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Description

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
Who this course is for:
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.

What you'll 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.

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction
5
1.1-Introduction
1.2-Installing Python and Spyder
1.3-Installing R and RStudio
1.4-How to get more from the course
1.5-Reviews and future of this course
2-Intuition Tutorial
13
2.1-Problem Statement
2.2-Introducing XGBoost
2.3-How XGBoost works
2.4-XGBoost quirks
2.5-Dummy variable trap
2.6-Training and test set
2.7-Confusion Matrix
2.8-Area Under the Curve (AUC ROC)
2.9-Root Square Mean Error
2.10-Variance vs. Bias trade off
2.11-Parameter tuning and Cross Validation
2.12-SHAP Values
2.13-Your feedback is valuable
3-Python - XGBoost Project
15
3.1-How to get the dataset
3.2-Python - XGBoost Setup
3.3-Python - Data Processing
3.4-Python - First XGBoost Model
3.5-Python - Evaluating XGBoost Model
3.6-Python - Evaluating XGBoost Model part 2
3.7-Python - Building Functions for Binary Model Assessment
3.8-Python - Data Processing Part 2
3.9-Python - Second XGBoost Model
3.10-Random Parameter Tuning
3.11-Python - Parameter Tuning
3.12-Python - Final XGBoost Model
3.13-Python - SHAP Importance and Summary Plot
3.14-Python - SHAP Dependence and Force Plots
3.15-Python - SHAP Waterfall and Cohort Deep Dives
4-R Practice Tutorial
26
4.1-Course Materials
4.2-Loading and inspecting data
4.3-Isolating numerical variables
4.4-Summary Statistics and Correlation Matrix
4.5-Preparing first dataset
4.6-Training and test set
4.7-Isolating X and Y variables
4.8-Setting XGBoost Parameters
4.9-Parallel Processing
4.10-Running XGBoost
4.11-Predicting with XGBoost
4.12-Confusion Matrix
4.13-Transforming factors into numerical variables
4.14-Preparing final dataset
4.15-Second XGBoost model
4.16-Predictions and Confusion Matrix part 2
4.17-Start Parallel Processing
4.18-Cross Validation inputs
4.19-Cross Validation Parameters
4.20-Parameters to tune
4.21-Parameter Tuning round 1
4.22-Parameter Tuning round 2
4.23-Final XGBoost model
4.24-Business Perspective
4.25-Importance Drivers and SHAP Values
4.26-End of Course Feedback
5-Bonus Section
1
5.1-Bonus Lecture