Explore practical coding of basic machine learning models using Python. Gain insights into algorithms like linear regression, logistic regression, SVM, KNN, and decision trees.

Machine Learning with Python A Practical Beginners’ Guide by Oliver Theobald-01.png

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This course teaches you how to code basic machine learning models. The content is designed for beginners with general knowledge of machine learning, including common algorithms such as linear regression, logistic regression, SVM, KNN, decision trees, and more. If you need a refresher, we have summarized key concepts from machine learning, and there are overviews of specific algorithms dispersed throughout the course.

A Practical Guide to Machine Learning with Python

## Quick overview of k-means clustering
Another popular technique to reduce data complexity is k-means clustering, which identifies groups of data points without prior knowledge of existing classes.

K-means clustering splits the dataset into `k` number of clusters, with `k` representing the number of clusters. Setting `k` to “3,” for example, splits the data into three clusters.

Each cluster is assigned a random centroid, a data point that becomes the epicenter of an individual cluster. The remaining data points are assigned to the closest centroid. The centroid coordinates are then updated based on the mean of the new cluster. This update may cause some data points to switch allegiance and join a new cluster based on comparative proximity with a different centroid. If so, the centroid coordinates are recalculated and updated.
 
The algorithm reaches completion when all data points remain in the same cluster following an update in centroid coordinates, which will produce the final set of clusters.

## Exercise 
This exercise will generate an artificial dataset and use k-means clustering to split the data into four natural groupings.

## 1) Import libraries
The artificial dataset used for this exercise is generated using `make_blobs` from Scikit-learn and grouped using the k-means algorithm. Visualization will be created for this exercise using Matplotlib Pyplot and Matplotlib inline.


# Quick overview of k-means clustering
Another popular technique to reduce data complexity is k-means clustering, which identifies groups of data points without prior knowledge of existing classes.

K-means clustering splits the dataset into `k` number of clusters, with `k` representing the number of clusters. Setting `k` to “3,” for example, splits the data into three clusters.

Each cluster is assigned a random centroid, a data point that becomes the epicenter of an individual cluster. The remaining data points are assigned to the closest centroid. The centroid coordinates are then updated based on the mean of the new cluster. This update may cause some data points to switch allegiance and join a new cluster based on comparative proximity with a different centroid. If so, the centroid coordinates are recalculated and updated.
 
The algorithm reaches completion when all data points remain in the same cluster following an update in centroid coordinates, which will produce the final set of clusters.

# Exercise 
This exercise will generate an artificial dataset and use k-means clustering to split the data into four natural groupings.

# 1) Import libraries
The artificial dataset used for this exercise is generated using `make_blobs` from Scikit-learn and grouped using the k-means algorithm. Visualization will be created for this exercise using Matplotlib Pyplot and Matplotlib inline.


We will learn how to reduce dimensions and become familiar with the implementation steps (1-3) of the k-means clustering.

K-Means Clustering Implementation Steps: 1 to 3

Introduction to Course

Introduction to Machine Learning

Exploratory Data Analysis

Data Scrubbing

Pre-Model Algorithms

Customer Segmentation with K-Means Clustering

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Cardiovascular Disease Risk Prediction with Random Forest

Conclusion

Appendix

K-Means Clustering Implementation Steps: 1 to 3

Quick overview of k-means clustering