Online or onsite, instructor-led live Explainable AI (XAI) training courses demonstrate through interactive hands-on practice how to use explainable AI techniques to interpret, understand, and communicate the decision-making processes of AI models, enhancing transparency and trust in AI systems.
Explainable Artificial Intelligence (XAI) 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 Explainable AI (XAI) trainings in Indiana can be carried out locally on customer premises or in NobleProg corporate training centers.
NobleProg -- Your Local Training Provider
IN, Indianapolis - Lockerbie Marketplace
333 N. Alabama Street Suite 350, Indianapolis, United States, 46204
With its closeness to interstates and a dynamic global transportation infrastructure, as well as downtown amenities, the Lockerbie Marketplace office on N. Alabama Street offers great accessibility. The office occupies the third floor of the smart Sears Building in the heart of Indianapolis’s historic district. Lockerbie Square is where many immigrants settled in the United States. Indianpolis (Indy) is an international city in Marion County that’s home to more than 500 overseas companies. It also has many vibrant sporting events including motoring mainstay, the Indy 500. Indianapolis lays claim to being one of the least expensive large cities in the US. The property is located near Lockerbie Chatham Arch and the main commercial hub of Massachusetts Avenue.
This instructor-led, live training in Indiana (online or onsite) is aimed at intermediate-level professionals who wish to apply Explainable AI techniques to ensure fairness, transparency, and ethical AI systems.
By the end of this training, participants will be able to:
Understand the role of XAI in ethical AI systems.
Implement XAI techniques to detect and mitigate bias in AI models.
Ensure transparency in AI model decision-making processes.
Align AI development with ethical and regulatory standards.
This instructor-led, live training in Indiana (online or onsite) is aimed at advanced-level professionals who wish to explore state-of-the-art XAI techniques for deep learning models, with a focus on building interpretable AI systems.
By the end of this training, participants will be able to:
Understand the challenges of explainability in deep learning.
Implement advanced XAI techniques for neural networks.
Interpret decisions made by deep learning models.
Evaluate the trade-offs between performance and transparency.
This instructor-led, live training in Indiana (online or onsite) is aimed at advanced-level professionals who wish to enhance their understanding of XAI techniques for complex AI models.
By the end of this training, participants will be able to:
Implement state-of-the-art XAI techniques in AI models.
Interpret deep learning models and their decisions.
Apply advanced model-agnostic and model-specific explainability methods.
Address challenges related to AI transparency in complex systems.
This instructor-led, live training in Indiana (online or onsite) is aimed at intermediate-level to advanced-level professionals who wish to implement transparent AI systems in real-world applications.
By the end of this training, participants will be able to:
Understand the importance of transparency in AI models.
Implement advanced XAI techniques to interpret complex models.
Enhance model transparency using SHAP, LIME, and other tools.
Address ethical concerns and fairness in AI models.
This instructor-led, live training in Indiana (online or onsite) is aimed at beginner-level professionals who wish to learn the basics of Explainable AI and its role in developing responsible AI systems.
By the end of this training, participants will be able to:
Understand the fundamental concepts of Explainable AI.
Explore the importance of transparency and interpretability in AI models.
Learn basic techniques for making AI models explainable.
Apply XAI techniques to simple machine learning models.
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