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
Our next supervised learning classification technique is **K-Nearest Neighbors** (k-NN), which classifies new unknown data points based on their proximity to known data points. This classification process is determined by setting the `k` number of data points closest to the target data point. For example, if you set `k` to 3, k-NN analyzes the nearest three data points (neighbors) to the target data point.

# Quick overview
Our next supervised learning classification technique is **K-Nearest Neighbors** (k-NN), which classifies new unknown data points based on their proximity to known data points. This classification process is determined by setting the `k` number of data points closest to the target data point. For example, if you set `k` to 3, k-NN analyzes the nearest three data points (neighbors) to the target data point.

This lesson will introduce the k-nearest neighbors technique and the steps involved in its implementation.

Introduction to K-Nearest Neighbors

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

Introduction to K-Nearest Neighbors

Quick overview