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Applied Classification with XGBoost 1

Matt Harrison

Using Jupyter notebook demos, you'll experience preliminary exploratory data analysis. You will create a classification model with XGBoost. Using third-party libraries, you will explore feature interactions, and explaining the models.

Are you a data professional who needs a complete, end-to-end classification demonstration of XGBoost and the libraries surrounding it? In this course, Applied Classification with XGBoost, you'll get introduced to the popular XGBoost library, an advanced ML tool for classification and regression. First, you'll explore the underpinnings of the XGBoost algorithm, see a base-line model, and review the decision tree. Next, you'll discover how boosting works using Jupyter Notebook demos, as well as see preliminary exploratory data analysis in action. Finally, you'll learn how to create, evaluate, and explain data using third party libraries. You won't be using the Iris or Titanic data-set, you'll use real survey data! By the end of this course, you'll be able to take raw data, prepare it, model a classifier, and explore the performance of it. Using the provided notebook, you can follow along on your own machine, or take and adapt the code to your needs.

This course is no longer available. Find something similar by browsing:

XGBoost Classification Gradient Boosting Model Evaluation Model Interpretation Feature Interactions Hyperparameter Tuning

What's inside

Syllabus

Course Overview

Getting Started

Classifying with Gradient Boosting

Tuning: Regularization and Hyperparameters

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Teaches classifying with gradient boosting, a standard in machine learning

Taught by Matt Harrison, who is recognized for his work in machine learning

Covers evaluating models and exploring metrics, core skills for a data scientist

Examines XGBoost models, which are highly relevant to data science

Offers hands-on labs and interactive materials, which strengthen foundational knowledge

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Reviews summary

Applied xgboost for data professionals

According to students, 'Applied Classification with XGBoost 1' is a largely positive course, particularly praised for its practical, hands-on approach using real-world survey data, moving beyond typical datasets. Learners highlight the clear explanations and the valuable focus on model interpretation and evaluation. The Jupyter Notebook demos are consistently noted as helpful and easy to follow. However, some students, especially beginners, found the pace fast and suggested it assumes a certain level of prior knowledge. A common suggestion was the inclusion of more practice exercises.

The included Jupyter Notebook demos are well-explained and easy to follow.

"The Jupyter Notebook demos are well-explained and easy to follow."

"The code examples are practical. I could easily follow along with the provided notebooks."

"The notebooks are fantastic resources."

"The practical approach with Jupyter notebooks is very effective."

Praised for clear explanations and valuable insights into model interpretation.

"The instructor explains complex concepts clearly."

"I appreciated the focus on model evaluation and interpretation, which are critical skills."

"The instructor's insights into model explanation are invaluable."

"I loved the clear explanations and the depth of coverage on model interpretation."

Emphasizes hands-on application with practical, non-standard datasets.

"It covers XGBoost in a practical, hands-on way using real-world data, which is a huge plus."

"The use of real survey data instead of standard datasets was refreshing."

"This course delivers exactly what it promises – applied classification with XGBoost."

"The real-world data and hands-on approach make this stand out."

Learners wished for more practice exercises or assignments beyond the demos.

"More practice exercises would also be beneficial."

"I would have liked to see more challenges or assignments to reinforce learning beyond just running the provided notebooks."

Some beginners found the course fast-paced, suggesting it suits intermediate learners.

"The course assumes a certain level of prior knowledge that wasn't explicitly stated. As a beginner, I found myself struggling."

"More introductory material on machine learning fundamentals or a prerequisite section would be helpful."

"It was a bit fast-paced for me. I have some ML background but not extensive, and I sometimes got lost."

"It feels like it's geared more towards intermediate to advanced users rather than those just getting into applied ML."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Applied Classification with XGBoost 1 with these activities:

Review decision tree fundamentals

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Strengthen the foundational understanding of decision trees, a prerequisite for XGBoost.

Browse courses on Decision Trees

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Revisit previous coursework or materials on decision trees.
Complete practice problems and exercises on decision tree algorithms.

