AI, ML, DL and Everything in Between



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Tasks of Machine Learning

Source: AlmaBetter

Supervised Learning

Whenever the main problem at hand is to map the given data to an outcome or a label which could also be called a dependent variable, then it is a Supervised Learning Problem.

  1. If the dependent variable at hand is of a continuous type, then it is a Regression Problem. Forecasting the price of a Car based upon a list of its given features could be an example of a Regression Problem.
  2. If the dependent variable is of categorical type, then the problem at hand is a Classification Problem. Classifying animal pictures as ‘Dog picture’ or ‘not a dog picture’ could be an example of a Classification Problem

Unsupervised Learning

When, a target label/dependent variable is absent from the problem, then it is an unsupervised Learning problem. Here, we are not required to determine labels for a given data, but we are required to find out clusters in our data. A cluster is, nothing but, a compartment/group or a part of our data which shows similar characteristics. Based on what action people take when a particular recommendation is shown to them on Netflix, they can be clustered into different types of users. Going forward, Netflix could create tailor made recommendations for each user type. This is how powerful Clustering Algorithms are when it comes to understanding customer user behaviour.

Reinforcement Learning

Source: AlmaBetter

A reinforcement learning model, has an agent which takes a certain action in an environment with a certain initial state, S0. For the action taken by the agent, the environment provides it feedback or a Reward. After receiving the feedback, the Agent changes the state to, say S1. With an optimized state, the agent continues to produce action till the best possible optimization is not achieved for the state of the algorithm.

AI/ML

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