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Types of Machine Learning

Machine Learning algorithms are generally categorized based on how they learn. Let's break down the three main types!

1. Supervised Learning​

In Supervised Learning, the algorithm learns from labeled data. This means the dataset includes both the input features (X) and the correct answer/target (y).

Goal: Learn a mapping from inputs to outputs so that the model can predict the output for unseen data.

Sub-types:​

  • Regression: Predicting a continuous numerical value (e.g., predicting daily ice cream sales for Bigkart).
  • Classification: Predicting a discrete category/class (e.g., predicting if a user will buy a Premium Ice Cream Cake).

2. Unsupervised Learning​

In Unsupervised Learning, the algorithm learns from unlabeled data. The dataset only has inputs (X), with no corresponding output/target (y). The model's job is to find hidden structure or patterns within the data on its own.

Goal: Discover the underlying structure or distribution in the data.

Sub-types:​

  • Clustering: Grouping similar data points together (e.g., segmenting Bigkart customers based on their favorite ice cream flavors).
  • Dimensionality Reduction: Reducing the number of random variables under consideration by obtaining a set of principal variables (e.g., PCA for visualization).

3. Reinforcement Learning​

Reinforcement Learning (RL) is about training an agent to make a sequence of decisions in an environment to maximize a cumulative reward.

Instead of being told the explicit right answer, the agent learns by trial and error, receiving positive or negative reinforcement (rewards or penalties) based on its actions.

Examples:

  • Training an AI to play Chess or Go.
  • Self-driving cars navigating traffic.
  • Robotics learning to walk.