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Udemy - Reinforcement Learning Essentials and Classical Methods

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Udemy - Reinforcement Learning Essentials and Classical Methods (Size: 3.5 GB)
  1 -Introduction.mp4 51.3 MB
  1 -Monte Carlo Methods in RL.mp4 49.1 MB
  1 -Thompson Sampling.mp4 102.4 MB
  1 -What is Python.mp4 45.4 MB
  1 -What is SARSA.mp4 71.3 MB
  10 -Dictionaries and Advanced Data Structures.mp4 142.6 MB
  10 -Dynamic Programming - Python Code.mp4 50.7 MB
  11 -Dynamic Programming - Python Code Output.mp4 5.6 MB
  11 -Modules, Packages & Importing Libraries.mp4 137.7 MB
  12 -File Handling.mp4 89 MB
  12 -Policy Evaluation.mp4 147.5 MB
  13 -Exception Handling & Robust Code.mp4 139.4 MB
  13 -Iterative Policy Evaluation Algorithm with Python.mp4 62.5 MB
  14 -OOP.mp4 137.7 MB
  15 -Advanced List Operations & Comprehensions.mp4 94.2 MB
  16 -Visualization Basics.mp4 148.3 MB
  2 -Anaconda & Jupyter & Visual Studio Code.mp4 31.7 MB
  2 -Blackjack - Intro.mp4 33.4 MB
  2 -SARSA - Taxi Implementation.mp4 153.1 MB
  2 -Upper Confidence Bound (UCB) in Reinforcement Learning.mp4 65.8 MB
  2 -What's Reinforcement Learning.mp4 29.1 MB
  3 -Blackjack Python.mp4 157.8 MB
  3 -Components of Reinforcement Learning.mp4 18.2 MB
  3 -Google Colab.mp4 45.1 MB
  3 -Markov Decision Processes.mp4 36.4 MB
  4 -Blackjack Output.mp4 15.5 MB
  4 -Environment Setup.mp4 43.6 MB
  4 -Markov Decision Processes - Case.mp4 16.5 MB
  4 -Q-Learning Intro.mp4 60.2 MB
  5 -Frozen Lake.mp4 107.7 MB
  5 -Markov Decision Processes - Python.mp4 28.9 MB
  5 -Python Syntax & Basic Operations.mp4 258.7 MB
  6 -Data Structures Lists, Tuples, Sets.mp4 155.4 MB
  6 -Frozen Lake Python.mp4 237 MB
  6 -Markov Decision Processes Code Output.mp4 11.8 MB
  7 -Cliff Walking Python.mp4 216.6 MB
  7 -Control Structures & Looping.mp4 111.6 MB
  7 -Dynamic Programming Principles.mp4 16.1 MB
  8 -Dynamic Programming - Case.mp4 6.6 MB
  8 -Functions & Basic Functional Programming.mp4 143 MB
  9 -Dynamic Programming - Mathematical Model.mp4 14.9 MB
  9 -Intermediate Functions.mp4 86.5 MB
  Bonus Resources.txt 102.4 B
  Get Bonus Downloads Here.url 204.8 B
  ▲ 44 total files

Description


Reinforcement Learning Essentials and Classical Methods

https://WebToolTip.com

Published 8/2025
Created by Advancedor Academy
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Level: Beginner | Genre: eLearning | Language: English | Duration: 43 Lectures ( 9h 11m ) | Size: 3.5 GB

Learn the foundations of reinforcement learning through MDPs, dynamic programming, and Python examples.

What you'll learn
Understand the key components of reinforcement learning, including agents, environments, states, actions, rewards, and policies
Gain a clear understanding of Markov Decision Processes (MDPs) and how they form the foundation of RL problems
Apply dynamic programming techniques such as policy evaluation and value iteration using Python Explore model-free methods like Monte Carlo, SARSA, Q-Learning
See how RL problems are implemented and solved using environments like Blackjack, Taxi, Frozen Lake, and Cliff Walking

Requirements
No previous experience in machine learning or reinforcement learning is needed
A willingness to follow the logic behind RL step by step is more important than memorizing equations

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