Gain insights into data science fundamentals, explore machine learning and big data, and delve into real-time projects. Discover systematic approaches to data acquisition, wrangling, and solving diverse problems.

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There is a lot of dispersed, and somewhat conflicting information on the internet when it comes to data science, making it tough to know where to start. Don't worry. 

This course will get you familiar with the state of data science and the related fields such as machine learning and big data. You will be going through the fundamental concepts and libraries which are essential to solve any problem in this field. 

You will work on real-time projects from Kaggle while also honing your mathematical skills which will be used extensively in most problems you face. You will also be taken through a systematic approach to learning about data acquisition to data wrangling and everything in between. This is your all-in-one guide to becoming a confident data scientist.

An Introductory Guide to Data Science and Machine Learning

## Support Vector Regression

The idea of Support Vector Regression has been borrowed from **Support Vector Machines**. In classification, we predict a discrete-valued output. Here are some things to note:

* As the name suggests **Support Vector Regression** is used for predicting the real-valued output.

* The model produced by **Support Vector Regression** depends only on a subset of the training data.

* There is a concept of **Kernel**, which involves mapping the features or columns or dimensions to higher dimensions to make the problem solvable. 



### Implementation in Scikit Learn

`SVR` class implements the Support Vector Regression.


# Support Vector Regression

The idea of Support Vector Regression has been borrowed from **Support Vector Machines**. In classification, we predict a discrete-valued output. Here are some things to note:

* As the name suggests **Support Vector Regression** is used for predicting the real-valued output.

* The model produced by **Support Vector Regression** depends only on a subset of the training data.

* There is a concept of **Kernel**, which involves mapping the features or columns or dimensions to higher dimensions to make the problem solvable. 



## Implementation in Scikit Learn

`SVR` class implements the Support Vector Regression.


Support Vector Regression is a powerful Model that works on the principles of Support Vector Classification. However, it is used for Regression problems.  You'll learn more about it in this lesson.

What is Data Science ?

Applications of Data Science

Overview of Libraries

Probability and Statistics

Machine Learning Part-1

Machine Learning Part-2

Machine Learning Part-3

Deep Learning

Machine Learning Tools and Libraries

Big Data Tools and Technologies

Where to go next ?

Support Vector Regression

Support Vector Regression