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



## Nearest Neighbors Regression


The idea of Nearest Neighbor Regression has been borrowed from **Nearest Neighbors Classification**. Note that: 

* The principle behind the nearest neighbors algorithm in regression is to find the nearest, let's say, k neighbors. The neighbors are calculated based on some measure of **similarity or distance** calculation. Based on the value **K** chosen and neighbors retrieved, this algorithm is also called **K-Nearest neighbors**. 

* $K$ is a parameter that can be tuned.

* The output value for a new instance is returned by taking the mean of its nearest neighbors in case of Regression. The important thing to remember is that no equation is constructed and no parameters are optimized.

* The nearest neighbors algorithms remembers all the training dataset and comes under the category of non-generalizing algorithms of Machine Learning. It stores the training dataset in some efficient data structure. 


### Implementation in Scikit Learn

The `KNeighborsRegressor` class implements the KNN algorithm.



# Nearest Neighbors Regression


The idea of Nearest Neighbor Regression has been borrowed from **Nearest Neighbors Classification**. Note that: 

* The principle behind the nearest neighbors algorithm in regression is to find the nearest, let's say, k neighbors. The neighbors are calculated based on some measure of **similarity or distance** calculation. Based on the value **K** chosen and neighbors retrieved, this algorithm is also called **K-Nearest neighbors**. 

* $K$ is a parameter that can be tuned.

* The output value for a new instance is returned by taking the mean of its nearest neighbors in case of Regression. The important thing to remember is that no equation is constructed and no parameters are optimized.

* The nearest neighbors algorithms remembers all the training dataset and comes under the category of non-generalizing algorithms of Machine Learning. It stores the training dataset in some efficient data structure. 


## Implementation in Scikit Learn

The `KNeighborsRegressor` class implements the KNN algorithm.

In this lesson, you'll learn about the details of the Nearest Neighbour Regression, which is used for Regression Problems and predicts the outcome based on the nearest neighbors of an instance.

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 ?

Nearest Neighbour Regression

Nearest Neighbors Regression