Published 9/2025
Created by Advancedor Academy
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Intermediate | Genre: eLearning | Language: English | Duration: 10 Lectures ( 3h 10m ) | Size: 1.43 GB
Hands-on reinforcement learning with Python through real projects
What you’ll learn
Implement reinforcement learning projects in Python from start to finish.
Apply reinforcement learning to games, optimization, and real-world tasks
Build projects for recommendation systems, inventory, and resource management.
Use reinforcement learning methods like DQN, SAC, and simulated annealing.
Requirements
Basic knowledge of Python programming (variables, loops, functions).
Familiarity with libraries such as NumPy and Matplotlib is helpful.
Some exposure to machine learning concepts will make the course easier to follow, but it’s not strictly required.
Description
Reinforcement learning is one of the most exciting areas in machine learning, and the best way to learn it is by working on projects. This course is designed to give you practical, step-by-step experience with reinforcement learning using Python. Instead of only focusing on theory, we’ll build working solutions that you can run, test, and extend.We begin with a classic example, the Blackjack game, to introduce the core ideas of agents, rewards, and policies. From there, the course moves into optimization and decision-making problems. You’ll explore the Traveling Salesman Problem with simulated annealing, and then see how reinforcement learning can be applied in real applications such as recommendation systems, inventory management, and resource allocation.The course also covers more advanced use cases. We’ll look at CNC machining parameter optimization with Soft Actor-Critic (SAC), chemical batch process optimization with Deep Q-Networks (DQN), and network optimization in two parts. In the final section, we’ll introduce safe reinforcement learning, which has become increasingly important in real-world systems where safety constraints must be respected.By the end of this course, you will have implemented a range of reinforcement learning projects in Python and gained a deeper understanding of how these methods can be applied across different domains. The course is suitable for learners who already have some Python experience and want to strengthen their knowledge of machine learning through practice.If you’re looking for a hands-on way to learn reinforcement learning and see it applied in diverse problems, this course will guide you through each step.
Password/解压密码www.tbtos.com
转载请注明：0daytown » Reinforcement Learning Projects with Python