problem using linear algebra
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 Ciudad Juárez (online or onsite) is aimed at engineers who wish to use Azure ML's drag-and-drop platform to deploy Machine Learning workloads without having to purchase software and hardware and without having to worry about maintenance and deployment.
By the end of this training, participants will be able to:
Write highly-accurate machine learning models using Python, R, or zero-code tools.
Leverage Azure's available data sets and algorithms to train and track machine learning and deep-learning models.
Use Azures interactive workspace to collaboratively develop ML models.
Choose from different Azure-supported ML frameworks such as PyTorch, TensorFlow, and scikit-learn.
Artificial Neural Network is a computational data model used in the development of Artificial Intelligence (AI) systems capable of performing "intelligent" tasks. Neural Networks are commonly used in Machine Learning (ML) applications, which are themselves one implementation of AI. Deep Learning is a subset of ML.
This is a 4 day course introducing AI and it's application. There is an option to have an additional day to undertake an AI project on completion of this course.
This training course is for people that would like to apply Machine Learning in practical applications.
Audience
This course is for data scientists and statisticians that have some familiarity with statistics and know how to program R (or Python or other chosen language). 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 practical applications 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.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at data scientists and developers who wish to create and train machine learning models for deployment into production-ready hosting environments.
By the end of this training, participants will be able to:
Use notebook instances to prepare and upload data for training.
Train machine learning models using training datasets.
Deploy trained models to an endpoint to create predictions.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at data scientists who wish to use Azure Machine Learning to build end-to-end machine learning models for predictive analysis.
By the end of this training, participants will be able to:
Build machine learning models with zero programming experience.
Create predictive algorithms with Azure Machine Learning.
Deploy production ready machine learning algorithms.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at web developers who wish to create machine learning models with Core ML for iOS 11 and macOS High Sierra development.
By the end of this training, participants will be able to:
Build AI driven applications that use machine learning.
Implement machine learning models that classify images.
Use the Core ML API for supporting custom workflows and advanced use cases
Analyze natural language text with machine learning models.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at engineers, data scientists, and data analysts who wish to use Dataiku DSS for the development of machine learning pipelines, and leverage AI processes in generating strategic organizational initiatives.
By the end of this training, participants will be able to:
Install and configure Dataiku DSS upon their preferred operating system.
Understand AI/ML concepts and principles that are fundamental to Dataiku features.
Create and implement their own data visualization codes in Dataiku DSS projects.
Deploy ML models and pipelines into production environments built around Dataiku DSS.
Optimize the agility and flexibility of data analysis methods of their enterprise applications.
Utilize Dataiku DSS to secure and manage how data flows through further business systems.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at researchers and developers who wish to install, set up, customize, and use the DeepMind Lab platform to develop general artificial intelligence and machine learning systems.
By the end of this training, participants will be able to:
Customize DeepMind Lab to build and run an environment that suits learning and training needs.
Use DeepMind Lab's 3D simulation environment to train learning agents in a first-person viewpoint.
Facilitate agent evaluation to develop intelligence in a 3D game-like world.
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.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at engineers who wish to apply feature engineering techniques to better process data and achieve obtain better machine learning models.
By the end of this training, participants will be able to:
Set up an optimal development environment, including all needed Python packages.
Obtain important insights by analyzing the features of a data set.
Optimize machine learning models through adaptation of the raw data itself.
Clean and transform data sets in preparation for machine learning.
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 course is for people that already have a background in data science and statistics. The explanations given are designed to either serve as a reminder to those that are already familiar with the concepts or inform those with a suitable background.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow on premise and in the cloud using AWS EKS (Elastic Kubernetes Service).
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at developers and data scientists who wish to build, deploy, and manage machine learning workflows on Kubernetes.
By the end of this training, participants will be able to:
Install and configure Kubeflow on premise and in the cloud.
Build, deploy, and manage ML workflows based on Docker containers and Kubernetes.
Run entire machine learning pipelines on diverse architectures and cloud environments.
