Online or onsite, 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 "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Austin onsite live Artificial Intelligence (AI) trainings can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
TX, Austin - Littlefield Congress
106 E 6th St Suite 900, Austin, united states, 78701
The venue is located in the Littlefield Bvilding between the Capital One Cafe and the Due Forni Pizza and Wine.
This instructor-led, live training in Austin (online or onsite) is aimed at intermediate-level content creators, digital artists and media professionals who wish to learn how multimodal AI can be applied to various forms of content creation.By the end of this training, participants will be able to:
Use AI tools to enhance music and video production.
Generate unique visual art and designs with AI.
Create interactive multimedia experiences.
Understand the impact of AI on the creative industries.
This instructor-led, live training in Austin (online or onsite) is aimed at advanced-level robotics engineers and AI researchers who wish to utilize Multimodal AI for integrating various sensory data to create more autonomous and efficient robots that can see, hear, and touch.By the end of this training, participants will be able to:
Implement multimodal sensing in robotic systems.
Develop AI algorithms for sensor fusion and decision-making.
Create robots that can perform complex tasks in dynamic environments.
Address challenges in real-time data processing and actuation.
This instructor-led, live training in Austin (online or onsite) is aimed at intermediate-level UX/UI designers and front-end developers who wish to utilize Multimodal AI to design and implement user interfaces that can understand and process various forms of input.By the end of this training, participants will be able to:
Design multimodal interfaces that improve user engagement.
Integrate voice and visual recognition into web and mobile applications.
Utilize multimodal data to create adaptive and responsive UIs.
Understand the ethical considerations of user data collection and processing.
This instructor-led, live training in Austin (online or onsite) is aimed at intermediate-level AI researchers, data scientists, and machine learning engineers who wish to create intelligent systems that can process and interpret multimodal data.By the end of this training, participants will be able to:
Understand the principles of multimodal AI and its applications.
Implement data fusion techniques to combine different types of data.
Build and train models that can process visual, textual, and auditory information.
Evaluate the performance of multimodal AI systems.
Address ethical and privacy concerns related to multimodal data.
This instructor-led, live training in Austin (online or onsite) is aimed at beginner-level to intermediate-level software developers who wish to integrate AI coding assistants into their development workflow.By the end of this training, participants will be able to:
Understand the role and capabilities of AI coding assistants in software development.
Utilize various AI coding assistant tools to automate routine coding tasks.
Integrate AI coding assistants into their software development lifecycle.
Enhance their productivity and focus on more complex and creative programming tasks.
Address ethical considerations and responsible use of AI in software development.
This instructor-led, live training in Austin (online or onsite) is aimed at intermediate-level software professionals who wish to leverage AI and machine learning to improve efficiency and innovation in software development.By the end of this training, participants will be able to:
Understand the role of AI and machine learning in automating software development tasks.
Implement AI tools to generate code, tests, and documentation.
Apply AI techniques for code optimization, quality assurance, and debugging.
Integrate AI into the DevOps and CI/CD pipelines for improved deployment strategies.
Address ethical considerations and challenges in AI-augmented software engineering.
This instructor-led, live training in Austin (online or onsite) is aimed at beginner-level to intermediate-level IT professionals who wish to understand and implement AI TRiSM in their organizations.By the end of this training, participants will be able to:
Grasp the key concepts and importance of AI trust, risk, and security management.
Identify and mitigate risks associated with AI systems.
Implement security best practices for AI.
Understand regulatory compliance and ethical considerations for AI.
Develop strategies for effective AI governance and management.
This instructor-led, live training in Austin (online or onsite) is aimed at beginner-level to intermediate-level developers and architects who wish to learn the fundamentals of intelligent driving and how to apply them to real-world scenarios.By the end of this training, participants will be able to:
Explain the basic concepts and principles of AI and how it can be applied to driving.
Understand the architecture and components of intelligent driving systems.
Create and visualize a composite driving model from different design disciplines.
Communicate and annotate issues and feedback within the model.
Perform clash detection and resolution between driving scenarios.
This instructor-led, live training in Austin (online or onsite) is aimed at professionals who wish to learn and understand the concept of AI and how to use it effectively and responsibly.
By the end of this training, participants will be able to:
Learn the concept of Artificial Intelligence (AI).
Understand the limits and dangers of AI and use it responsibly.
Know how to effectively use AI in real-world scenarios.
This instructor-led, live training in Austin (online or onsite) is aimed at digital marketers, content creators, and developers who wish to understand the impact of AI on search engines, explore the capabilities of Bing AI, and learn about chatbot technologies and their applications.
By the end of this training, participants will be able to:
Understand the principles and benefits of Bing AI.
Identify the AI-powered features within Bing and their applications.
Utilize Bing AI to enhance search results and user experiences.
Evaluate the potential of AI in search engine optimization (SEO) and content creation.
Explore chatbot technologies and their applications, including Bing AI Chatbot.
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 Austin (online or onsite) is aimed at data scientists who wish to use IBM Cloud Pak to prepare data for use in AI solutions.
By the end of this training, participants will be able to:
Install and configure Cloud Pak for Data.
Unify the collection, organization and analysis of data.
Integrate Cloud Pak for Data with a variety of services to solve common business problems.
Implement workflows for collaborating with team members on the development of an AI solution.
AI-102 Designing and Implementing an Azure AI Solution is intended for software developers wanting to build AI infused applications that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. The course will use C# or Python as the programming language.
Audience Profile
Software engineers concerned with building, managing and deploying AI solutions that leverage Azure Cognitive Services, Azure Cognitive Search, and Microsoft Bot Framework. They are familiar with C# or Python and have knowledge on using REST-based APIs to build computer vision, language analysis, knowledge mining, intelligent search, and conversational AI solutions on Azure.
