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Machine Learning with XGBoost Using scikit-learn in Python

Mike West

XGBoost is the most winning supervised machine learning approach in competitive modeling on structured datasets. This course will teach you the basics of XGBoost, including basic syntax, functions, and implementing the model in the real world.

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XGBoost is the most winning supervised machine learning approach in competitive modeling on structured datasets. This course will teach you the basics of XGBoost, including basic syntax, functions, and implementing the model in the real world.

At the core of applied machine learning is supervised machine learning. In this course, Machine Learning with XGBoost Using scikit-learn in Python, you will learn how to build supervised learning models using one of the most accurate algorithms in existence. First, you will discover what XGBoost is and why it’s revolutionized competitive modeling. Next, you will explore the importance of data wrangling and see how clean data affects XGBoost’s performance. Finally, you will learn how to build, train, and score XGBoost models for real-world performance. When you are finished with this course, you will have a foundational knowledge of XGBoost that will help you as you move forward to becoming a machine learning engineer.

This course is no longer available. Find something similar by browsing:
XGBoost Machine Learning Python scikit-learn Data Wrangling Feature Selection

What's inside

Syllabus

Course Overview
Introducing Essential Processes
Preparing Data for Gradient Boosting
Scoring XGBoost Models
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Suitable for those requiring accurate modeling on organized data
Appropriate for those desiring a knowledge base for machine learning engineering
Beginner-friendly, while remaining valuable for intermediate learners
Provides hands-on experience in implementing XGBoost models
Mike West known for expertise and practical knowledge in machine learning
Requires proficiency in Python and familiarity with data wrangling

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Reviews summary

Xgboost foundational & practical ml

According to learners, this course provides a strong foundational knowledge of XGBoost, particularly excelling in its practical applications and hands-on coding exercises using scikit-learn. Many students found the instructor's explanations to be clear and engaging, demystifying complex concepts. While largely seen as a highly practical course applicable to data science careers, some reviews indicate it might require some prior machine learning knowledge and might be less suitable for absolute beginners seeking deep theoretical dives. There were occasional mentions of minor code compatibility issues in older reviews, but more recent feedback emphasizes the course's overall structure and applicability.
Instructor effectively demystifies complex topics.
"The instructor explains complex concepts clearly, and the hands-on coding exercises using scikit-learn are invaluable."
"Excellent course! The instructor's teaching style is engaging..."
"Loved this course! It demystified XGBoost for me. The instructor made complex topics easy to grasp."
Focuses on real-world implementation with hands-on labs.
"The hands-on coding exercises using scikit-learn are invaluable. I particularly appreciated the section on feature selection, which is highly practical..."
"The content is directly applicable to my work as a data scientist... The labs were particularly useful."
"I feel much more confident applying XGBoost in my own projects now. Highly practical."
Older code examples may cause minor compatibility issues.
"I noticed some of the code examples could use an update to reflect newer versions of libraries, which caused minor compatibility issues."
"It feels a bit dated in some parts, especially concerning package versions. I had to spend time troubleshooting due to this."
"Some of the package versions mentioned in the course were a bit old, requiring me to manually adjust my environment."
Beneficial for those with some existing machine learning background.
"While it claims to be 'foundational,' some prior knowledge of machine learning is definitely beneficial. It's not for absolute beginners in ML."
"The pace was just right for someone with a basic ML background."
"I found that the course was best suited for learners who already have a basic grasp of machine learning concepts."
Provides solid basics but lacks advanced theoretical depth.
"I felt it could have gone a bit deeper into hyperparameter tuning and advanced ensemble techniques..."
"Disappointed with this course. It felt too superficial. I was hoping for more theoretical depth and detailed case studies."
"Perhaps more explanation on the theoretical aspects of gradient boosting would be useful for a more complete understanding."

