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Swetha Kolalapudi
Classification problems are common in all domains and tree based models are very effective solutions to these problems. This course is all about tree based models, from simple decision trees, to complex ensemble learning techniques, and more.
Read more
Classification problems are common in all domains and tree based models are very effective solutions to these problems. This course is all about tree based models, from simple decision trees, to complex ensemble learning techniques, and more.
Read more
Classification problems are common in all domains and tree based models are very effective solutions to these problems. This course is all about tree based models, from simple decision trees, to complex ensemble learning techniques, and more.
Machine Learning can sound very complicated, but anyone with a will to learn can successfully apply it, if they approach it from first principles. This course, Classification Using Tree Based Models, covers a specific class of Machine Learning problems - classification problems and how to solve these problems using Tree based models. First, you'll learn about building and visualizing decision trees as well as recognizing the serious problem of overfitting and its causes. Next, you'll learn about using ensemble learning to overcome overfitting. Finally, you'll explore 2 specific ensemble learning techniques - Random Forests and Gradient boosted trees By the end of this course, you'll be able to recognize opportunities where you can use Tree based models to solve classification problems and measure how well your solution is doing.
Register for this course and see more details by visiting:
OpenCourser.com/course/s8b5og/classification
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What's inside
Syllabus
Course Overview
Building Decision Trees
Predicting Survival on the Titanic Using a Decision Tree
Using Ensembles of Algorithms to Overcome Overfitting
Read more
Syllabus
Course Overview
Building Decision Trees
Predicting Survival on the Titanic Using a Decision Tree
Using Ensembles of Algorithms to Overcome Overfitting
Read more
Predicting Survival on the Titanic Using Random Forests
Predicting Survival on the Titanic Using Gradient Boosted Trees
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines tree based models, which are standard in developing industrial-strength solutions to classification problems
Taught by Swetha Kolalapudi, who is recognized for her work in machine learning and data science
Develops skills in building and visualizing decision trees, overcoming overfitting, and using ensemble learning for higher accuracy
Provides practical experience through hands-on labs and interactive materials
Requires students to have some background in foundational machine learning and basic programming concepts
The course is platform-based and online, which may not be suitable for all learners
Save this course
Save Classification Using Tree Based Models 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 Classification Using Tree Based Models with these
activities:
Review Tree Structures
Show steps
Begin this course with a review of your understanding of tree structures, because they are central to this course's topics.
Browse courses on
Decision Tree
Show steps
-
Start by recalling what you know about tree data structures and their components, such as root, leaf, parent, and child.
-
Practice implementing different tree structures in a programming language of your choice.
Centralized Knowledge Compilation
Show steps
Organize and expand your understanding by compiling a comprehensive set of materials related to this course.
Show steps
-
Create a centralized repository for all course materials, including notes, assignments, quizzes, and readings.
-
Review and expand your notes by adding additional information and insights.
-
Connect different concepts and topics by creating mind maps or diagrams.
Machine Learning Tree Algorithm Tutorials
Show steps
Build on your understanding of tree structures by exploring tutorials on specific tree algorithms used in machine learning.
Show steps
-
Search for tutorials on tree-based machine learning algorithms, such as ID3, C4.5, or CART.
-
Follow the tutorials to implement a tree algorithm and apply it to a classification task.
-
Experiment with different parameters and hyperparameters to optimize the performance of your algorithm.
Three other activities
Expand to see all activities and additional details
Show all six activities
Peer-to-Peer Discussion on Decision Tree Algorithms
Show steps
Exchange ideas and deepen your understanding of decision tree algorithms through discussions with peers.
Browse courses on
Classification Problems
Show steps
-
Join a study group or online forum to connect with other learners.
-
Engage in discussions about the strengths and weaknesses of different decision tree algorithms.
-
Share your experiences and insights on applying decision trees in real-world scenarios.
Supervised Learning Tree-Based Modeling Practice
Show steps
Apply your knowledge of tree algorithms by completing practice exercises and drills in supervised learning using tree-based models.
Browse courses on
Supervised Learning
Show steps
-
Solve practice problems involving binary and multi-class classification using decision trees.
-
Practice identifying overfitting and underfitting issues and apply techniques to mitigate them.
-
Experiment with different tree-based ensemble methods, such as random forests and gradient boosting.
Machine Learning with Tree-Based Models Workshop
Show steps
Expand your practical skills by attending a workshop focused on machine learning using tree-based models.
Show steps
-
Attend an in-person or virtual workshop on tree-based modeling.
-
Follow along with hands-on exercises and demonstrations.
-
Network with experts and professionals in the field.
Review Tree Structures
Show steps
Begin this course with a review of your understanding of tree structures, because they are central to this course's topics.
Browse courses on
Decision Tree
Show steps
Show steps
Show steps
- Start by recalling what you know about tree data structures and their components, such as root, leaf, parent, and child.
