Course Content
Day-2: How to use VScode (an IDE) for Python?
Day-3: Basics of Python Programming
This section will train you for Python programming language
Day-4: Data Visualization and Jupyter Notebooks
You will learn basics of Data Visualization and jupyter notebooks in this section.
Day-5: MarkDown language
You will learn whole MarkDown Language in this section.
Day-10: Data Wrangling and Data Visualization
Data Wrangling and Visualization is an important part of Exploratory Data Analysis, and we are going to learn this.
Day-11: Data Visualization in Python
We will learn about Data Visualization in Python in details.
Day-12,13: Exploratory Data Analysis (EDA)
EDA stands for Exploratory Data Analysis. It refers to the initial investigation and analysis of data to understand the key properties and patterns within the dataset.
Day-15: Data Wrangling Techniques (Beginner to Pro)
Data Wrangling in python
Day-26: How to use Conda Environments?
We are going to learn conda environments and their use in this section
Day-37: Time Series Analysis
In this Section we will learn doing Time Series Analysis in Python.
Day-38: NLP (Natural Language Processing)
In this section we learn basics of NLP
Day-39: git and github
We will learn about git and github
Day-40: Prompt Engineering (ChatGPT for Social Media Handling)
Social media per activae rehna hi sab kuch hy, is main ap ko wohi training milay ge.
Python ka Chilla for Data Science (40 Days of Python for Data Science)
About Lesson

K-means clustering in beginner-friendly terms:

What is K-means clustering?
K-means clustering is an unsupervised machine learning algorithm used to automatically group or cluster similar data points together.

How does it work?
K-means clustering works by defining ‘K’ number of clusters or groups ahead of time. The algorithm then assigns each data point to one of these K clusters based on feature similarity. The features could be things like age, income, spending habits etc.

It then calculates the ‘center’ of each cluster. This is called the centroid. Next it recalculates cluster membership by finding which cluster center each point is closest to. This process repeats until the membership assignments no longer change.

Why use K-means clustering?
The main reasons to use K-means clustering are:

  1. Grouping Data: It automatically organizes unlabeled data points into meaningful clusters or groups.

  2. Pattern Recognition: Clustering helps recognize hidden patterns in unstructured data and gain insights.

  3. Data Segmentation: Identifying distinct groups in data allows treating each segment differently for tasks like targeting customers.

  4. Data Compression: Cluster IDs can replace raw data for storing, visualizing or processing large datasets.

How is it applied?
K-means clustering is commonly used for customer segmentation, image recognition, compiler optimization, gene expression analysis and more. It works best with numerical data and when you have a general idea of ‘K’ clusters to aim for.

In summary, K-means clustering provides an automatic way to group messy data into organized, interpretable clusters based on similarities between data points.

Join the conversation
Muhammad Shahzad 9 months ago
K clustering is type of unsupervised Machine Learning.