Decision Trees, Random Forests & Gradient Boosting In R

Predictive models with machine learning wit Rstudio´s ROCR, XGBoost, rparty. Bonus: Neural Networks for Credit Scoring

What you'll 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

Requirements
There are no specific prerequisites for this course. It is designed to cater to both beginners and those with prior experience in spreadsheet analysis and R programming. However, having a basic understanding of these skills is recommended to fully benefit from the course content. Here is a list of recommended skills, tools, experience, and equipment for students:
Basic knowledge of spreadsheets: It is helpful to have familiarity with basic spreadsheet concepts such as formulas, functions, and data manipulation. If you are new to spreadsheets, don't worry, as an introduction to the necessary concepts will be provided.
Basic knowledge of R: While prior experience in R is not essential, having a basic understanding of R programming will enable you to follow the instructions and examples provided in the course. If you are new to R, explanations and additional resources will be provided to help you become acquainted with the environment.
Computer with access to R: To practice and complete exercises in the course, you will need access to a computer with R installed. Instructions for installing R will be provided in case you don't have it set up already.
Motivation and willingness to learn: This course requires dedication and practice to grasp the concepts and apply them effectively. Having a proactive attitude and being willing to work on exercises and challenges throughout the course is recommended.
Don't let the lack of prior experience be a barrier to joining this course. It is designed to be accessible and understandable for both beginners and those looking to strengthen their skills in the field of machine learning. Come and join us on this exciting learning journey!

Description
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!

Who this course is 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:,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.