We may earn an affiliate commission when you visit our partners.
Ashish Dikshit
In this 1-hour long project-based course, you will learn how to perform regression tasks using decision tree & some PCA fundamental coding.
you will get expertise in acing following tasks-
Predicting two decision tree regression model
Drawing Decision tree for regression
Regularize a decision tree regressor
Setting up the environment for dimensional reduction
Coding for Projection methods in Dimensionality reduction
Coding for PCA using SVD decomposition and SCIKIT learn
Register for this course and see more details by visiting:
OpenCourser.com/course/95bk8c/performing
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches critical real-world skills used in industry for regression tasks
Taught by Ashish Dikshit, an instructor recognized for their work in decision trees
Develops core skills for data science and regression analysis
Covers both fundamental and advanced concepts of decision tree regression
Provides a comprehensive introduction to the Python libraries needed for decision tree regression
Requires no prior knowledge of decision trees or regression, making it accessible to beginners
Save this course
Save Performing regression tasks using decision tree & PCA basics to your list so you can find it easily later:
Save
Save
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 Performing regression tasks using decision tree & PCA basics with these
activities:
Find a Mentor to Guide Your Learning in Decision Tree Regression
Show steps
Finding a mentor will provide you with personalized guidance and support throughout your learning journey.
Show steps
-
Identify the skills and knowledge you want to develop
-
Research potential mentors who have expertise in those areas
-
Reach out to mentors and introduce yourself
-
Set up regular meetings to discuss your progress and get feedback
Review Fundamentals of Machine Learning
Show steps
Reviewing this book will provide you with the background knowledge necessary to fully understand the course material.
View
Entropy and Diversity: The Axiomatic Approach
on Amazon
Show steps
-
Read the first three chapters of the book
-
Complete the exercises at the end of each chapter
-
Summarize the key concepts discussed in each chapter
-
Discuss the concepts with a classmate or colleague
Participate in a Study Group for Decision Tree Regression
Show steps
Participating in a study group will help you learn from others, share your knowledge, and stay motivated.
Show steps
-
Find a study group to join or start your own
-
Meet regularly with your group to discuss the course material
-
Work together on practice problems and projects
-
Provide feedback and support to each other
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Decision Tree Regression Practice Problems
Show steps
Solving decision tree regression practice problems will help you improve your coding skills and solidify your understanding of the concepts.
Show steps
-
Find a set of decision tree regression practice problems
-
Solve the problems using a programming language like Python or R
-
Check your solutions against the provided answer key
-
Review your mistakes and identify areas where you need improvement
Follow a Guided Tutorial on Regularization in Decision Tree Regression
Show steps
Following a guided tutorial on regularization in decision tree regression will help you gain a deeper understanding of the topic and improve your coding skills.
Browse courses on
Regularization
Show steps
-
Find a guided tutorial on regularization in decision tree regression
-
Follow the steps in the tutorial to build a regularized decision tree regression model
-
Evaluate the performance of your model
-
Discuss the results of your experiment with others
Build a Simple Decision Tree Regression Model
Show steps
Building a simple decision tree regression model will help you apply the concepts learned in the course and gain practical experience.
Show steps
-
Choose a dataset to use for your model
-
Preprocess the data and prepare it for modeling
-
Create a decision tree regression model using Scikit-learn
-
Evaluate the performance of your model
-
Write a report summarizing your findings
Create a Tutorial on PCA for Dimensionality Reduction
Show steps
Creating a tutorial on PCA for dimensionality reduction will help you deepen your understanding of the topic and improve your communication skills.
Browse courses on
Principal Component Analysis
Show steps
-
Research the topic of PCA and dimensionality reduction
-
Write a detailed outline for your tutorial
-
Create a presentation or video tutorial
-
Share your tutorial with others and get feedback
Develop a Data Visualization Dashboard for Regression Analysis
Show steps
Developing a data visualization dashboard for regression analysis will help you showcase your skills in data analysis and presentation.
Browse courses on
Data Visualization
Show steps
-
Gather the data you need for your dashboard
-
Clean and prepare the data
-
Build a regression model and evaluate its performance
-
Create a data visualization dashboard using a tool like Tableau or Power BI
-
Present your dashboard to others and get feedback
-
Update your dashboard based on feedback
Find a Mentor to Guide Your Learning in Decision Tree Regression
Show steps
Finding a mentor will provide you with personalized guidance and support throughout your learning journey.
Show steps
Show steps
Show steps
- Identify the skills and knowledge you want to develop
- Research potential mentors who have expertise in those areas
- Reach out to mentors and introduce yourself
- Set up regular meetings to discuss your progress and get feedback
Review Fundamentals of Machine Learning
Show steps
Reviewing this book will provide you with the background knowledge necessary to fully understand the course material.
