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Best Decision Tree Books & Best Decision Tree Courses in 2024

Best Decision Tree Courses 2021

Decision Trees, Random Forests, AdaBoost & XGBoost in Python

You’re looking for a complete Decision tree course that teaches you everything you need to create a Decision tree/ Random Forest/ XGBoost model in Python, right?

You’ve found the right Decision Trees and tree based advanced techniques course!

After completing this course you will be able to:

Identify the business problem which can be solved using Decision tree/ Random Forest/ XGBoost of Machine Learning.

Have a clear understanding of Advanced Decision tree based algorithms such as Random Forest, Bagging, AdaBoost and XGBoost

Create a tree based (Decision tree, Random Forest, Bagging, AdaBoost and XGBoost) model in Python and analyze its result.

Confidently practice, discuss and understand Machine Learning concepts

Decision Tree – Theory, Application and Modeling using R

Decision Tree Model building is one of the most applied technique in analytics vertical. The decision tree model is quick to develop and easy to understand. The technique is simple to learn. A number of business scenarios in lending business / telecom / automobile etc. require decision tree model building.

This course ensures that student get understanding of

what is the decision tree
where do you apply decision tree
what benefit it brings
what are various algorithm behind decision tree
what are the steps to develop decision tree in R
how to interpret the decision tree output of R

Getting Started with Decision Trees

Decision Tree algorithm is one of the most powerful algorithms in machine learning and data science. It is very commonly used by data scientists and machine learning engineers to solve business problem and explain that to your customers easily. This course will introduce you to the concept of Decision Trees and teach you how to build one using Python

Why learn about Decision Trees?

Decision Trees are the most widely and commonly used machine learning algorithms.

It can be used for solving both classification as well as regression problems.

Decision Trees are easy to interpret and hence have multiple applications around different industries.

What would you learn in Getting started with Decision Tree course?

Introduction to Decision Trees

Terminologies related to decision trees

Different splitting criterion for decision tree like Gini, chi-square, etc.

Implementation of decision tree in Python

Best Decision Tree Books 2021

Decision Trees and Random Forests: A Visual Introduction For Beginners

Decision Trees and Random Forests: A Visual Introduction For Beginners
  • Smith, Chris (Author)
  • English (Publication Language)
  • 168 Pages - 10/04/2017 (Publication Date) - Independently published (Publisher)

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.

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners
  • Amazon Kindle Edition
  • Hartshorn, Scott (Author)
  • English (Publication Language)

If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly used in a variety of applications including big data analysis for industry and data analysis competitions like you would find on Kaggle.

This book explains how Decision Trees work and how they can be combined into a Random Forest to reduce many of the common problems with decision trees, such as overfitting the training data.

Data Mining With Decision Trees: Theory And Applications (2Nd Edition)

Decision trees have become one of the most powerful and popular approaches in knowledge discovery and data mining; it is the science of exploring large and complex bodies of data in order to discover useful patterns. Decision tree learning continues to evolve over time. Existing methods are constantly being improved and new methods introduced. This 2nd Edition is dedicated entirely to the field of decision trees in data mining; to cover all aspects of this important technique, as well as improved or new methods and techniques developed after the publication of our first edition. In this new edition, all chapters have been revised and new topics brought in.

New topics include Cost-Sensitive Active Learning, Learning with Uncertain and Imbalanced Data, Using Decision Trees beyond Classification Tasks, Privacy Preserving Decision Tree Learning, Lessons Learned from Comparative Studies, and Learning Decision Trees for Big Data. A walk-through guide to existing open-source data mining software is also included in this edition. This book invites readers to explore the many benefits in data mining that decision trees offer: Self-explanatory and easy to follow when compacted Able to handle a variety of input data: nominal, numeric and textual Scales well to big data Able to process datasets that may have errors or missing values High predictive performance for a relatively small computational effort Available in many open source data mining packages over a variety of platforms Useful for various tasks, such as classification, regression, clustering and feature selection

Bestseller No. 1
The Decision Tree: How to make better choices and take control of your health
  • Amazon Kindle Edition
  • Goetz, Thomas (Author)
  • English (Publication Language)
Bestseller No. 2
Decision Trees and Random Forests: A Visual Introduction For Beginners
  • Smith, Chris (Author)
  • English (Publication Language)
  • 168 Pages - 10/04/2017 (Publication Date) - Independently published (Publisher)
Bestseller No. 3
Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners
  • Amazon Kindle Edition
  • Hartshorn, Scott (Author)
  • English (Publication Language)
SaleBestseller No. 4
  • Hardcover Book
  • Lior Rokach & Oded Maimon (Author)
  • English (Publication Language)
Bestseller No. 5
The Drama Therapy Decision Tree: Connecting Drama Therapy Interventions to Treatment
  • Amazon Kindle Edition
  • Dickinson, Paige (Author)
  • English (Publication Language)
Bestseller No. 6
Medical Conditions and Massage Therapy: A Decision Tree Approach (LWW Massage Therapy and Bodywork...
  • Used Book in Good Condition
  • Walton, Tracy (Author)
  • English (Publication Language)
SaleBestseller No. 7
Knock Knock Make a Decision Pad
  • 6 x 9 inches; 60 sheets
  • Reduce your chances of regret by more than 83.4 percent
  • Give good guidance—whether it’s a commonplace or life-altering choice
Bestseller No. 8
The Drama Therapy Decision Tree, 2nd Edition: Connecting Drama Therapy Interventions to Treatment
  • English (Publication Language)
  • 448 Pages - 04/17/2024 (Publication Date) - Intellect Ltd (Publisher)
Bestseller No. 9
Tree-based Machine Learning Algorithms: Decision Trees, Random Forests, and Boosting
  • Sheppard, Clinton (Author)
  • English (Publication Language)
  • 110 Pages - 09/09/2017 (Publication Date) - CreateSpace Independent Publishing Platform (Publisher)
Bestseller No. 10
Data-Based Decision Making (Essentials for Principals)
  • Used Book in Good Condition
  • Edie L. Holcomb (Author)
  • English (Publication Language)

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