Supervised, Unsupervised, and Reinforcement Learning
An Intuitive explanation of Supervised, Unsupervised, and Reinforcement learning along with the differences
Machine Learning(ML) is a subset of Artificial Intelligence(AI), defined as a computer's ability to learn from data by using algorithms to imitate intelligent human behavior of decision making and predictions.
There are three main groups of algorithms in ML:
- Supervised learning,
- Unsupervised learning
- Reinforcement learning(RL)
You are learning python using manuals and online tutorials by following the code examples. Supervised learning is where you learn python by understanding its features by practicing the examples that act as labeled data and then using the knowledge acquired to write python programs for unseen use cases.
Input and output data is provided to a Supervised machine model, so supervised learning is learning by example.
Supervised learning uses a labeled dataset, typically labeled by an external supervisor, subject matter expert(SME), or an algorithm/program. The dataset is split into training and test dataset for training and then validating the model. The supervised learned model is then used to generate predictions on previously unseen unlabeled data that belongs to the category of data the model was trained on.
Examples of Supervised Learning are Classification and Regression. Classification is used in applications like Image Classification and K- Nearest Neighbors for identifying customer churn. Regression algorithms are used to predict sales, home prices, etc.
A child playing with toys can arrange them by identifying patterns based on colors, shapes, sizes, or just based on their interests. The kid discovers new ways to cluster the toys without needing…