View
Entropy and Diversity: The Axiomatic Approach
on Amazon
Show steps
Show steps
Show steps
- Read the first three chapters of the book
- Complete the exercises at the end of each chapter
- Summarize the key concepts discussed in each chapter
- Discuss the concepts with a classmate or colleague
Participate in a Study Group for Decision Tree Regression
Show steps
Participating in a study group will help you learn from others, share your knowledge, and stay motivated.
Show steps
Show steps
Show steps
- Find a study group to join or start your own
- Meet regularly with your group to discuss the course material
- Work together on practice problems and projects
- Provide feedback and support to each other
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Decision Tree Regression Practice Problems
Show steps
Solving decision tree regression practice problems will help you improve your coding skills and solidify your understanding of the concepts.
Show steps
Show steps
Show steps
- Find a set of decision tree regression practice problems
- Solve the problems using a programming language like Python or R
- Check your solutions against the provided answer key
- Review your mistakes and identify areas where you need improvement
Follow a Guided Tutorial on Regularization in Decision Tree Regression
Show steps
Following a guided tutorial on regularization in decision tree regression will help you gain a deeper understanding of the topic and improve your coding skills.
Browse courses on
Regularization
Show steps
Show steps
Show steps
- Find a guided tutorial on regularization in decision tree regression
- Follow the steps in the tutorial to build a regularized decision tree regression model
- Evaluate the performance of your model
- Discuss the results of your experiment with others
Build a Simple Decision Tree Regression Model
Show steps
Building a simple decision tree regression model will help you apply the concepts learned in the course and gain practical experience.
Show steps
Show steps
Show steps
- Choose a dataset to use for your model
- Preprocess the data and prepare it for modeling
- Create a decision tree regression model using Scikit-learn
- Evaluate the performance of your model
- Write a report summarizing your findings
Create a Tutorial on PCA for Dimensionality Reduction
Show steps
Creating a tutorial on PCA for dimensionality reduction will help you deepen your understanding of the topic and improve your communication skills.
Browse courses on
Principal Component Analysis
Show steps
Show steps
Show steps
- Research the topic of PCA and dimensionality reduction
- Write a detailed outline for your tutorial
- Create a presentation or video tutorial
- Share your tutorial with others and get feedback
Develop a Data Visualization Dashboard for Regression Analysis
Show steps
Developing a data visualization dashboard for regression analysis will help you showcase your skills in data analysis and presentation.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Gather the data you need for your dashboard
- Clean and prepare the data
- Build a regression model and evaluate its performance
- Create a data visualization dashboard using a tool like Tableau or Power BI
- Present your dashboard to others and get feedback
- Update your dashboard based on feedback
Career center
Learners who complete Performing regression tasks using decision tree & PCA basics will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists apply decision trees and PCA for data modeling and feature engineering tasks. This course offers a comprehensive introduction to these concepts, empowering you to build robust data-driven solutions and uncover hidden patterns in complex datasets.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers leverage decision trees and PCA for building predictive models and reducing data dimensionality. By understanding these techniques thoroughly through this course, you can sharpen your skills in designing and implementing ML algorithms for various industry applications.
Financial Analyst
Financial Analyst
Financial Analysts leverage decision trees and PCA for financial modeling, forecasting, and risk assessment. Enroll in this course to strengthen your understanding of these techniques and gain a competitive edge in the financial industry by making data-driven investment decisions.
Data Engineer
Data Engineer
Data Engineers leverage decision trees and PCA for data preprocessing and feature extraction. This course offers practical guidance on these techniques, enabling you to build efficient and scalable data pipelines for machine learning and data analysis projects.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts employ decision trees and PCA for risk assessment and portfolio optimization. This course provides a solid foundation in these techniques, empowering you to develop advanced quantitative models and make data-driven financial decisions.
Actuary
Actuary
Actuaries utilize decision trees and PCA for risk assessment, pricing, and financial modeling. By taking this course, you can gain a deeper understanding of these techniques and enhance your skills in developing actuarial models for insurance and risk management.
Biostatistician
Biostatistician
Biostatisticians apply decision trees and PCA for analyzing medical data, disease risk assessment, and developing predictive models. By enrolling in this course, you can expand your expertise in statistical methods for healthcare and biomedical research.
Data Analyst
Data Analyst
For Data Analysts, working knowledge of regression analysis and dimensionality reduction techniques are essential to extracting actionable insights from data. Enroll in this course to strengthen your foundation in decision trees and PCA, enabling you to develop effective machine learning pipelines for data analysis.
