Decision Trees, Random Forests & Gradient Boosting in R
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
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Carlos Martínez, PhD • Best-Selling Instructor • 80K+ Students
Ph.D. in Management | MBA | MsC. in Finance | Industrial Eng
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 7 votes
- 4 stars: 16 votes
- 5 stars: 24 votes
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