Flowood, MS – Regus at Market Street

232 Market Street, Flowood, United States, 39232

The venue is centrally located at Market Street Flowood, just off US‑25/Lakeland Drive and Old Fannin Road, with plentiful free on-site and nearby municipal parking. From Jackson‑Medgar Wiley Evers International Airport (JAN), about 10 miles northwest, a taxi or rideshare takes around 15 minutes via I‑55 North and Lakeland Drive. Public transit is available via JATRAN buses serving Lakeland Drive with stops just steps from the entrance, making it accessible even without a car. The pedestrian-friendly plaza also includes shaded seating and walking paths connecting retail and dining options.

Gulfport, MS

1600 E Beach Blvd, Gulfport, United States, 39501

The venue is conveniently accessible by car via US‑90/Beach Boulevard, with on-site parking available for a daily fee. For those arriving by air, Gulfport–Biloxi International Airport (GPT) is just a short 5-minute drive away, approximately 5 miles via East Beach Boulevard. Public transportation is also an option, with Coast Transit Authority routes serving the area and the Gulfport Amtrak Station located about 0.7 miles from the venue. Rideshare services and local shuttles provide additional convenient transportation options.

Jackson, MS - Regus at East Capitol Street

317 East Capitol Street, Jackson, United States, 39201

The venue is conveniently accessible by car via I‑55 and I‑20, with public parking options available near the Capitol area. For those using public transportation, several Capital Area Transit System (CATS) bus lines stop along East Capitol Street, providing easy access to the venue. Travelers arriving at Jackson–Medgar Wiley Evers International Airport (JAN) can reach the location in approximately 15 minutes by car, taking I‑55 North and East Capitol Street for a quick 10-mile drive.

Explore Our Courses

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

 21 Hours

This instructor-led, live training in Mississippi (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 Mississippi (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 Mississippi (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 Mississippi (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 Mississippi (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
Flowood, MS – Regus at Market Street
$ 4054 (Online)
$ 4054 (Classroom)

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

2025-10-06 9:30 am
14 hours
Flowood, MS – Regus at Market Street
$ 2703 (Online)
$ 2703 (Classroom)

Fundamentals of Reinforcement Learning

2025-10-20 9:30 am
21 hours
Flowood, MS – Regus at Market Street
$ 4054 (Online)
$ 4054 (Classroom)

Fundamentals of Reinforcement Learning

2025-11-03 9:30 am
21 hours
Flowood, MS – Regus at Market Street
$ 4054 (Online)
$ 4054 (Classroom)

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