difference between supervised and unsupervised machine learning

Machine Learning: Supervised and Unsupervised Learning

Machine learning is primarily concerned with the design and development of algorithms that enable the system to learn from historical data. It is based on the idea that machines can learn from past data, recognize patterns and make decisions using algorithms.

Machine learning algorithms are designed to learn and improve their performance automatically. It helps in discovering patterns in data. The goal is to learn from data on a specific task in order to maximize the machine’s performance on that task.

The goal is to increase accuracy, but it doesn’t care about success. It enables systems to learn new things from data. From data on a specific task, it is easy to maximize the machine’s performance on that task.

Machine learning is a subset of artificial intelligence that involves the use of algorithms and statistical models to allow a system to improve its performance on a specific task over time. This is achieved by providing the system with large amounts of data and allowing it to adjust its algorithms and models accordingly. In contrast, reinforcement learning is a type of machine learning that trains a system to make decisions in an environment to maximize a reward. This is usually done using a technique called trial and error, where the system repeatedly tries different actions and learns from the results.

Application Areas of Machine Learning are:

  • Financial Services
  • Marketing and Sales
  • Government
  • Healthcare
  • Transportation

Machine learning can be divided in to three types:

  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Leaning

Supervised Learning

Supervised learning is also known as associative learning, where the network is trained by feeding it with input and matching output patterns. It requires pairing each input vector with a destination vector that represents the desired output.

The input vector along with the corresponding target vector is called a training pair. There are two types of Supervised Learning categories:


  • Classification is a task of predicting a discrete class designation.
  • A classification algorithm can predict a continuous value, but the continuation value takes the form of a probability for a class label.
  • Classification Predictions can be rated based on accuracy.


  • Regression is the task of predicting continuous quality.
  • A regression algorithm can predict a discrete value, but the discrete value takes the form of an integer quantity.
  • Regression predictions can be evaluated using the mean squared error.

Unsupervised Learning

Unsupervised learning, an output unit is trained to respond to clusters of patterns within the output. In this training method, the output vectors of similar type are grouped without the use of training data to specify what a typical member of each group looks like or what group a member belongs to.

The unsupervised training does not carry a teacher, it requires certain guidelines to form groups. There are two types of unsupervised learning categories:


It’s a kind of unsupervised machine learning algorithm. As the name suggests, it is based on the grouping of the data set. Each set of grouped data contains analog matches.


Association is a rule-based machine competency to discover the probability of the co-occurrence of items in a collection.

Reinforcement Learning

Reinforcement learning is the study of how creatures and artificial systems can learn to optimize their gesture in the face of prizes and corrections. Reinforcement learning is a type of machine learning in which an agent learns to make decisions in an environment by taking actions and receiving rewards for those actions.

The goal of reinforcement learning is for the agent to maximize its accumulated reward over time by learning which actions produce the best results. This differs from other forms of machine learning, such as Supervised learning, where the model is trained on labeled data and learns to make predictions based on that data.

In reinforcement learning, the model learns through trial and error by examining different actions and observing the rewards that result from those actions. This allows the agent to adapt to changing environments and learn from their own experience.

Advantage of Reinforcement Learning are:

  • Maximizes Performance
  • Increase Behavior

Disadvantage of Reinforcement are:

  • Too much reinforcement can lead to overload of states which can diminish the results.
  • It only provides enough to meet up the minimum behavior.

Types of Reinforcement

  • Positive Reinforcement Learning
  • Negative Reinforcement Learning

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