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.

# 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.

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

Split Validation

Model Design

Linear Regression

Logistic Regression

Support Vector Machines

K-Nearest Neighbors

Tree-Based Methods

Conclusion

Appendix

K-Means Clustering Implementation Steps: 1 to 3

Quick overview of k-means clustering