XGBoost algorithm deep dive

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Enhance understanding of the theoretical foundations and mathematical underpinnings of XGBoost.

Browse courses on XGBoost

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Explore external resources and tutorials on XGBoost algorithms.
Delve into research papers and academic literature on XGBoost.

XGBoost resource hub

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Expand knowledge and establish a personalized reference by gathering and organizing relevant XGBoost resources.

Browse courses on XGBoost

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Collect articles, tutorials, and documentation on XGBoost.
Curate and organize the materials into a central location.
Share the resource hub with others for mutual benefit.

Five other activities

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Interactive code-along exercises

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Gain hands-on experience and build confidence in applying XGBoost classification techniques.

Browse courses on XGBoost

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Follow along with the provided Jupyter notebook demos.
Experiment with different parameters and observe the impact on model performance.

Collaborative model evaluation

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Enhance critical thinking and broaden perspectives by discussing and evaluating models with peers.

Browse courses on XGBoost

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Form study groups with fellow students.
Share and present individual classification models.
Provide constructive feedback and collectively improve model performance.

Feature interaction exploration

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Deepen understanding of feature engineering and its role in improving model performance.

Browse courses on Feature Engineering

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Use third-party libraries to generate and visualize feature interactions.
Analyze the interactions and identify patterns that contribute to model accuracy.

Real-world classification project

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Apply XGBoost classification skills to solve a real-world problem, solidifying understanding and demonstrating proficiency.

Browse courses on XGBoost

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Identify a suitable classification problem.
Prepare and preprocess the dataset.
Train and evaluate a XGBoost classification model.
Deploy the model and interpret results.

XGBoost hackathon

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Challenge oneself, gain experience, and showcase expertise by participating in a XGBoost-focused hackathon.

Browse courses on XGBoost

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Identify and register for a relevant hackathon.
Form a team or collaborate with others.
Develop a novel classification solution using XGBoost.
Submit the solution and compete for recognition.

Career center

Learners who complete Applied Classification with XGBoost 1 will develop knowledge and skills that may be useful to these careers:

Data Scientist

As a Data Scientist, your responsibilities will include designing and building machine learning models that detect patterns and predict outcomes. XGBoost, a powerful library for classification and regression, can be a valuable tool in your arsenal to effectively develop predictive models. This course provides a comprehensive foundation in XGBoost, enabling you to build, evaluate, and interpret complex models to derive actionable insights from data.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

Machine Learning Engineers are responsible for developing, deploying, and maintaining machine learning models. XGBoost, known for its efficiency and accuracy, is widely used in industry for solving complex machine learning problems. This course will equip you with the skills to build and optimize XGBoost models, enabling you to contribute effectively to machine learning projects.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

Data Analysts are responsible for extracting insights from data to inform decision-making. XGBoost is a powerful tool for building predictive models that can uncover hidden patterns and trends in data. This course will provide you with the knowledge and skills to leverage XGBoost to gain deeper insights from data, enhancing your ability to make data-driven recommendations.

See salaries and explore the career path for Data Analyst

Quantitative Analyst

Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. XGBoost, with its ability to handle large datasets and complex interactions, is becoming increasingly popular in the finance industry. This course will provide you with a strong foundation in XGBoost, enabling you to build and evaluate models for risk assessment, portfolio optimization, and other financial applications.

See salaries and explore the career path for Quantitative Analyst

Statistician

Statisticians apply statistical methods to analyze data and draw conclusions. XGBoost, with its advanced algorithms and ability to handle complex relationships, can be a valuable tool for statisticians to build predictive models and gain deeper insights from data. This course will provide you with a comprehensive understanding of XGBoost, enabling you to leverage its capabilities for statistical analysis and modeling.

See salaries and explore the career path for Statistician

Data Engineer

Data Engineers are responsible for designing, building, and maintaining data pipelines and infrastructure. XGBoost, known for its efficiency and scalability, is often used for building models in production environments. This course will provide you with the skills to prepare data, optimize XGBoost models, and deploy them into production, enabling you to contribute to the development of robust and scalable data pipelines.

