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Difference Between Machine Learning vs Statistics

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  • Last Updated : 24 Oct, 2020
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Machine Learning: Machine Learning is the use of Artificial Intelligence (AI) that gives frameworks the capacity to naturally take in and improve as a matter of fact without being unequivocally modified. Machine Learning centers around the advancement of PC programs that can get to information and use it to learn for themselves.  

The way toward learning starts with perceptions or information, for example, models, direct insight, or guidance, to search for designs in information and settle on better choices later on dependent on the models that we give. The essential point is to permit the computers to adapt naturally without human intercession or help and alter activities as needs are.

Statistics: Statistics is a type of numerical investigation that utilization evaluated models, portrayals, and outlines for a given arrangement of test information or genuine examinations. Statistics considers the approach to assemble, audit, dissect and make inferences from the information.

Statistics is a term used, to sum up, a cycle that an examiner uses to describe a data set. On the off chance that the informational collection relies upon an example of a bigger populace, at that point the examiner can create understandings about the populace fundamentally dependent on the factual results from the example. Statistics include the way toward social occasion and assessing information and afterward summing up the data into a numerical structure.

Machine Learning


Machine Learning is a lot of steps or rules taken care of by the user where the machine comprehends and train without anyone else. 

Statistics is a numerical idea in finding the pattern from the information.

It makes the most accurate prediction possible and then foresee future events or arrange a current material. 

It interfaces the relationship between the variables and finds out the connection between the information points.

Inputs and Outputs are labels and features.

Inputs and Outputs are Data points.

It consists of Mathematics and Algorithms.

It consists of only Mathematical and Statistical Information.

It is mainly used in the hypothesis or prediction.

It is mainly used to find a correlation between the data points, univariate, multivariable, etc.

It concerned in the field of Data Science and Artificial Intelligence with concepts like predominant algorithms and neural networks.

It concerned in the field of Data Analytics and Artificial Intelligence with concepts like probabilities and derivatives.

Keywords: Decision Tree, Neural Networks, Logistic Regression, Support Vector Machine, etc.

Keywords: Covariance, Univariate, Estimators, etc.

Types: Supervised, Unsupervised, and Reinforcement Learning.

Types: Regression, Classification, and Forecasting Continuous Variable.

Applications: Weather forecasting, Stock Market Prediction, etc.

Applications: Statistics description techniques, finding patterns in the data, outliers in the data, etc.

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