An Introduction To Machine Learning – AI
An Introduction To Machine Learning – AI

Machine learning is a branch of Artificial Intelligence which deals with providing systems the ability to learn and improve from data samples without explicit programming instructions.

Following are some definitions from the literature.

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.

https://expertsystem.com/machine-learning-definition/

Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.

https://en.wikipedia.org/wiki/Machine_learning

Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover. The hidden patterns and knowledge about a problem can be used to predict future events and perform all kinds of complex decision making.

https://towardsdatascience.com/machine-learning-an-introduction-23b84d51e6d0

The Concept:

Generally, computers solve any problem by running a set of predefined rules/ instructions. So we have the data (input), a set of rules/ instructions (model) and we get the result (output). This is called traditional programming. In machine learning we have the data (input), we also know the result (output) and we train the system to learn the hidden rules so that the system can predict the output of an unknown input using those learned rules.

5 Steps Process of Machine Learning

  1. Data Collection – Data is collected from multiple sources
  2. Data Preparation – Removal of unwanted fields and to making sure the data is ready for analyses
  3. Model Training – The model is trained based on the prepared data
  4. Evaluation – The results are analysed and evaluated to check the performance of the model
  5. Tuning – The model features are tuned/adjusted to maximize the performance

Machine Learning Types/ Approaches

  • Supervised Learning (we have inputs and outputs i.e labelled data and the model learns the rules)
  • Unsupervised Learning (We only have the input but we don’t have the outputs i.e. unlabeled data and the model learns the hidden rules)
  • Semi-supervised Learning (we have mixture of labelled and unlabeled data)
  • Reinforcement Learning (the model tries to correct the learned rules by repeating the process again and again)

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