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 Machine Learning with XGBoost Using scikit-learn in Python with these activities:
Review the basics of supervised machine learning
Refresh your understanding of the fundamental concepts of supervised machine learning to strengthen the foundation for XGBoost.
Browse courses on Supervised Machine Learning
Show steps
  • Go through online tutorials or documentation on supervised machine learning
  • Review your notes or previous coursework on the topic
  • Complete practice exercises or quizzes to test your understanding
Review Python syntax
Python syntax is the cornerstone of effective machine learning in this course and refreshing in advance will help you stay ahead of the curve.
Browse courses on Python Syntax
Show steps
  • Review Python variable types and data structures
  • Take an online Python syntax quiz
Seek guidance from experienced XGBoost practitioners
Connect with experienced XGBoost practitioners to gain valuable insights, mentorship, and support in your learning journey.
Browse courses on XGBoost
Show steps
  • Identify experienced XGBoost practitioners in your network or online communities
  • Reach out to them and request guidance or mentorship
  • Attend industry events or webinars to connect with potential mentors
Eight other activities
Expand to see all activities and additional details
Show all 11 activities
Compile resources and tools for XGBoost learning
Organize and gather resources such as tutorials, documentation, and tools related to XGBoost, providing a valuable reference for your learning.
Browse courses on XGBoost
Show steps
  • Identify and collect relevant resources from online sources
  • Create a central repository or document to organize the resources
  • Categorize and label the resources for easy access
Work through XGBoost coding examples
XGBoost is at the core of this course and working through the coding examples will provide a solid foundation.
Show steps
  • Find a set of XGBoost coding examples online
  • Start working through the examples, one by one
  • Debug any errors you encounter
Practice XGBoost by solving coding challenges
Solve coding challenges based on the same principles used by XGBoost to test your understanding of the core concepts.
Browse courses on XGBoost
Show steps
  • Find coding challenges related to XGBoost
  • Solve the challenges using the concepts learned in the course
  • Review your solutions against others or discuss them with your peers
Tutor others in XGBoost
Reinforce your understanding of XGBoost by teaching it to others, solidifying your knowledge and identifying areas for improvement.
Browse courses on XGBoost
Show steps
  • Find opportunities to tutor others in XGBoost
  • Prepare lesson plans and materials to teach the concepts effectively
  • Provide guidance and support to your students
Follow a tutorial on XGBoost hyperparameter tuning
Hyperparameter tuning is essential for optimizing XGBoost models, so completing this tutorial will enhance your understanding.
Show steps
  • Search for a tutorial on XGBoost hyperparameter tuning
  • Follow the tutorial step-by-step
Attend XGBoost workshops or conferences
Engage in XGBoost-focused workshops or conferences to gain hands-on experience, learn from experts, and expand your knowledge.
Browse courses on XGBoost
Show steps
  • Research and identify upcoming XGBoost workshops or conferences
  • Register and attend the events
  • Actively participate in sessions and discussions
Contribute to open-source projects related to XGBoost
Gain practical experience and deepen your understanding by contributing to open-source projects related to XGBoost.
Browse courses on XGBoost
Show steps
  • Identify open-source projects related to XGBoost
  • Review the project's documentation and codebase
  • Identify areas where you can contribute
Develop a personal project using XGBoost
Work on a project that applies the concepts of XGBoost to a real-life problem, strengthening your understanding and practical skills.
Browse courses on XGBoost
Show steps
  • Identify a problem or dataset that can benefit from XGBoost
  • Design and implement an XGBoost model for the problem
  • Evaluate the performance of the model and make improvements
  • Deploy the model and monitor its performance

