Phoenix, AZ - Regus at 24th and Camelback

2375 East Camelback Road, Phoenix, United States, 85016

The office in Phoenix 24th at Camelback is located at the city most prestigious address and provides stunning views of Camelback Mountain and the downtown Phoenix skyline. Conveniently placed at the crossing of major transport links, it features a secure underground parking as well as a beautiful courtyard.

Phoenix, AZ - Central Ave

40 North Central Ave, Suite 1400, Pheonix, United States, 85004

The venue is located in the heart of Downtown Phoenix at Two Renaissance Square on North Central Avenue, easily reached by car via Highways 87, I‑10, or I‑17, with attached and nearby covered parking. From Phoenix Sky Harbor International Airport (PHX), a taxi or rideshare takes about 10–15 minutes via Sky Harbor Blvd and I‑10 West. Public transit is seamless: the Washington/Central Ave Metro Light Rail station is just steps away, and multiple bus routes service Central Avenue, making it highly accessible for attendees without a car.

Explore Our Courses

Large Language Models (LLMs) and Reinforcement Learning (RL)

 21 Hours

This instructor-led, live training in Phoenix (online or onsite) is aimed at intermediate-level data scientists who wish to gain a comprehensive understanding and practical skills in both Large Language Models (LLMs) and Reinforcement Learning (RL).

By the end of this training, participants will be able to:

  • Understand the components and functionality of transformer models.
  • Optimize and fine-tune LLMs for specific tasks and applications.
  • Understand the core principles and methodologies of reinforcement learning.
  • Learn how reinforcement learning techniques can enhance the performance of LLMs.
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Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

 14 Hours

This instructor-led, live training in Phoenix (online or onsite) is aimed at advanced-level machine learning engineers and AI researchers who wish to apply RLHF to fine-tune large AI models for superior performance, safety, and alignment.

By the end of this training, participants will be able to:

  • Understand the theoretical foundations of RLHF and why it is essential in modern AI development.
  • Implement reward models based on human feedback to guide reinforcement learning processes.
  • Fine-tune large language models using RLHF techniques to align outputs with human preferences.
  • Apply best practices for scaling RLHF workflows for production-grade AI systems.
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Reinforcement Learning with Google Colab

 28 Hours

This instructor-led, live training in Phoenix (online or onsite) is aimed at advanced-level professionals who wish to deepen their understanding of reinforcement learning and its practical applications in AI development using Google Colab.

By the end of this training, participants will be able to:

  • Understand the core concepts of reinforcement learning algorithms.
  • Implement reinforcement learning models using TensorFlow and OpenAI Gym.
  • Develop intelligent agents that learn through trial and error.
  • Optimize agents' performance using advanced techniques such as Q-learning and deep Q-networks (DQNs).
  • Train agents in simulated environments using OpenAI Gym.
  • Deploy reinforcement learning models for real-world applications.
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Deep Reinforcement Learning with Python

 21 Hours

This instructor-led, live training in Phoenix (online or onsite) is aimed at developers and data scientists who wish to learn the fundamentals of Deep Reinforcement Learning as they step through the creation of a Deep Learning Agent.

By the end of this training, participants will be able to:

  • Understand the key concepts behind Deep Reinforcement Learning and be able to distinguish it from Machine Learning.
  • Apply advanced Reinforcement Learning algorithms to solve real-world problems.
  • Build a Deep Learning Agent.
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Fundamentals of Reinforcement Learning

 21 Hours

This instructor-led, live training in Phoenix (online or onsite) is aimed at data scientists who wish to go beyond traditional machine learning approaches to teach a computer program to figure out things (solve problems) without the use of labeled data and big data sets.

By the end of this training, participants will be able to:

  • Install and apply the libraries and programming language needed to implement Reinforcement Learning.
  • Create a software agent that is capable of learning through feedback instead of through supervised learning.
  • Program an agent to solve problems where decision making is sequential and finite.
  • Apply knowledge to design software that can learn in a way similar to how humans learn.
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Upcoming Courses

Large Language Models (LLMs) and Reinforcement Learning (RL)

2025-09-22 9:30 am
21 hours
Phoenix, AZ - Central Ave
$ 4054 (Online)
$ 5254 (Classroom)

Fine-Tuning with Reinforcement Learning from Human Feedback (RLHF)

2025-10-06 9:30 am
14 hours
Phoenix, AZ - Regus at 24th and Camelback
$ 2703 (Online)
$ 3143 (Classroom)

Fundamentals of Reinforcement Learning

2025-10-20 9:30 am
21 hours
Phoenix, AZ - Central Ave
$ 4054 (Online)
$ 5254 (Classroom)

Fundamentals of Reinforcement Learning

2025-11-03 9:30 am
21 hours
Phoenix, AZ - Regus at 24th and Camelback
$ 4054 (Online)
$ 4714 (Classroom)

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