Decision Trees and Random Forests: A Visual Introduction For Beginners

If you want to learn how decision trees and random forests work, plus create your own, this visual book is for you.  

The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday.

From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services.

They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk.

Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact.

This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests. If you want to dig into the basics with a visual twist plus create your own algorithms in Python, this book is for you.

• Bayes' Theorem Examples: A Visual Introduction For Beginners
• Make Your Own Neural Network: An In-depth Visual Introduction For Beginners
• The Math of Neural Networks
• Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners)
• Algorithms Illuminated (Part 2): Graph Algorithms and Data Structures (Volume 2)
• Algorithms Illuminated: Part 1: The Basics
• Hypothesis Testing: A Visual Introduction To Statistical Significance
• The Hundred-Page Machine Learning Book
• Algorithms Illuminated (Part 3): Greedy Algorithms and Dynamic Programming
• Practical Statistics for Data Scientists: 50 Essential Concepts