See salaries and explore the career path for Data Engineer

Business Analyst

Business Analysts use data to identify opportunities and solve problems within an organization. XGBoost, with its ability to build accurate predictive models, can be a valuable tool for Business Analysts to gain insights from data and make data-driven recommendations. This course will provide you with the knowledge and skills to leverage XGBoost for business analysis, enabling you to drive informed decision-making and contribute to the success of your organization.

See salaries and explore the career path for Business Analyst

Market Research Analyst

Market Research Analysts conduct research to understand market trends and consumer behavior. XGBoost, with its ability to handle large datasets and complex relationships, can be a valuable tool for Market Research Analysts to build predictive models that uncover consumer insights and market opportunities. This course will provide you with the skills to leverage XGBoost for market research, enabling you to gain a deeper understanding of your target audience and make informed marketing decisions.

See salaries and explore the career path for Market Research Analyst

Risk Analyst

Risk Analysts assess and manage risks for various organizations. XGBoost, with its ability to build predictive models and handle large datasets, is becoming increasingly popular in risk management. This course will provide you with a strong foundation in XGBoost, enabling you to build and evaluate models for risk assessment, fraud detection, and other risk management applications.

See salaries and explore the career path for Risk Analyst

Financial Analyst

Financial Analysts evaluate and make recommendations on investments and financial decisions. XGBoost, with its ability to build predictive models and handle large datasets, is finding applications in financial analysis. This course will provide you with the skills to leverage XGBoost for financial analysis, enabling you to make more informed investment decisions and provide valuable insights to clients.

See salaries and explore the career path for Financial Analyst

Actuary

Actuaries use mathematical and statistical methods to assess risk and uncertainty. XGBoost, with its advanced algorithms and ability to handle complex relationships, can be a valuable tool for Actuaries to build predictive models for risk assessment, insurance pricing, and other actuarial applications. This course will provide you with a comprehensive understanding of XGBoost, enabling you to leverage its capabilities for actuarial analysis and modeling.

See salaries and explore the career path for Actuary

Software Engineer

Software Engineers design, develop, and maintain software systems. XGBoost, known for its efficiency and scalability, is often used in software development for building machine learning models and data pipelines. This course will provide you with the skills to integrate XGBoost into your software projects, enabling you to develop robust and scalable software solutions.

See salaries and explore the career path for Software Engineer

Data Architect

Data Architects design and manage data systems and infrastructure. XGBoost, with its ability to handle large datasets and complex relationships, is becoming increasingly popular for building data models and pipelines. This course will provide you with a strong foundation in XGBoost, enabling you to design and implement data architectures that leverage its capabilities for efficient data processing and modeling.

See salaries and explore the career path for Data Architect

IT Consultant

IT Consultants provide technology solutions and advice to organizations. XGBoost, with its ability to build accurate predictive models, can be a valuable tool for IT Consultants to provide data-driven recommendations and help organizations make informed decisions. This course will provide you with the knowledge and skills to leverage XGBoost for IT consulting, enabling you to add value to your clients and contribute to their success.

See salaries and explore the career path for IT Consultant

BI Analyst

BI Analysts use data to identify trends and patterns and provide insights to businesses. XGBoost, with its ability to build accurate predictive models, can be a valuable tool for BI Analysts to uncover hidden insights from data and make data-driven recommendations. This course will provide you with the knowledge and skills to leverage XGBoost for business intelligence, enabling you to drive informed decision-making and contribute to the success of your organization.

See salaries and explore the career path for BI Analyst

Reading list

We've selected 14 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Applied Classification with XGBoost 1.

Deep Learning

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Provides a comprehensive overview of deep learning techniques. It valuable resource for those who want to understand the mathematical foundations of deep learning.

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Applied Classification with XGBoost 1

What's inside

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Reviews summary

Applied xgboost for data professionals

Activities

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