Using Kubeflow to spawn and manage Jupyter notebooks.
Build ML training, hyperparameter tuning, and serving workloads across multiple platforms.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at data scientists who wish to use machine learning in Mathematica for data analysis.
By the end of this training, participants will be able to:
Build and train machine learning models.
Import and prepare data for machine learning.
Separate training data from test data.
Explore deep learning and neural network applications in data analysis.
This instructor-led, live training in Ciudad Juárez (online or onsite) is aimed at engineers who wish to learn about the applicability of artificial intelligence to mechatronic systems.
By the end of this training, participants will be able to:
Gain an overview of artificial intelligence, machine learning, and computational intelligence.
Understand the concepts of neural networks and different learning methods.
Choose artificial intelligence approaches effectively for real-life problems.
Implement AI applications in mechatronic engineering.
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
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Testimonials (24)
We had an overview about Machine Learning, Neural Networks, AI with practical examples.
Catalin - DB Global Technology SRL
Course - Machine Learning and Deep Learning
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
Keeping it short and simple.
Creating intuition and visual models around the concepts (decision tree graph, linear equations, calculating y_pred manually to prove how the model works).
Nicolae - DB Global Technology
Course - Machine Learning
Interesting knowledge
Gabriel - MINDEF
Course - Machine Learning with Python – 4 Days
examples based on our data
Witold - P4 Sp. z o.o.
Course - Deep Learning for Telecom (with Python)
Fantastic training, one of the best I have ever attended! The moderator, Rafal, provided excellent answers to the issues raised and explained all the methods very thoroughly. JestI am very satisfied and will gladly take advantage of the training conducted by this trainer again.
Darek Paszkowski - Orange Szkolenia Sp. z o.o.
Machine Translated
The details and the presentation style.
Cristian Mititean - Accenture Industrial SS
Course - Azure Machine Learning (AML)
The structure from first principles, to case studies, to application.
Margaret Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to Deep Learning
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
Los ejemplos y la paciencia del instructor
José Emilio Sánchez García - Universidad Autónoma Indígena de México
Course - Natural Language Processing with TensorFlow
The way of transferring knowledge and the knowledge of the trainer.
Jakub Rękas - Bitcomp Sp. z o.o.
Course - Machine Learning on iOS
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
Guillaume Gautier - OLEA MEDICAL | Improved diagnosis for life™
Course - Kubeflow
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
The enthusiasm to the topic. The examples he made an he explained it very well. Sympatic. A little to detailed for beginners. For managers, it could be more abstract in fewer days. But it was designed to fit and we had a good alignment in advance.
Benedikt Chiandetti - HDI Deutschland Bancassurance Kundenservice GmbH
Course - Machine Learning Concepts for Entrepreneurs and Managers
Convolution filter
Francesco Ferrara
Course - Introduction to Machine Learning
Tomasz really know the information well and the course was well paced.
Raju Krishnamurthy - Google
Course - TensorFlow Extended (TFX)
Even with having to miss a day due to customer meetings, I feel I have a much clearer understanding of the processes and techniques used in Machine Learning and when I would use one approach over another. Our challenge now is to practice what we have learned and start to apply it to our problem domain
Richard Blewett - Rock Solid Knowledge Ltd
Course - Machine Learning – Data science
I was benefit from the passion to teach and focusing on making thing sensible.
Zaher Sharifi - GOSI
Course - Advanced Deep Learning
In-depth coverage of machine learning topics, particularly neural networks. Demystified a lot of the topic.
Sacha Nandlall
Course - Python for Advanced Machine Learning
Very flexible.
Frank Ueltzhöffer
Course - Artificial Neural Networks, Machine Learning and Deep Thinking
Very updated approach or CPI (tensor flow, era, learn) to do machine learning.
Paul Lee
Course - TensorFlow for Image Recognition
I liked the opportunities to ask questions and get more in depth explanations of the theory.
Sharon Ruane
Course - Neural Networks Fundamentals using TensorFlow as Example
The trainer was so knowledgeable and included areas I was interested in.
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