Risk Manager
Risk Manager
Risk Managers use decision trees and PCA to identify and mitigate risks in various industries. This course provides a thorough introduction to these techniques, enabling you to develop effective risk management strategies and make informed decisions.
Epidemiologist
Epidemiologist
Epidemiologists leverage decision trees and PCA for studying the distribution and determinants of health events. This course provides a solid foundation in these techniques, empowering you to conduct epidemiological research, identify risk factors, and develop public health interventions.
Market Researcher
Market Researcher
Market Researchers use decision trees and PCA for market segmentation, customer profiling, and forecasting trends. This course provides a comprehensive overview of these techniques, enabling you to gain insights into customer behavior, preferences, and market dynamics.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts apply decision trees and PCA for optimization problems and decision-making. This course offers practical knowledge in these techniques, empowering you to develop data-driven solutions for improving operational efficiency and productivity.
Statistician
Statistician
For Statisticians, decision trees and PCA provide valuable tools for predictive modeling and data visualization. By enrolling in this course, you can expand your statistical toolkit, enabling you to analyze and interpret data more effectively for informed decision-making.
Software Engineer
Software Engineer
Software Engineers with expertise in decision trees and PCA are in high demand for developing machine learning applications and data-driven systems. This course provides a solid foundation in these techniques, empowering you to design and implement ML-powered solutions.
Business Analyst
Business Analyst
Business Analysts utilize decision trees and PCA to identify trends, patterns, and insights from business data. By taking this course, you can enhance your analytical skills and make data-driven recommendations to improve business performance.
For more career information including salaries, visit:
OpenCourser.com/course/95bk8c/performing
Reading list
We've selected ten 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
Performing regression tasks using decision tree & PCA basics.
Classic in the field of statistical learning. It provides a deep dive into the theory and practice of decision trees and PCA.
Classic in the field of decision trees. It provides a deep dive into the theory and practice of decision trees.
Provides a comprehensive overview of machine learning using Python. It includes coverage of decision trees and PCA.
Provides a comprehensive overview of machine learning using R. It includes coverage of decision trees and PCA.
Provides a comprehensive overview of statistical learning methods, including decision trees and PCA. It valuable resource for both beginners and experienced practitioners.
Save
Provides a comprehensive overview of machine learning, including decision trees and PCA. It valuable resource for both beginners and experienced practitioners.
Provides a comprehensive overview of machine learning using Python. It includes coverage of decision trees and PCA.
Save
Provides a practical guide to data mining, including decision trees and PCA. It valuable resource for both beginners and experienced practitioners.
Provides a practical guide to machine learning using Python. It includes coverage of decision trees and PCA.
Save
Provides a comprehensive overview of data analysis and machine learning fundamentals. It includes coverage of decision trees and PCA.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/95bk8c/performing
Share
Similar courses
Here are nine courses similar to
Performing regression tasks using decision tree & PCA basics.
Data Analysis with Python Project
OpenCourser.com/course/b3nagq/data
Data Analysis with Python Project
Most relevant
Classification Analysis
OpenCourser.com/course/y6nuor/classification
Classification Analysis
Most relevant
Clustering Analysis
OpenCourser.com/course/3pk1ai/clustering
Clustering Analysis
Most relevant
Advanced Learning Algorithms
OpenCourser.com/course/g1jq38/advanced
Advanced Learning Algorithms
Understanding and Applying Factor Analysis and PCA
OpenCourser.com/course/ynywoi/understanding
Understanding and Applying Factor Analysis and PCA
Machine Learning A-Z: AI, Python & R + ChatGPT Prize [2024]
OpenCourser.com/course/lt28bf/machine
Machine Learning A-Z: AI, Python & R + ChatGPT Prize...
XG-Boost 101: Used Cars Price Prediction
OpenCourser.com/course/l72xlq/xg
XG-Boost 101: Used Cars Price Prediction
Data Preprocessing for Data Science
OpenCourser.com/course/qu8q3u/data
Data Preprocessing for Data Science
Master Regression and Feedforward Networks [2024]
OpenCourser.com/course/jadffl/master
Master Regression and Feedforward Networks [2024]
Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Ashish Dikshit
Good to know
Teaches critical real-world skills used in industry for regression tasks
Taught by Ashish Dikshit, an instructor recognized for their work in decision trees
Develops core skills for data science and regression analysis
Covers both fundamental and advanced concepts of decision tree regression
Provides a comprehensive introduction to the Python libraries needed for decision tree regression
Requires no prior knowledge of decision trees or regression, making it accessible to beginners
Our mission
OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.
Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.
Find this site helpful? Tell a friend about us.
Affiliate disclosure
We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.
Your purchases help us maintain our catalog and keep our servers humming without ads.
Thank you for supporting OpenCourser.
© 2016 - 2024 OpenCourser