The purpose of this study is to predict which ad will be the most preferred by the customers over the fictitious ads clicked by the users.
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Updated
Sep 23, 2021 - Python
The purpose of this study is to predict which ad will be the most preferred by the customers over the fictitious ads clicked by the users.
A python implementation of an agent for ultimate tic-tac-toe using Monte Carlo Tree Search and Upper Confidential Bound
Real-Life Example for Machine Learning Projects (Python3) -Part-2
Reinforcement learning
A simple implementation of Reinforcement Learning using UCB in python.
An AI agent implemented using Monte Carlo Tree Search (MCTS) using Upper Confidence Bounds (UCT).
AI for the game "Connect Four". Available on PyPI.
A python based ML tool for CRT inspection & optimization
Implementation of greedy, E-greedy and Upper Confidence Bound (UCB) algorithm on the Multi-Armed-Bandit problem.
The easiest way to optimize Facebook Ads using Upper Confidence Bound Algorithm. 💻
Python implementation of UCB, EXP3 and Epsilon greedy algorithms
All codes, both created and optimized for best results from the SuperDataScience Course
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