What is machine learning?
Machine learning(MI) is the study of computer algorithms that improve automatically through previous experience and by the use of data. It is a part of artificial intelligence. For example- medical diagnosis, image processing, prediction, classification, learning association, regression etc. The intelligent systems built on this algorithms have the capability to learn from past experience or historical data. This algorithms use historical data as input to predict new output values.
Why is machine learning important?
It is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today’s leading companies, such as Facebook, Google and Uber, make it a central part of their operations. It has become a significant competitive differentiator for many companies.
What is MI good for?
It allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data.
What are the different types of machine learning?
There are four basic approaches: Supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
- Supervised learning: In this type of machine learning, data scientist supply algorithms with labeled training data and define the variables they want the algorithm to assess for correlations. Both the input and the output of the algorithm is specified.
- Unsupervised learning: This type of machine learning involves algorithms that train on unlabeled data. The algorithm scans through data sets looking for any meaningful connection. The data that algorithms train on as well as the predictions or recommendations they output are predetermined.
- Semi-supervised learning: This approach to machine learning involves a mix of the two preceding types. Data scientists may feed an algorithm mostly labeled training data, but the model is free to explore the data on its own and develop its own understanding of the data set.
- Reinforcement learning: Data scientists typically use reinforcement learning to teach a machine to complete a multi-step process for which there are clearly defined rules. Data scientists program an algorithm to complete a task and give it positive or negative cues as it works out how to complete a task. But for the most part, the algorithm decides on its own what steps to take along the way.
Five best languages for MI
- Python programming language
- R programming language