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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.
Read more
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.
Read more
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.
Register for this course and see more details by visiting:
OpenCourser.com/course/kypyde/applied
What's inside
Syllabus
Course Overview
Getting Started
Classifying with Gradient Boosting
Tuning: Regularization and Hyperparameters
Read more
Syllabus
Course Overview
Getting Started
Classifying with Gradient Boosting
Tuning: Regularization and Hyperparameters
Read more
Evaluating Models and Exploring Metrics
Interpreting XGBoost Models
Good to know
Good to know
Know what's good ,
what to watch for , 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
Save this course
Save Applied Classification with XGBoost 1 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 Applied Classification with XGBoost 1 with these
activities:
Review decision tree fundamentals
Show steps
Strengthen the foundational understanding of decision trees, a prerequisite for XGBoost.
Browse courses on
Decision Trees
Show steps
-
Revisit previous coursework or materials on decision trees.
-
Complete practice problems and exercises on decision tree algorithms.
XGBoost algorithm deep dive
Show steps
Enhance understanding of the theoretical foundations and mathematical underpinnings of XGBoost.
Browse courses on
XGBoost
Show steps
-
Explore external resources and tutorials on XGBoost algorithms.
-
Delve into research papers and academic literature on XGBoost.
XGBoost resource hub
Show steps
Expand knowledge and establish a personalized reference by gathering and organizing relevant XGBoost resources.
Browse courses on
XGBoost
Show steps
-
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
Expand to see all activities and additional details
Show all eight activities
Interactive code-along exercises
Show steps
Gain hands-on experience and build confidence in applying XGBoost classification techniques.
Browse courses on
XGBoost
Show steps
-
Follow along with the provided Jupyter notebook demos.
-
Experiment with different parameters and observe the impact on model performance.
Collaborative model evaluation
Show steps
Enhance critical thinking and broaden perspectives by discussing and evaluating models with peers.
Browse courses on
XGBoost
Show steps
-
Form study groups with fellow students.
-
Share and present individual classification models.
-
Provide constructive feedback and collectively improve model performance.
Feature interaction exploration
Show steps
Deepen understanding of feature engineering and its role in improving model performance.
Browse courses on
Feature Engineering
Show steps
-
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
Show steps
Apply XGBoost classification skills to solve a real-world problem, solidifying understanding and demonstrating proficiency.
Browse courses on
XGBoost
Show steps
-
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
Show steps
Challenge oneself, gain experience, and showcase expertise by participating in a XGBoost-focused hackathon.
Browse courses on
XGBoost
Show steps
-
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.
Review decision tree fundamentals
Show steps
Strengthen the foundational understanding of decision trees, a prerequisite for XGBoost.
Browse courses on
Decision Trees
Show steps
Show steps
Show steps
- Revisit previous coursework or materials on decision trees.
- Complete practice problems and exercises on decision tree algorithms.
XGBoost algorithm deep dive
Show steps
Enhance understanding of the theoretical foundations and mathematical underpinnings of XGBoost.
Browse courses on
XGBoost
Show steps
Show steps
Show steps
- Explore external resources and tutorials on XGBoost algorithms.
- Delve into research papers and academic literature on XGBoost.
XGBoost resource hub
Show steps
Expand knowledge and establish a personalized reference by gathering and organizing relevant XGBoost resources.
Browse courses on
XGBoost
Show steps
Show steps
Show steps
- 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
Expand to see all activities and additional details
Show all eight activities
Interactive code-along exercises
Show steps
Gain hands-on experience and build confidence in applying XGBoost classification techniques.
Browse courses on
XGBoost
Show steps
Show steps
Show steps
- Follow along with the provided Jupyter notebook demos.
- Experiment with different parameters and observe the impact on model performance.
Collaborative model evaluation
Show steps
Enhance critical thinking and broaden perspectives by discussing and evaluating models with peers.
Browse courses on
XGBoost
Show steps
Show steps
Show steps
- Form study groups with fellow students.
- Share and present individual classification models.
- Provide constructive feedback and collectively improve model performance.
Feature interaction exploration
Show steps
Deepen understanding of feature engineering and its role in improving model performance.
Browse courses on
Feature Engineering
Show steps
Show steps
Show steps
- 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
Show steps
Apply XGBoost classification skills to solve a real-world problem, solidifying understanding and demonstrating proficiency.
Browse courses on
XGBoost
Show steps
Show steps
Show steps
- 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
Show steps
Challenge oneself, gain experience, and showcase expertise by participating in a XGBoost-focused hackathon.
Browse courses on
XGBoost
Show steps
Show steps
Show steps
- 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
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.
Machine Learning Engineer
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.
Data Analyst
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.
Quantitative 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.
Statistician
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.
Data Engineer
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.
Business Analyst
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.
Market Research 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.
Risk 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.
Financial 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.
Actuary
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.
Software Engineer
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.
Data Architect
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.
IT Consultant
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.
BI Analyst
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.
For more career information including salaries, visit:
OpenCourser.com/course/kypyde/applied
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.
Save
Provides a comprehensive overview of deep learning techniques. It valuable resource for those who want to understand the mathematical foundations of deep learning.
Provides a comprehensive overview of reinforcement learning techniques. It valuable resource for those who want to understand the mathematical foundations of reinforcement learning.
Provides a practical introduction to XGBoost, a powerful machine learning library for classification and regression. It covers the basics of XGBoost, as well as more advanced topics such as hyperparameter tuning and feature engineering.
Provides a comprehensive overview of statistical learning techniques. It valuable resource for those who want to understand the mathematical foundations of machine learning.
Save
Provides a comprehensive overview of machine learning techniques. It valuable resource for those who want to understand the mathematical foundations of machine learning.
Provides a comprehensive overview of machine learning techniques. It valuable resource for those who want to understand the mathematical foundations of machine learning.
Save
Provides a comprehensive overview of machine learning techniques. It valuable resource for those who want to understand the mathematical foundations of machine learning.
Provides a comprehensive overview of machine learning techniques, including classification, regression, clustering, and dimensionality reduction. It valuable resource for those who are new to machine learning or who want to refresh their knowledge of the field.
Provides a comprehensive overview of machine learning techniques, using Python as the programming language. It covers a wide range of topics, from data preprocessing to model evaluation.
Provides a practical introduction to machine learning for hackers. It covers a wide range of topics, from data preprocessing to model evaluation.
Provides a comprehensive overview of machine learning techniques, using R as the programming language. It covers a wide range of topics, from data preprocessing to model evaluation.
Provides a comprehensive overview of data mining techniques, using R as the programming language. It covers a wide range of topics, from data preprocessing to model evaluation.
Provides a practical introduction to machine learning for beginners. It covers a wide range of topics, from data preprocessing to model evaluation.
Provides a practical introduction to machine learning for web developers. It covers a wide range of topics, from data preprocessing to model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/kypyde/applied
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Time
122 hours
Level
Intermediate
Via
Pluralsight
Instructor
Matt Harrison
Language
English
Good to know
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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