An Introduction to Machine Learning | The Complete Guide
Data Preprocessing for Machine Learning | Apply All the Steps in Python
Learn Simple Linear Regression in the Hard Way(with Python Code)
Multiple Linear Regression in Python (The Ultimate Guide)
Polynomial Regression in Two Minutes (with Python Code)
Support Vector Regression Made Easy(with Python Code)
Decision Tree Regression Made Easy (with Python Code)
Random Forest Regression in 4 Steps(with Python Code)
4 Best Metrics for Evaluating Regression Model Performance
A Beginners Guide to Logistic Regression(with Example Python Code)
K-Nearest Neighbor in 4 Steps(Code with Python & R)
Support Vector Machine(SVM) Made Easy with Python
Kernel SVM for Dummies(with Python Code)
Naive Bayes Classification Just in 3 Steps(with Python Code)
Decision Tree Classification for Dummies(with Python Code)
Random forest Classification
Evaluating Classification Model performance
A Simple Explanation of K-means Clustering in Python
Hierarchical Clustering
Association Rule Learning | Apriori
Eclat Intuition
Reinforcement Learning
Upper Confidence Bound (UCB) Algortihm: Solving the Multi-Armed Bandit Problem
Thompson Sampling Intuition
Natural Language Processing
Deep Learning
Artificial Neural Networks
Principal Component Analysis
Linear Discriminant Analysis (LDA)
Kernel PCA
Model Selection & Boosting
K-fold Cross Validation in Python | Master this State of the Art Model Evaluation Technique
Convolution Neural Network
Dimensionality Reduction

Reinforcement Learning | Machine Learning

Reinforcement Learning: Reinforcement Learning is a branch of Machine Learning, also called Online Learning. It is used to solve interacting problems where the data observed up to time t is considered to decide which action to take at time t + 1. It is also used for Artificial Intelligence when training machines to perform tasks such as walking. Desired outcomes provide the AI with reward, undesired with punishment. Machines learn through trial and error.

Reinforcement Learning is learning how to act in order to maximize a numerical reward.

In this part, you will understand and learn how to implement the following Reinforcement Learning models:

  1. Upper Confidence Bound (UCB)

  2. Thompson Sampling

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