HOT
blank

Decision Trees, Random Forests & Gradient Boosting in R

Business

Rating4.4
Enroll Now

Decision Trees, Random Forests & Gradient Boosting in R, available at $64.99, has an average rating of 4.4, with 72 lectures, 1 quizzes, based on 47 reviews, and has 16880 subscribers.

You will learn about The algorithm behind recursive partitioning decision trees Construct conditional inference decision trees with R`s ctree function Construct recursive partitioning decision trees with R`s rpart function Learn to estimate Gini´s impurity Construct ROC and estimate AUC Random Forests with R´s randomForest package Gradient Boosting with R´s XGBoost package Deal with missing data This course is ideal for individuals who are This course is aimed at students and professionals who are interested in expanding their knowledge and skills in machine learning and business intelligence. The content is designed to be accessible to individuals with varying levels of experience, making it suitable for both beginners and those with prior knowledge in the field. The ideal students for this course may include: or University students: Those pursuing degrees in fields such as data science, computer science, business analytics, or related disciplines can benefit from this course to enhance their understanding of machine learning algorithms and their practical application. or Working professionals: Individuals already employed in roles that involve data analysis, business intelligence, or decision-making can leverage this course to upskill and stay updated with the latest techniques and methodologies in predictive modeling using decision trees, random forests, and gradient boosting. or Data enthusiasts: If you have a passion for data analysis and are eager to dive into the world of machine learning, this course provides a solid foundation. It caters to individuals who may not have extensive experience in the field but are motivated to learn and apply predictive modeling techniques in their work or personal projects. or Research scholars: For those pursuing research in fields related to machine learning, this course can serve as a valuable resource to deepen their understanding of decision trees, ensemble methods, and evaluation metrics for predictive models. It is particularly useful for This course is aimed at students and professionals who are interested in expanding their knowledge and skills in machine learning and business intelligence. The content is designed to be accessible to individuals with varying levels of experience, making it suitable for both beginners and those with prior knowledge in the field. The ideal students for this course may include: or University students: Those pursuing degrees in fields such as data science, computer science, business analytics, or related disciplines can benefit from this course to enhance their understanding of machine learning algorithms and their practical application. or Working professionals: Individuals already employed in roles that involve data analysis, business intelligence, or decision-making can leverage this course to upskill and stay updated with the latest techniques and methodologies in predictive modeling using decision trees, random forests, and gradient boosting. or Data enthusiasts: If you have a passion for data analysis and are eager to dive into the world of machine learning, this course provides a solid foundation. It caters to individuals who may not have extensive experience in the field but are motivated to learn and apply predictive modeling techniques in their work or personal projects. or Research scholars: For those pursuing research in fields related to machine learning, this course can serve as a valuable resource to deepen their understanding of decision trees, ensemble methods, and evaluation metrics for predictive models.

Enroll now: Decision Trees, Random Forests & Gradient Boosting in R

Summary

Title: Decision Trees, Random Forests & Gradient Boosting in R

Price: $64.99

Average Rating: 4.4

Number of Lectures: 72

Number of Quizzes: 1

Number of Published Lectures: 72

Number of Published Quizzes: 1

Number of Curriculum Items: 78

Number of Published Curriculum Objects: 78

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

  • The algorithm behind recursive partitioning decision trees
  • Construct conditional inference decision trees with R`s ctree function
  • Construct recursive partitioning decision trees with R`s rpart function
  • Learn to estimate Gini´s impurity
  • Construct ROC and estimate AUC
  • Random Forests with R´s randomForest package
  • Gradient Boosting with R´s XGBoost package
  • Deal with missing data

Who Should Attend

  • This course is aimed at students and professionals who are interested in expanding their knowledge and skills in machine learning and business intelligence. The content is designed to be accessible to individuals with varying levels of experience, making it suitable for both beginners and those with prior knowledge in the field. The ideal students for this course may include:
  • University students: Those pursuing degrees in fields such as data science, computer science, business analytics, or related disciplines can benefit from this course to enhance their understanding of machine learning algorithms and their practical application.
  • Working professionals: Individuals already employed in roles that involve data analysis, business intelligence, or decision-making can leverage this course to upskill and stay updated with the latest techniques and methodologies in predictive modeling using decision trees, random forests, and gradient boosting.
  • Data enthusiasts: If you have a passion for data analysis and are eager to dive into the world of machine learning, this course provides a solid foundation. It caters to individuals who may not have extensive experience in the field but are motivated to learn and apply predictive modeling techniques in their work or personal projects.
  • Research scholars: For those pursuing research in fields related to machine learning, this course can serve as a valuable resource to deepen their understanding of decision trees, ensemble methods, and evaluation metrics for predictive models.