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.
This instructor-led, live training in Austin (online or onsite) is aimed at marketers who wish to use AI to improve improve digital marketing strategies through valuable customer insights.
By the end of this training, participants will be able to:
Leverage AI software to improve the way brands connect to users.
Use chatbots to optimize the user-experience.
Increase productivity and revenue through the automation of tasks.
This instructor-led, live training in Austin (online or onsite) is aimed at managers and business leaders who wish to learn about the fundamentals of artificial intelligence and manage AI projects for their organization.
By the end of this training, participants will be able to understand AI at a technical level and strategize using their organization’s data and resources to successfully manage AI projects.
This instructor-led, live training in Austin (online or onsite) is aimed at engineers who wish to program and create robots through basic AI methods.
By the end of this training, participants will be able to:
Implement filters (Kalman and particle) to enable the robot to locate moving objects in its environment.
Implement search algorithms and motion planning.
Implement PID controls to regulate a robot's movement within an environment.
Implement SLAM algorithms to enable a robot to map out an unknown environment.
In this instructor-led, live training in Austin (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 6-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
Understand the key concepts used in robotic technologies.
Understand and manage the interaction between software and hardware in a robotic system.
Understand and implement the software components that underpin robotics.
Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
Implement search algorithms and motion planning.
Implement PID controls to regulate a robot's movement within an environment.
Implement SLAM algorithms to enable a robot to map out an unknown environment.
Extend a robot's ability to perform complex tasks through Deep Learning.
Test and troubleshoot a robot in realistic scenarios.
In this instructor-led, live training in Austin (online or onsite), participants will learn the different technologies, frameworks and techniques for programming different types of robots to be used in the field of nuclear technology and environmental systems.
The 4-week course is held 5 days a week. Each day is 4-hours long and consists of lectures, discussions, and hands-on robot development in a live lab environment. Participants will complete various real-world projects applicable to their work in order to practice their acquired knowledge.
The target hardware for this course will be simulated in 3D through simulation software. The code will then be loaded onto physical hardware (Arduino or other) for final deployment testing. The ROS (Robot Operating System) open-source framework, C++ and Python will be used for programming the robots.
By the end of this training, participants will be able to:
Understand the key concepts used in robotic technologies.
Understand and manage the interaction between software and hardware in a robotic system.
Understand and implement the software components that underpin robotics.
Build and operate a simulated mechanical robot that can see, sense, process, navigate, and interact with humans through voice.
Understand the necessary elements of artificial intelligence (machine learning, deep learning, etc.) applicable to building a smart robot.
Implement filters (Kalman and Particle) to enable the robot to locate moving objects in its environment.
Implement search algorithms and motion planning.
Implement PID controls to regulate a robot's movement within an environment.
Implement SLAM algorithms to enable a robot to map out an unknown environment.
Test and troubleshoot a robot in realistic scenarios.
If you're a developer looking to expand your abilities and advance your career, the ChatGPT course for Developers is the perfect choice for you. This all-inclusive program will teach you how to utilize GPT-3, a cutting-edge language processing technology, to construct interactive chatbots that can comprehend and respond to natural language inputs.
With this course, you'll discover how to incorporate GPT-3 into your chatbot projects, and how to use its advanced features to offer users a smooth and natural conversation experience. Additionally, you'll learn how to customize GPT-3 to meet the specific requirements of your chatbot, and how to train it on custom datasets to enhance its performance.
This instructor-led, live training in Austin (online or onsite) is aimed at software testers who wish to have an AI driven software testing environment.
By the end of this training, participants will be able to:
Automate unit test generation and parameterization with AI.
Apply machine learning learning in a real world use-case.
Automate the generation and maintenance of API tests with AI.
Use machine learning methods to self-heal the execution of Selenium tests.
This course uses a practical approach to teaching OptaPlanner. It provides participants with the tools needed to perform the basic functions of this tool.
This instructor-led, live training in Austin (online or onsite) is aimed at technical persons who wish to set up or extend an RPA system with more intelligent capabilities.
By the end of this training, participants will be able to:
Install and configure UiPath IPA.
Enable robots to manage other robots.
Apply computer vision to locate screen objects with accuracy.
Enable robots that can detect language patterns and carry out sentiment analysis on unstructured content.
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.
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.
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Testimonials (25)
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
examples and exercises
Kamil
Course - Introduction to Data Science and AI using Python
Machine Translated
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
All the information presented
Jose Victor - si
Course - Artificial Intelligence (AI) for Managers
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)
All the examples used and the lecturing style was on point even for a begginer i was able to understand and the training was so patient and always willing to go extra mile when in need of assistance.
Mathipa Chepape - Vodacom
Course - Big Data Business Intelligence for Telecom and Communication Service Providers
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
It felt like we were going through directly relevant information at a good pace (i.e. no filler material)
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Introduction to the use of neural networks
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
The training definitely backfilled some of the gaps in my knowledge left by reading the OptaPlanner userguide.
It gave me a good broad understanding of how to approach using OptaPlanner in our projects going forward.
Terry Strachan - Exel Computer Systems plc
Course - OptaPlanner in Practice
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
The exercises.
Elena Velkova - CEED Bulgaria
Course - Predictive Modelling with R
Good detail on what R is used for and how to start using it right away
Hoss Shenassa - Trimac Management Services LP
Course - Introduction to R with Time Series Analysis
The content, as I found it very interesting and think it would help me in my final year at University.
Krishan - NBrown Group
Course - From Data to Decision with Big Data and Predictive Analytics
This is one of the best hands-on with exercises programming courses I have ever taken.
Laura Kahn
Course - Artificial Intelligence - the most applied stuff - Data Analysis + Distributed AI + NLP
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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