I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots. ...

Full Bio 
Follow on

I am a marketing intern at Valuefirst Digital Media. I write blogs on AI, Machine Learning, Chatbots, Automation etc for House of Bots.

Why is there so much buzz around Predictive Analytics?
1072 days ago

Changing Scenario of Automation over the years
1073 days ago

Top 7 trending technologies in 2018
1074 days ago

A Beginner's Manual to Data Science & Data Analytics
1074 days ago

Artificial Intelligence: A big boon for recruitment?
1075 days ago

Top 5 chatbot platforms in India

Artificial Intelligence: Real-World Applications

How can Big Data Analytics influence Decision Making?

Why is there so much buzz around Predictive Analytics?

Levels of Big Data Maturity

5 Best Machine Learning Algorithms for Beginners

By shiwaneeg |Email | May 8, 2018 | 24339 Views

Machine Learning has become increasingly important today because of the digital transformation of companies leading to the production of massive data of different forms and types, at an ever increasing rate. Due to the advancements in computing technologies and exposure to huge amounts of data, the applicability of machine learning is dramatically increasing.

There is a great significance of Machine Learning. So, to get started with Machine Learning, here are 5 best machine learning algorithms for beginners:

1. Ordinary Least Square (OLS):

OLS is an algorithm which is used for estimating the unknown parameters in a linear regression model. It is used to fit a linear line to the data set. To peculiarly define it, it is a statistical method of analysis that helps in estimating the relationship between one or more independent variables and a dependent variable.
It estimates the relationship by minimizing the sum of the squares in the difference between the observed and predicted values of the dependent variable configured as a straight line. 

OLS is used to have a best fit regression line, used for forecasting & quantify marginal effect.

2. Decision tree:

Decision Tree is one of the popular algorithm. It is a binary classifier which helps to choose one from two given decisions that uses features.
Decision tree are used for making classifications & predictions.  It uses a tree-like graphical representation where the branches signify the outcomes and leaf signify a particular class label- mostly, a Yes or a No to any outcome. 

Decision trees is used when the data scientist wants to evaluate different operations of an alternate decision. It gives a structured direction for businesses to make favorable decisions by assessing the likeable probabilities. It is used for classification & segmentation. 

3. Naive Bayesian Classification:

Naive Bayesian Classification uses Bayesian theorem for data classification. It also uses strong independent assumption between features. So, whatever features you are using in your data it assumes that there is no dependency between the features. 

It is also used for text mining, email spam classification. And, it is also popular in text classification. 

4. Logistics Regression:

Logistics Regression is also one of the popular algorithm in Machine Learning. It is simple & linear in nature. It helps in classifying data into multiple groups. It is primarily used for modelling binomial target variable and can be extended to multinomial logistics regression. It is popular in credit scoring, marketing campaign analytics.

5. Support Vector Machine:

Support Vector Machine is a type of algorithm which uses hyperplanes to separate binary classes. So, instead of a linear line, it actually uses multiple ones to separate data. And, the separation is more efficient in this case because it takes into consideration the non-linearity in data which often gets ignored in simple classification algorithms like logistic regression. Also, it is very highly scalable so that it can be used for a large deficit even if there is consideration of non-linearity as multiple hyperplanes are highly used here. 

Source: HOB