- Practice implementing different tree structures in a programming language of your choice.
Centralized Knowledge Compilation
Show steps
Organize and expand your understanding by compiling a comprehensive set of materials related to this course.
Show steps
Show steps
Show steps
- Create a centralized repository for all course materials, including notes, assignments, quizzes, and readings.
- Review and expand your notes by adding additional information and insights.
- Connect different concepts and topics by creating mind maps or diagrams.
Machine Learning Tree Algorithm Tutorials
Show steps
Build on your understanding of tree structures by exploring tutorials on specific tree algorithms used in machine learning.
Show steps
Show steps
Show steps
- Search for tutorials on tree-based machine learning algorithms, such as ID3, C4.5, or CART.
- Follow the tutorials to implement a tree algorithm and apply it to a classification task.
- Experiment with different parameters and hyperparameters to optimize the performance of your algorithm.
Three other activities
Expand to see all activities and additional details
Show all six activities
Peer-to-Peer Discussion on Decision Tree Algorithms
Show steps
Exchange ideas and deepen your understanding of decision tree algorithms through discussions with peers.
Browse courses on
Classification Problems
Show steps
Show steps
Show steps
- Join a study group or online forum to connect with other learners.
- Engage in discussions about the strengths and weaknesses of different decision tree algorithms.
- Share your experiences and insights on applying decision trees in real-world scenarios.
Supervised Learning Tree-Based Modeling Practice
Show steps
Apply your knowledge of tree algorithms by completing practice exercises and drills in supervised learning using tree-based models.
Browse courses on
Supervised Learning
Show steps
Show steps
Show steps
- Solve practice problems involving binary and multi-class classification using decision trees.
- Practice identifying overfitting and underfitting issues and apply techniques to mitigate them.
- Experiment with different tree-based ensemble methods, such as random forests and gradient boosting.
Machine Learning with Tree-Based Models Workshop
Show steps
Expand your practical skills by attending a workshop focused on machine learning using tree-based models.
Show steps
Show steps
Show steps
- Attend an in-person or virtual workshop on tree-based modeling.
- Follow along with hands-on exercises and demonstrations.
- Network with experts and professionals in the field.
Career center
Learners who complete Classification Using Tree Based Models will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve real-world problems. This course provides a comprehensive overview of tree based models, a crucial weapon in a Machine Learning Engineer's arsenal. You'll learn about building and tuning decision trees, utilizing ensemble learning to enhance performance, and leveraging Random Forests and Gradient Boosted Trees for top-notch predictive capabilities. Mastering these techniques will empower you to deliver effective and scalable machine learning solutions.
Data Scientist
Data Scientist
As a Data Scientist, your job involves investigating and analyzing large sets of data to uncover valuable information and insights. Classification problems are ubiquitous in data science. Being well-versed in tree based models would equip you with a powerful toolkit to solve these problems effectively. This course covers the fundamentals of decision trees, ensemble learning, and how to avoid the pitfalls of overfitting. With this knowledge, you'll be able to build and evaluate robust predictive models, gaining a competitive advantage in the data science field.
Data Analyst
Data Analyst
As a Data Analyst, you're responsible for transforming raw data into actionable insights. Tree based models are a fundamental tool in your toolkit, enabling you to identify patterns and make accurate predictions. This course delves into the intricacies of decision trees, ensemble learning, and techniques like Random Forests and Gradient Boosted Trees. By gaining proficiency in these concepts, you'll elevate your data analysis skills, extract meaningful insights from complex data, and drive informed decision-making.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make trading decisions. Tree based models are a valuable tool in this field, allowing for efficient analysis of complex financial instruments and market trends. This course provides a solid foundation in decision trees, ensemble learning, and their application in finance. With this knowledge, you'll be able to construct predictive models, assess risk, and make data-driven investment decisions, giving you an edge in the competitive world of quantitative finance.
Software Engineer
Software Engineer
As a Software Engineer, you may encounter situations where building predictive models is required. This course provides a thorough understanding of tree based models, including decision trees, ensemble learning, and advanced techniques. By gaining proficiency in these concepts, you'll be able to seamlessly integrate machine learning capabilities into your software applications, enhancing their functionality and user experience.
Actuary
Actuary
Actuaries use mathematical and statistical techniques to assess risk and uncertainty. Tree based models offer a powerful approach to modeling complex relationships and predicting future outcomes. This course covers the fundamentals of decision trees, ensemble learning, and their applicability in actuarial science. By mastering these techniques, you'll gain the skills to develop accurate predictive models, evaluate financial risks, and make informed decisions, enhancing your value in the insurance and financial industries.
Business Analyst
Business Analyst
Business Analysts help organizations make informed decisions by analyzing data and identifying opportunities. Tree based models provide a valuable tool for predictive analytics and decision-making. This course introduces the concepts of decision trees, ensemble learning, and how to avoid overfitting. By gaining proficiency in these techniques, you'll be able to extract meaningful insights from data, forecast trends, and make data-driven recommendations, driving business growth and success.
