XGBoost for Business: Machine Learning Course in Python & R

XGBoost for Business: Machine Learning Course in Python & R, available at $64.99, has an average rating of 4.2, with 73 lectures, based on 229 reviews, and has 1994 subscribers.

You will learn about Understand the underlying concepts of XGBoost. Code in Python and R to implement XGBoost. Apply XGBoost to a business problem in the form of a case study. Utilize XGBoost to solve similar business problems in the future. Understand how to effectively communicate the results of using XGBoost to stakeholders. Enhance your skills in coding and machine learning through hands-on practice with XGBoost. Understand the role of machine learning in business and how it can be used to improve decision-making and solve complex problems. Use machine learning techniques, including XGBoost, to analyze and interpret data in the context of business applications. This course is ideal for individuals who are Data scientists looking to improve their machine learning skills and understanding of XGBoost. or Business professionals who want to learn how to apply XGBoost to solve business problems and make data-driven decisions. or Computer science students interested in learning about the latest machine learning techniques and how to implement them. or Entrepreneurs looking to leverage the power of machine learning to improve their businesses. or Data analysts who want to expand their toolkit and learn how to use XGBoost to better understand and analyze data. It is particularly useful for Data scientists looking to improve their machine learning skills and understanding of XGBoost. or Business professionals who want to learn how to apply XGBoost to solve business problems and make data-driven decisions. or Computer science students interested in learning about the latest machine learning techniques and how to implement them. or Entrepreneurs looking to leverage the power of machine learning to improve their businesses. or Data analysts who want to expand their toolkit and learn how to use XGBoost to better understand and analyze data.

Enroll now: XGBoost for Business: Machine Learning Course in Python & R

Summary

Title: XGBoost for Business: Machine Learning Course in Python & R

Price: $64.99

Average Rating: 4.2

Number of Lectures: 73

Number of Published Lectures: 73

Number of Curriculum Items: 73

Number of Published Curriculum Objects: 73

Original Price: €219.99

Quality Status: approved

Status: Live

What You Will Learn

Understand the underlying concepts of XGBoost.
Code in Python and R to implement XGBoost.
Apply XGBoost to a business problem in the form of a case study.
Utilize XGBoost to solve similar business problems in the future.
Understand how to effectively communicate the results of using XGBoost to stakeholders.
Enhance your skills in coding and machine learning through hands-on practice with XGBoost.
Understand the role of machine learning in business and how it can be used to improve decision-making and solve complex problems.
Use machine learning techniques, including XGBoost, to analyze and interpret data in the context of business applications.

Who Should Attend

Data scientists looking to improve their machine learning skills and understanding of XGBoost.
Business professionals who want to learn how to apply XGBoost to solve business problems and make data-driven decisions.
Computer science students interested in learning about the latest machine learning techniques and how to implement them.
Entrepreneurs looking to leverage the power of machine learning to improve their businesses.
Data analysts who want to expand their toolkit and learn how to use XGBoost to better understand and analyze data.

Target Audiences

Data scientists looking to improve their machine learning skills and understanding of XGBoost.
Business professionals who want to learn how to apply XGBoost to solve business problems and make data-driven decisions.
Computer science students interested in learning about the latest machine learning techniques and how to implement them.
Entrepreneurs looking to leverage the power of machine learning to improve their businesses.
Data analysts who want to expand their toolkit and learn how to use XGBoost to better understand and analyze data.

XGBoost is a state-of-the-art Machine Learning algorithm. It is well known for being faster to compute and its results more accurate than other well-known techniques like Neural Networks or Random Forest. XGBoost is also one of the most preferred algorithms in Data Science competitions around the world. Fortunately, it is a very accessible algorithm to grasp and implement.

The course focus is on the application of XGBoost in the business world. We will solve a Direct Marketing case study and conclude that we can increase our sales efficiency by 50% while having minimal impact on revenue.

WHY XGBOOST FOR BUSINESS IN Python?

The learning process is divided into 2. The first part is the Intuition tutorial. The aim is for you to understand why the method makes sense. As well, we will go through all underlying concepts you need to know to implement XGBoost. The second part is the Practice tutorials, where we will code in Python and R, and solve together a Direct Marketing problem.

1| BUSINESS EXAMPLE TO FOSTER INTUITION

We will start the intuition tutorial by explaining the Case Study and the problem statement. One of the benefits of giving actual business problems as examples is that you will find similar or even equal issues in your current company. In turn, this enables you to apply what you have learned immediately.