Target Audiences

  • This course is aimed at students and professionals who are interested in expanding their knowledge and skills in machine learning and business intelligence. The content is designed to be accessible to individuals with varying levels of experience, making it suitable for both beginners and those with prior knowledge in the field. The ideal students for this course may include:
  • University students: Those pursuing degrees in fields such as data science, computer science, business analytics, or related disciplines can benefit from this course to enhance their understanding of machine learning algorithms and their practical application.
  • Working professionals: Individuals already employed in roles that involve data analysis, business intelligence, or decision-making can leverage this course to upskill and stay updated with the latest techniques and methodologies in predictive modeling using decision trees, random forests, and gradient boosting.
  • Data enthusiasts: If you have a passion for data analysis and are eager to dive into the world of machine learning, this course provides a solid foundation. It caters to individuals who may not have extensive experience in the field but are motivated to learn and apply predictive modeling techniques in their work or personal projects.
  • Research scholars: For those pursuing research in fields related to machine learning, this course can serve as a valuable resource to deepen their understanding of decision trees, ensemble methods, and evaluation metrics for predictive models.

Are you interested in mastering the art of building predictive models using machine learning? Look no further than this comprehensive course, “Decision Trees, Random Forests, and Gradient Boosting in R.” Allow me to introduce myself, I’m Carlos Martínez, a highly accomplished expert in the field with a Ph.D. in Management from the esteemed University of St. Gallen in Switzerland. My research has been showcased at prestigious academic conferences and doctoral colloquiums at renowned institutions such as the University of Tel Aviv, Politecnico di Milano, University of Halmstad, and MIT. Additionally, I have co-authored over 25 teaching cases, some of which are included in the esteemed case bases of Harvard and Michigan.

This course takes a hands-on, practical approach utilizing a learning-by-doing methodology. Through engaging presentations, in-depth tutorials, and challenging assignments, you’ll gain the skills necessary to understand decision trees and ensemble methods based on decision trees, all while working with real datasets. Not only will you have access to video content, but you’ll also receive all the accompanying Excel files and R codes utilized in the course. Furthermore, comprehensive solutions to the assignments are provided, allowing you to self-evaluate and build confidence in your newfound abilities.

Starting with a concise theoretical introduction, we will delve deep into the algorithm behind recursive partitioning decision trees, uncovering its inner workings step by step. Armed with this knowledge, we’ll then transition to automating the process in R, leveraging the ctree and rpart functions to construct conditional inference and recursive partitioning decision trees, respectively. Additionally, you’ll learn invaluable techniques such as estimating the complexity parameter and pruning trees to enhance accuracy and reduce overfitting in your predictive models. But it doesn’t stop there! We’ll also explore two powerful ensemble methods: Random Forests and Gradient Boosting, which are both built upon decision trees. Finally, we’ll construct ROC curves and calculate the area under the curve, providing us with a robust metric to evaluate and compare the performance of our models.

This course is designed for university students and professionals eager to delve into the realms of machine learning and business intelligence. Don’t worry if you’re new to the decision trees algorithm, as we’ll provide an introduction to ensure everyone is on the same page. The only prerequisite is a basic understanding of spreadsheets and R.

Get ready to elevate your skills and unlock the potential to optimize investment portfolios with the power of Excel and R. Enroll in this course today and I look forward to seeing you in class!

Bonus Section:Master Neural Networks for Business Analytics! Unlock the full potential of your decision tree skills with an exclusive bonus section in the Decision Trees course! I’ve added a comprehensive module covering the application of neural network models in business intelligence. Dive deep into neural network architectures, training techniques, and fine-tuning methods. Plus, get hands-on experience with a real-world case study on credit scoring using actual data. By including this bonus section, I’m providing you with valuable insights into cutting-edge techniques that can revolutionize your data analysis capabilities. Don’t miss this opportunity to take your skills to the next level and stand out in the competitive world of business analytics. Enroll now and embrace the power of neural networks in decision-making!

