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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/64aetr/machine
What's inside
Syllabus
Course Overview
Introducing Essential Processes
Preparing Data for Gradient Boosting
Scoring XGBoost Models
Read more
Syllabus
Course Overview
Introducing Essential Processes
Preparing Data for Gradient Boosting
Scoring XGBoost Models
Read more
Saving the Trained Model
Selecting Features in Gradient Boosting
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Machine Learning with XGBoost Using scikit-learn in Python to your list so you can find it easily later:
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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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Show steps
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
Review the basics of supervised machine learning
Show steps
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
Show steps
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
Show steps
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
Show steps
Show steps
- Review Python variable types and data structures
- Take an online Python syntax quiz
Seek guidance from experienced XGBoost practitioners
Show steps
Connect with experienced XGBoost practitioners to gain valuable insights, mentorship, and support in your learning journey.
Browse courses on
XGBoost
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
XGBoost is at the core of this course and working through the coding examples will provide a solid foundation.
Show steps
Show steps
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
Show steps
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
Show steps
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
Show steps
Reinforce your understanding of XGBoost by teaching it to others, solidifying your knowledge and identifying areas for improvement.
Browse courses on
XGBoost
Show steps
Show steps
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
Show steps
Hyperparameter tuning is essential for optimizing XGBoost models, so completing this tutorial will enhance your understanding.
Show steps
Show steps
Show steps
- Search for a tutorial on XGBoost hyperparameter tuning
- Follow the tutorial step-by-step
Attend XGBoost workshops or conferences
Show steps
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
Show steps
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
Show steps
Gain practical experience and deepen your understanding by contributing to open-source projects related to XGBoost.
Browse courses on
XGBoost
Show steps
Show steps
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
Show steps
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
Show steps
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 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.
Software 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.
Data Scientist
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.
Data Engineer
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.
Analyst
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.
Statistician
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.
Data 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.
Business 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.
Consultant
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.
Operations Research Analyst
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.
Marketing 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.
Risk Analyst
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.
Product Manager
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.
Quantitative 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.
Actuary
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.
For more career information including salaries, visit:
OpenCourser.com/course/64aetr/machine
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.
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.
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.
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.
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.
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.
Save
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.
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.
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.
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.
Save
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.
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.
Save
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.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/64aetr/machine
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Time
93 hours
Level
Intermediate
Via
Pluralsight
Instructor
Mike West
Language
English
Good to know
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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