Machine Learning

Dataset,Algorithms,and Prediction are the key flow for machine learning, how a machine learns from the historical data fed into it and make predictions is the objective. i-cons artificial intelligence nowadays hearing and reading words like data scientist, a new term used which is predominant in the artificial intelligence domain. The machine learning algorithms becoming more accurate with their predictions have more impact on the decisions and can be very fascinating as well.

Types of Machine Learning:
Supervised Learning:

When both input and output are provided for the algorithm to assess, input would be in the form of labelled data and the output will be Variables that need to be assessed.

Unsupervised Learning:

Here the input is unlabelled data and the outputs are predictions, algorithms connect the data provided and see if something meaningful interpretation can be done, and based on that the predetermined output will be chosen.

Semi-Supervised Learning:

This is about a mix of two types of data sets and for the algorithm to develop its understanding based on the dataset. Input will be both labelled data and some general dataset, output will be its interpretations to analyze and decide.

Reinforcement Learning:

Here the algorithm does everything from a clearly defined rule, it takes data as it comes in and it decides what steps to take along the process, and takes cues from positives and negatives!

Machine learning is a process where it identifies relevant datasets and prepares them for analysis, choose the algorithm to use, build a model around the chosen algorithm, train and revise the models as dataset requesting and generate the findings!