Introduction to machine learning

Machine Learning machine-learning superviced-machine-learning unsuperviced-machine-learning reinforcement-learning

This introduce about type of machine learning algorithams. The machine learning is mostly used in every area of industry now Eg:, 

  1. Google assistence
  2. Automated driving cars
  3. Google search engine to predict what we expect on first
  4. Auto suggestion in development IDE
  5. Face recognization systems
  6. Most of the medical equipments etc

Actually the machine learning is the subset of Artificial Intelligents(AI). And there are maily three types of machine learning.

  1. Superviced Learning
  2. Unsuperviced Learning
  3. Reinforcement learning

So we can go through the each one of them

Superviced Learning

  1. Supervised learning is one of the most basic types of machine learning.
  2. In this type, the machine learning algorithm is trained on labeled data. Even though the data needs to be labeled accurately for this method to work, supervised learning is extremely powerful when used in the right circumstances.
  3. ML system learn how to combine input to produce useful prediction on never before seen data
  4. And this can be achieved by training models. 
  5. At the end of the training, the algorithm has an idea of how the data works and the relationship between the input and the output.
  6. The supervised machine learning algorithms will continue to improve even after being deployed, discovering new patterns and relationships as it trains itself on new data.

Unsuperviced Learning

  1. Unsupervised machine learning holds the advantage of being able to work with unlabeled data
  2. This means that human labor is not required to make the dataset machine-readable, allowing much larger datasets to be worked on by the program.
  3. unsupervised learning does not have labels to work off
  4. Relationships between data points are perceived by the algorithm in an abstract manner, with no input required from human beings
  5. The creation of these hidden structures is what makes unsupervised learning algorithms versatile
  6. This offers more post-deployment development than supervised learning algorithms.

Reinforcement Learning

  1. Reinforcement learning directly takes inspiration from how human beings learn from data in their lives
  2. It features an algorithm that improves upon itself and learns from new situations using a trial-and-error method
  3. Favorable outputs are encouraged or ‘reinforced’, and non-favorable outputs are discouraged or ‘punished’
  4. In every iteration of the algorithm, the output result is given to the interpreter, which decides whether the outcome is favorable or not.
  5. In case of the program finding the correct solution, the interpreter reinforces the solution by providing a reward to the algorithm.
  6. If the outcome is not favorable, the algorithm is forced to reiterate until it finds a better result