Course Curriculum

Chapter 1: Introducción

Lecture 1: Welcome to the Course!

Lecture 2: Improving Your Learning Experience

Lecture 3: Important: the code is in the resources of lesson 12!

Lecture 4: Section Introduction

Lecture 5: Introduction to Decision Trees

Lecture 6: Building a Decision Tree. Part A.

Lecture 7: Building a Decision Tree. Part B.

Lecture 8: Building a Decision Tree. Part C.

Lecture 9: Building a Decision Tree. Part D.

Chapter 2: Data Preprocessing

Lecture 1: Section Introduction

Lecture 2: Teaching Case: Edutravel

Lecture 3: Describing the Dataset

Lecture 4: Importing CSV Data into R

Lecture 5: Changing the Data Type

Lecture 6: Dealing with Missing Data

Lecture 7: Combining Rare Categories

Lecture 8: Data Split: Training and Testing Datasets

Chapter 3: Decisions Trees with CTREE

Lecture 1: Section Introduction

Lecture 2: Decisions Trees with CTREE

Lecture 3: Interpretation of Results

Lecture 4: Prediction with the CTREE Model

Lecture 5: Confusion Matrix

Lecture 6: ROC Curve

Lecture 7: Area Under the ROC Curve (AUC)

Chapter 4: Decisions Tress with RPART

Lecture 1: Section Introduction

Lecture 2: Decisions Trees with rpart

Lecture 3: Choosing Complexity Parameter

Lecture 4: Classification and Confusion Matrix

Lecture 5: ROC and AUC

Chapter 5: Random Forests

Lecture 1: Section Introduction

Lecture 2: Theoretical Introduction to Random Forests

Lecture 3: Building a Random Forest Model in R

Lecture 4: Classification and Confusion Matrix

Lecture 5: ROC & AUC

Chapter 6: Gradient Boosting Trees

Lecture 1: Section Introduction

Lecture 2: Theoretical Introduction to Gradient Boosting

Lecture 3: XGBoost Model

Lecture 4: Prediction and Confusion Matrix

Lecture 5: Conclusion

Chapter 7: New section: Neural Network for Business Analytics

Lecture 1: Welcome to the Neural Networks Course!

Lecture 2: Section Introduction

Lecture 3: Introduction to Neural Networks

Lecture 4: Artificial Neural Networks

Lecture 5: Recurrent Neural Networks

Lecture 6: Convolutional Neural Networks

Lecture 7: Section Introduction

Lecture 8: Teaching Case: Credicars. Session A.

Lecture 9: Teaching Case: Credicars. Session B.

Lecture 10: Introduction to Credit Scoring. Session A.

Lecture 11: Introduction to Credit Scoring. Session B.

Lecture 12: Introduction to Credit Scoring. Session C.

Lecture 13: Describing the Dataset.

Lecture 14: Section Introduction

Lecture 15: Benefits of Neural Networks in Credit Scoring

Lecture 16: Importing CSV Data into R

Lecture 17: Dealing With Missing Data. Session A.

Lecture 18: Teaching Case: Credicars. Session B.

Lecture 19: Removing Variables from the Dataframe

Lecture 20: Switching Variables from Categoricals to Numericals

Lecture 21: Correlation Matrix

Lecture 22: Normalizing the Data

Lecture 23: Splitting the Dataset into Training and Testing

Lecture 24: NeuralNet Package

Lecture 25: Building a Neural Network Model

Lecture 26: Prediction

Lecture 27: Cut-Off Threshold

Lecture 28: Confusion Matrix & Metrics (Step-by-Step in Excel)

Lecture 29: Logistic Regression

Lecture 30: Confussion Matrix. Session A.

Lecture 31: Confussion Matrix. Session B.

Lecture 32: Solution to the Course Project

Lecture 33: Congratulations!

Instructors

Rating Distribution

  • 1 stars: 0 votes
  • 2 stars: 0 votes
  • 3 stars: 7 votes
  • 4 stars: 16 votes
  • 5 stars: 24 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

You may also like

Top 10 Relationship Building Courses to Learn in November 2024

Top 10 Storytelling Courses to Learn in November 2024

Top 10 Resilience Training Courses to Learn in November 2024