This course covers data science, machine learning, and big data basics, including key concepts, libraries, and hands-on Kaggle projects for practical learning.

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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


## Linear Regression in Scikit Learn
 
We will be looking into how to use Scikit Learn, the famous library for classical Machine Learning, for Linear Regression.   

### Implementation 1

The following code snippet illustrates how `LinearRegression()` class is used in the implementation.


# Linear Regression in Scikit Learn
 
We will be looking into how to use Scikit Learn, the famous library for classical Machine Learning, for Linear Regression.   

## Implementation 1

The following code snippet illustrates how `LinearRegression()` class is used in the implementation.

Scikit Learn provides an easy-to-use implementation of Linear Regression. You can discover more about that implementation here.

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 ?

Linear Regression in Scikit Learn

Linear Regression in Scikit Learn

Implementation 1