Career center

Learners who complete Machine Learning with XGBoost Using scikit-learn in Python will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers design, build, and maintain machine learning models. They work on a variety of projects, from developing new machine learning algorithms to deploying machine learning models into production. Machine Learning with XGBoost Using scikit-learn in Python can help Machine Learning Engineers build more accurate and efficient models, which can lead to better machine learning products. This course can also help Machine Learning Engineers learn how to work with real-world data, which is essential for building successful machine learning models.
See salaries and explore the career path for Machine Learning Engineer
Software Engineer
Software Engineers design, build, and maintain software systems. They work on a variety of projects, from small, personal applications to large, enterprise-scale systems. Machine Learning with XGBoost Using scikit-learn in Python can help Software Engineers build more accurate and efficient models, which can lead to better software products. This course can also help Software Engineers learn how to work with real-world data, which is essential for building successful software systems.
See salaries and explore the career path for Software Engineer
Data Scientist
Data Scientists use data to solve business problems. They collect, clean, and analyze data to identify patterns and trends. Machine Learning with XGBoost Using scikit-learn in Python can help Data Scientists build more accurate and efficient models, which can lead to better insights and decisions. This course can also help Data Scientists learn how to work with real-world data, which is essential for building successful data science models.
See salaries and explore the career path for Data Scientist
Data Engineer
Data Engineers design, build, and maintain data pipelines. They work with data scientists and analysts to ensure that data is clean and accessible. Machine Learning with XGBoost Using scikit-learn in Python can help Data Engineers build more accurate and efficient models, which can lead to better data pipelines. This course can also help Data Engineers learn how to work with real-world data, which is essential for building successful data pipelines.
See salaries and explore the career path for Data Engineer
Statistician
Statisticians use data to solve problems. They collect, clean, and analyze data to identify patterns and trends. Machine Learning with XGBoost Using scikit-learn in Python can help Statisticians build more accurate and efficient models, which can lead to better insights and decisions. This course can also help Statisticians learn how to work with real-world data, which is essential for building successful statistical models.
See salaries and explore the career path for Statistician
Analyst
Analysts use data to solve problems. They collect, clean, and analyze data to identify patterns and trends. Machine Learning with XGBoost Using scikit-learn in Python can help Analysts build more accurate and efficient models, which can lead to better insights and decisions. This course can also help Analysts learn how to work with real-world data, which is essential for building successful data analysis models.
See salaries and explore the career path for Analyst
Data Analyst
Data Analysts use data to solve business problems. They collect, clean, and analyze data to identify patterns and trends. Machine Learning with XGBoost Using scikit-learn in Python can help Data Analysts build more accurate and efficient models, which can lead to better insights and decisions. This course can also help Data Analysts learn how to work with real-world data, which is essential for building successful data analysis models.
See salaries and explore the career path for Data Analyst
Business Analyst
Business Analysts help businesses understand their customers and needs. They use data to identify opportunities and develop strategies to improve business performance. Machine Learning with XGBoost Using scikit-learn in Python can help Business Analysts build more accurate and efficient models, which can lead to better insights and decisions. This course can also help Business Analysts learn how to work with real-world data, which is essential for building successful business analysis models.
See salaries and explore the career path for Business Analyst
Marketing Analyst
Marketing Analysts use data to understand customer behavior and trends. They use this information to develop marketing campaigns that are more likely to be successful. Machine Learning with XGBoost Using scikit-learn in Python can help Marketing Analysts build more accurate and efficient models, which can lead to better insights and decisions. This course can also help Marketing Analysts learn how to work with real-world data, which is essential for building successful marketing analysis models.
See salaries and explore the career path for Marketing Analyst
Consultant
Consultants help businesses solve problems. They provide advice and guidance on a variety of topics, including business strategy, operations, and technology. Machine Learning with XGBoost Using scikit-learn in Python can help Consultants build more accurate and efficient models, which can lead to better insights and decisions. This course can also help Consultants learn how to work with real-world data, which is essential for building successful consulting models.
See salaries and explore the career path for Consultant
Operations Research Analyst
Operations Research Analysts use data to solve complex business problems. They use mathematical models to identify the best course of action. Machine Learning with XGBoost Using scikit-learn in Python can help Operations Research Analysts build more accurate and efficient models, which can lead to better decision-making. This course can also help Operations Research Analysts learn how to work with real-world data, which is essential for building successful operations research models.
See salaries and explore the career path for Operations Research Analyst
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to create products that meet the needs of customers. Machine Learning with XGBoost Using scikit-learn in Python can help Product Managers build more accurate and efficient models, which can lead to better product decisions. This course can also help Product Managers learn how to work with real-world data, which is essential for building successful products.
See salaries and explore the career path for Product Manager
Risk Analyst
Risk Analysts identify and assess risks to businesses. They use data to develop strategies to mitigate these risks. Machine Learning with XGBoost Using scikit-learn in Python can help Risk Analysts build more accurate and efficient models, which can lead to better risk management decisions. This course can also help Risk Analysts learn how to work with real-world data, which is essential for building successful risk analysis models.
See salaries and explore the career path for Risk Analyst
Quantitative Analyst
Quantitative Analysts use data to make investment decisions. They use mathematical models to identify undervalued assets. Machine Learning with XGBoost Using scikit-learn in Python can help Quantitative Analysts build more accurate and efficient models, which can lead to better investment decisions. This course can also help Quantitative Analysts learn how to work with real-world data, which is essential for building successful quantitative analysis models.
See salaries and explore the career path for Quantitative Analyst
Actuary
Actuaries use data to assess risk and uncertainty. They use mathematical models to develop insurance policies and other financial products. Machine Learning with XGBoost Using scikit-learn in Python can help Actuaries build more accurate and efficient models, which can lead to better risk management decisions. This course can also help Actuaries learn how to work with real-world data, which is essential for building successful actuarial models.
See salaries and explore the career path for Actuary

