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

## Introduction to decision trees
#key# DT: Decision Trees #key# creates a decision structure to interpret patterns by splitting data into groups using variables that best split the data into homogenous or numerically relevant groups based on entropy (a measure of variance in the data among different classes). The primary appeal of decision trees is that they can be displayed graphically as a tree-like graph, and they’re easy to explain to non-experts.

Unlike an actual tree, the decision tree is displayed upside down with the “leaves” located at the bottom, or foot, of the tree. Each branch represents the outcome of a decision or variable, and each leaf node represents a class label such as “Go to the beach” or “Stay in.” Decision rules are subsequently marked by the path from the tree’s root to a terminal leaf node.

# Introduction to decision trees
#key# DT: Decision Trees #key# creates a decision structure to interpret patterns by splitting data into groups using variables that best split the data into homogenous or numerically relevant groups based on entropy (a measure of variance in the data among different classes). The primary appeal of decision trees is that they can be displayed graphically as a tree-like graph, and they’re easy to explain to non-experts.

Unlike an actual tree, the decision tree is displayed upside down with the “leaves” located at the bottom, or foot, of the tree. Each branch represents the outcome of a decision or variable, and each leaf node represents a class label such as “Go to the beach” or “Stay in.” Decision rules are subsequently marked by the path from the tree’s root to a terminal leaf node.

This lesson will go over decision trees in more detail and provide the steps involved in their implementation.

Decision Trees

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

1- Decision Trees

Introduction to decision trees