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

# Clustering

Clustering is a famous unsupervised learning technique. It involves making clusters or groups of items such that the items in the same cluster are more similar to each other than items in the other cluster. In this lesson, we will be looking into K-means clustering. 

# K-means clustering 

K-Means clustering as the name suggests, looks for a fixed number of clusters ($K$) in the dataset. The mean or center of the $K^{th}$ cluster is represented by $\mu_{k}$, which is also called the cluster centroid or average point. K-means relies on the idea of similarity/dissimilarity when assigning instances to respective clusters.

**Similarity** can also be thought of as proximity. It is a numerical measure of how alike two data instances are. **Cosine Similarity** is one of the most commonly used similarity measures. It takes values between 0 and 1, where a higher value indicates more similar instances. Cosine similarity between two feature vectors of $x = (x_1, x_2, ... , x_n)$ and $y = (y_1, y_2, ..., y_n)$ is given as 
$sim(x, y) = \frac{x.y}{||x|||y||}$

Where $||x||$ and $||y||$ are the *Euclidean norm* of the feature vectors $x$ and $y$ respectively. It is given as 

$||x|| = \sqrt{x_1^2+x_2^2+ ... + x_n^2}$
<br>
$||y|| = \sqrt{y_1^2+y_2^2+ ... + y_n^2}$
  
The notion of dissimilarity can be understood using the concept of distance between two points. It is a numerical measure of how far apart or different two data instances are. The **Euclidean distance** is one of the most commonly used measures of dissimilarity between instances. Lower values indicate more similar instances. The Euclidean distance between two instances can be given as $x = (x_1, x_2, ... , x_n)$ and $y = (y_1, y_2, ..., y_n)$ is given as

$EuclideanDistance =\sqrt{(y_1-x_1)^2 + (y_2 - x_2)^2 + ... + (y_n-x_n)^2}$

> In the case of Categorical Features, if two feature values are same then their similarity is one and vice versa.


# How does the K-means algorithm work

In K-means clustering we repeat the below steps iteratively. 

1. Randomly pick $K$ centroids in the dataset. 
2. Add each instance to the closest centroid by computing the similarity or distance measures. 
3. Recompute the centroids. The centroid is the center (average) point of the cluster. 
4. Add each instance to the closest centroid.
5. Recompute the centroids. 
6. Repeat until no changes occur.  





In this lesson, you’ll learn about clustering algorithms, which form the core of unsupervised learning and involve grouping similar items together.

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 ?

K-Means Clustering

Clustering

K-means clustering