Reading list

We've selected 12 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 Machine Learning with XGBoost Using scikit-learn in Python.
Cover image
Hands-On Machine Learning with Scikit-Learn, Keras,...
Save
Is an excellent resource for learners who want to go in-depth with XGBoost and Python's scikit-learn library. It provides a comprehensive overview of machine learning concepts, including supervised learning, model evaluation, and feature engineering.
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Paperback
$$$
Hands-On Machine Learning with Scikit-Learn, Keras,...
Kindle Edition
$$
Machine Learning Con Pytorch Y Scikit Lean
Save
Provides a broad overview of machine learning algorithms and techniques, including supervised learning, unsupervised learning, and deep learning. It serves as an excellent foundation for learners who want to develop a strong understanding of machine learning concepts.
(Español) Machine Learning con PyTorch y Scikit-Learn:...
Paperback
$$$
Cover image
Deep Learning
Save
While this book focuses on deep learning, it provides a thorough overview of machine learning concepts and algorithms, including supervised learning, unsupervised learning, and reinforcement learning. It's a valuable resource for learners who want to gain a deeper understanding of the theoretical foundations of machine learning, even if they're not primarily interested in deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Machine Learning, Animated
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This introductory book provides a gentle introduction to machine learning concepts, making it suitable for learners with no prior knowledge of the field. It covers supervised learning, unsupervised learning, and model evaluation.
Machine Learning, Animated
Paperback
Check
Machine Learning, Animated
Kindle Edition
Check
Cover image
The Elements of Statistical Learning
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This classic textbook provides a comprehensive overview of statistical learning methods, including supervised learning, unsupervised learning, and regression analysis. It's a valuable resource for learners who want to develop a strong foundation in statistical machine learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Tidy Modeling with R
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Provides a practical guide to predictive modeling, covering topics such as data preprocessing, model selection, and model evaluation. It's a valuable resource for learners who want to develop practical skills in machine learning.
Tidy Modeling with R
Paperback
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Tidy Modeling with R
Kindle Edition
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Probabilistic Machine Learning
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This advanced textbook provides a rigorous treatment of machine learning from a probabilistic perspective. It's a valuable resource for learners who want to develop a deep understanding of the theoretical foundations of machine learning.
Probabilistic Machine Learning: Advanced Topics...
Hardcover
$$$$
Bayesian Reasoning and Machine Learning South Asian...
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Provides a comprehensive overview of Bayesian reasoning and its applications in machine learning. It's a valuable resource for learners who want to develop a strong foundation in Bayesian machine learning.
Bayesian Reasoning and Machine Learning Paperback
Paperback
$$$
Pattern Recognition and Machine Learning
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This advanced textbook provides a comprehensive overview of pattern recognition and machine learning. It's a valuable resource for learners who want to develop a deep understanding of the theoretical foundations of machine learning and statistical pattern recognition.
Pattern Recognition and Machine Learning...
Hardcover
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Pattern Recognition and Machine Learning...
Paperback
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Pattern Recognition and Machine Learning (text...
Unknown Binding
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MACHINE LEARNING
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This advanced textbook provides a thorough overview of machine learning algorithms and their theoretical foundations. It's a valuable resource for learners who want to develop a deep understanding of machine learning algorithms and their mathematical underpinnings.
Machine Learning: An Algorithmic Perspective,...
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Machine Learning: An Algorithmic Perspective...
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An Algorithmic Perspective on Machine Learning (2nd...
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Machine Learning: An Algorithmic Perspective,...
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Machine Learning for Hackers
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This introductory book provides a hands-on approach to machine learning, covering topics such as data preprocessing, model selection, and model evaluation. It's a valuable resource for learners who want to develop practical skills in machine learning without getting bogged down in theory.
Machine Learning for Hackers: Case Studies and...
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Machine Learning for Hackers: Case Studies and...
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Data Mining
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This classic textbook provides a comprehensive overview of data mining and machine learning techniques. It's a valuable resource for learners who want to develop a strong foundation in data mining and machine learning.
Data Mining: Practical Machine Learning Tools and...
Paperback
$$$
DATA MINING, 4TH EDITION
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Data Mining: Practical Machine Learning Tools and...
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Course image
Time
93 hours
Level
Intermediate
Via
Pluralsight
Instructor
Mike West
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Suitable for those requiring accurate modeling on organized data
Appropriate for those desiring a knowledge base for machine learning engineering
Beginner-friendly, while remaining valuable for intermediate learners
Provides hands-on experience in implementing XGBoost models
Mike West known for expertise and practical knowledge in machine learning
Requires proficiency in Python and familiarity with data wrangling
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