Risk Analyst
Risk Analyst
Risk Analysts assess and manage financial risks faced by organizations. Tree based models offer a powerful approach to quantifying risk and making predictions. This course covers the foundations of decision trees, ensemble learning, and their application in risk management. By gaining expertise in these techniques, you'll be able to develop predictive models, evaluate risk exposures, and make informed decisions, strengthening your ability to safeguard organizations from financial harm.
Financial Analyst
Financial Analyst
Financial Analysts evaluate and make recommendations on investment opportunities. Tree based models are increasingly used for financial forecasting and stock analysis. This course provides a solid foundation in decision trees, ensemble learning, and their application in finance. With this knowledge, you'll be able to construct predictive models, analyze financial data, and make informed investment decisions, enhancing your capabilities in the financial markets.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to derive meaningful insights. Tree based models offer a powerful tool for data analysis and predictive modeling. This course covers the fundamentals of decision trees, ensemble learning, and how to avoid overfitting. By gaining proficiency in these techniques, you'll enhance your ability to analyze complex data, make accurate predictions, and draw informed conclusions, advancing your career as a Statistician.
Data Architect
Data Architect
Data Architects design and manage data systems. Tree based models are becoming increasingly important for data organization and classification. This course provides a solid foundation in decision trees, ensemble learning, and their application in data management. By gaining expertise in these techniques, you'll be able to develop efficient data storage solutions, enhance data accessibility, and improve overall data quality, enabling organizations to make better use of their data assets.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts provide valuable insights to businesses by analyzing data and identifying trends. Tree based models offer a powerful tool for predictive analytics and data-driven decision-making. This course introduces the concepts of decision trees, ensemble learning, and how to avoid overfitting. By gaining proficiency in these techniques, you'll be able to extract meaningful insights from data, forecast trends, and make data-driven recommendations, driving business growth and success.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze market data to identify trends and opportunities for businesses. Tree based models offer a powerful tool for predictive analytics and customer segmentation. This course provides a solid foundation in decision trees, ensemble learning, and their application in marketing. By gaining expertise in these techniques, you'll be able to develop predictive models, analyze customer behavior, and make informed marketing decisions, driving successful marketing campaigns and increasing brand awareness.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to improve business processes. Tree based models offer a powerful tool for optimizing operations and decision-making. This course covers the fundamentals of decision trees, ensemble learning, and their application in operations research. By gaining proficiency in these techniques, you'll be able to develop predictive models, analyze complex systems, and make informed decisions, enhancing operational efficiency and achieving organizational goals.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts use data to improve healthcare delivery and outcomes. Tree based models offer a powerful tool for predictive analytics and disease diagnosis. This course covers the basics of decision trees, ensemble learning, and their application in healthcare. By gaining proficiency in these techniques, you'll be able to develop predictive models, analyze patient data, and make informed decisions, contributing to improved patient care and healthcare system efficiency.
For more career information including salaries, visit:
OpenCourser.com/course/s8b5og/classification
Reading list
We've selected eight 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
Classification Using Tree Based Models.
Save
Seminal work on decision trees and random forests. It must-read for anyone who wants to learn more about these models and how to use them for classification problems.
Provides a comprehensive overview of tree-based models, including decision trees, random forests, and gradient boosting machines. It valuable resource for anyone who wants to learn more about these models and how to use them for classification problems.
Provides a practical introduction to machine learning using Python. It covers a wide range of topics, including tree-based models. It valuable resource for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
Provides a practical introduction to machine learning using R. It covers a wide range of topics, including tree-based models. It valuable resource for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
Provides a collection of recipes for solving common machine learning problems using Python. It covers a wide range of topics, including tree-based models. It valuable resource for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
Provides a practical introduction to machine learning. It covers a wide range of topics, including tree-based models. It valuable resource for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
Classic text on statistical learning, and it covers a wide range of topics, including tree-based models. It valuable resource for anyone who wants to learn more about the theoretical foundations of machine learning.
Provides a gentle introduction to machine learning. It covers a wide range of topics, including tree-based models. It valuable resource for anyone who wants to learn more about machine learning and how to use it to solve real-world problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/s8b5og/classification
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Time
117 hours
Level
Introductory
Via
Pluralsight
Instructor
Swetha Kolalapudi
Language
English
Good to know
Examines tree based models, which are standard in developing industrial-strength solutions to classification problems
Taught by Swetha Kolalapudi, who is recognized for her work in machine learning and data science
Develops skills in building and visualizing decision trees, overcoming overfitting, and using ensemble learning for higher accuracy
Provides practical experience through hands-on labs and interactive materials
Requires students to have some background in foundational machine learning and basic programming concepts
The course is platform-based and online, which may not be suitable for all learners
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