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


# 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.

import pandas as pd 
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split

dataset = pd.read_csv("/usr/local/notebooks/datasets/tips.csv")

X = dataset[["total_bill"]]
y = dataset[["tip"]]

X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, train_size=0.7)

reg = linear_model.LinearRegression()
reg.fit(X_train, y_train)
y_pred = reg.predict(X_test)

print("The MSE on test set is {0:.4f}".format(mean_squared_error(y_test, y_pred)))

* Line `1-4` imports the necessary modules. 

* Line `6` loads the dataset from the github repository.
* Line `8` and Line `9` extract the relevant independent and dependent columns.

* On Line `11` we divide the Tips Dataset into training and test dataset. Note that `train_size=0.7`, indicates to include 70% of the rows in the training dataset and 30% in the test dataset. 


* Line `14` fits the Regression model on the training dataset. 

* Line `15` uses the trained model to predict the values for the test dataset. Line `17` displays the Mean Squared Error on the test dataset. Lesser values indicate that the model has trained well and vice versa. 



## Implementation 2

In Implementation 2, `SGDRegressor()` class is used as illustrated below. It has the same underlying implementation that works to find the optimal parameters as gradient descent.


 

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