By the end of the intuition tutorial, you will be able to easily explain XGBoost to your colleagues, manager, and stakeholders.

2| HANDS-ON CODING IN PYTHON AND R

We will code together. We will start from scratch, building the code line by line. As also an online coding student, I feel this has been the easiest way to learn.

On top, we write the code so you can download it and use it in your work and projects. Additionally, I will explain what you have to change to use in your dataset and solve the problem you have at hand.

XGBoost for Business in Python and R is a course that naturally extends into your career.

***SUMMARY

The course is an end-to-end application of XGBoost with a simple intuition tutorial, hands-on coding, and, most importantly, is actionable in your career.

Feel free to reach out in case you have any questions, and I hope to see you inside!

Diogo

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Installing Python and Spyder

Lecture 3: Installing R and RStudio

Lecture 4: How to get more from the course

Lecture 5: Reviews and future of this course

Chapter 2: Intuition Tutorial

Lecture 1: Problem Statement

Lecture 2: Introducing XGBoost

Lecture 3: How XGBoost works

Lecture 4: XGBoost quirks

Lecture 5: Dummy variable trap

Lecture 6: Training and test set

Lecture 7: Confusion Matrix

Lecture 8: Area Under the Curve (AUC ROC)

Lecture 9: Root Square Mean Error

Lecture 10: Variance vs. Bias trade off

Lecture 11: Parameter tuning and Cross Validation

Lecture 12: SHAP Values

Lecture 13: Your feedback is valuable

Chapter 3: Python Practice Tutorial

Lecture 1: How to get the dataset

Lecture 2: Loading data

Lecture 3: Isolating numerical X and Y variables

Lecture 4: Training and test set

Lecture 5: Transforming the Y variable

Lecture 6: Creating XGBoost Matrices

Lecture 7: Setting XGBoost Parameters

Lecture 8: First XGBoost model

Lecture 9: Predicting with XGBoost

Lecture 10: Confusion Matrix

Lecture 11: Creating dummy variables

Lecture 12: Forming last dataset

Lecture 13: Saving variable names

Lecture 14: Training and test set part 2

Lecture 15: Creating XGBoost Matrices part 2

Lecture 16: Second XGBoost model

Lecture 17: Predictions and Confusion Matrix part 2

Lecture 18: Parallel Processing

Lecture 19: Setting Cross Validation parameters

Lecture 20: Tuning parameters

Lecture 21: Importing Classifier

Lecture 22: Assembling Cross Validation

Lecture 23: Setting Validation parameters

Lecture 24: Parameter Tuning round 1

Lecture 25: Parameter Tuning round 2

Lecture 26: Final XGBoost model

Lecture 27: Business Perspective

Lecture 28: Driver Importance

Lecture 29: SHAP values

Chapter 4: R Practice Tutorial

Lecture 1: Loading and inspecting data

Lecture 2: Isolating numerical variables

Lecture 3: Summary Statistics and Correlation Matrix

Lecture 4: Preparing first dataset

Lecture 5: Training and test set

Lecture 6: Isolating X and Y variables

Lecture 7: Setting XGBoost Parameters

Lecture 8: Parallel Processing

Lecture 9: Running XGBoost

Lecture 10: Predicting with XGBoost

Lecture 11: Confusion Matrix

Lecture 12: Transforming factors into numerical variables

Lecture 13: Preparing final dataset

Lecture 14: Second XGBoost model

Lecture 15: Predictions and Confusion Matrix part 2

Lecture 16: Start Parallel Processing

Lecture 17: Cross Validation inputs

Lecture 18: Cross Validation Parameters

Lecture 19: Parameters to tune

Lecture 20: Parameter Tuning round 1

Lecture 21: Parameter Tuning round 2

Lecture 22: Final XGBoost model

Lecture 23: Business Perspective

Lecture 24: Importance Drivers and SHAP Values

Lecture 25: End of Course Feedback

Chapter 5: Bonus Section

Lecture 1: Bonus Lecture

Instructors

Diogo Alves de Resende
Analytics and Data Science expert

Rating Distribution

1 stars: 2 votes
2 stars: 3 votes
3 stars: 25 votes
4 stars: 72 votes
5 stars: 127 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!

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