Introduction to Machine Learning

Machine Learning is a growing technology these days. It allows computers to learn from past data. ML uses various algorithms for building mathematical models and making predictions using historical data or information. It is a subfield of AI and Data Science. Its goal is to achieve a general understanding of data that fits into the model and can be utilized by the users.

It allows the computers to train the data inputs and uses statistical analysis to get the output values that will fall within the given ranges. ML is a continuously developing field because of this there are some considerations that we need to keep in mind as we work with ML methodologies or algorithms.

ML uses for various tasks such as image recognition, speech recognition, Facebook auto-tagging and many more.
In the real world, humans are there who can learn anything based on their experience and learning capabilities, so in the case of computers or machines it works on the instruction given by us but machines can also learn from experience or past data like human uses experience. 

The term Machine Learning was introduced by Arthur Samuel in 1959

ML helps the machine to automatically learn from data improve its performance from past experiences and predict things without being explicitly programmed. With the help of historical data which is also known as training data builds a model is also known as a mathematical model that helps in predicting decisions without being explicitly programmed. It brings computer science and statistics together for creating predictive models. ML algorithms use training datasets to train the model. The more we will provide the information the higher we will get performance.

A machine can learn if it can improve its performance by gaining more data. Machine Learning comes into the picture when problems cannot be solved using typical approaches. 


  • It uses training data to observe the various types of patterns given in the datasets.
  • It always learns from past data and improves its performance.
  • It is also a data-driven technology.
  • It is somehow similar to data mining as it also uses a huge amount of data.
  • It rapidly increases the production of data.
  • It can solve complex problems.
  • Its decision-making skills are good it can be used in various sectors like finance.
  • It finds hidden patterns easily and extracts useful information from the given data.

These days ML has got a great advancement and is used everywhere like self-driving cars, Amazon Alexa, Catboats, recommender systems, and many more. Modern machine learning models can be used for making various predictions, including weather prediction, disease prediction, stock market analysis, etc.