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Data Science - Algorithm

Data Science is the territory of study which includes removing bits of knowledge from immense measures of data by the utilization of different logical techniques, calculations, and procedures. It encourages you to find concealed examples from the crude data.

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Data Science - Algorithm

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  1. Data Science Explores the study and construction of Algorithms

  2. Introduction • In present reality, each assignment is being robotized. Gone are the days when you needed to stroll for twenty days or a ride a horse for miles to get to a town or even do manual work, for example, carrying heavy logs. • The term Data Science has developed as a result of the advancement of scientific measurements, data examination, and enormous data. • Data Science explores the study and construction of algorithms that can learn from and make predictions on data.

  3. Here are several Algorithms each in data science, for example, you should know today with the goal that our future can be more splendid. • Decision Tree • Linear Regression • Logistic Regression • Support Vector Machine • Naïve Bayes • Gradient Boosting Algorithm

  4. Decision Tree • A Decision tree is a Decision support device that uses a tree-like diagram or model of choices and their potential results, including chance event results, asset expenses, and utility. • It is one approach to show a algorithm that just contains contingent control explanations.

  5. Linear Regression • In Statistics, linear regression is a direct way to deal with demonstrating the connection between a scalar reaction and at least one logical factors. • A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept.

  6. Logistic regression • Logistics Regression is a factual model that in its essential structure utilizes a calculated capacity to demonstrate a paired dependent variable, albeit a lot increasingly complex expansions exist. • In regression analysis, logistic regression is assessing the parameters of a logistic model.

  7. Support Vector Machine • Support Vector Machines are based on the concept of decision planes that define decision boundaries. • In this example, the objects belong either to class GREEN or RED. The separating line defines a boundary on the right side of which all objects are GREEN and to the left of which all objects are RED. • Any new object (white circle) falling to the right is labeled, i.e., classified, as GREEN (or classified as RED should it fall to the left of the separating line).

  8. Naïve Bayes • Naive Bayes Classifiers rely on the Bayes’ Theorem, which is based on conditional probability or in simple terms, the likelihood that an event (A) will happen given that another event (B) has already happened. • Essentially, the theorem allows a hypothesis to be updated each time new evidence is introduced.

  9. Gradient Boosting Algorithm • Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. 

  10. THANK YOU https://www.gangboard.com/big-data-training/data-science-training

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