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Ryan Ahmed
Hello everyone and welcome to this new hands-on project on Scikit-Learn for solving machine learning regression problems. In this project, we will learn how to build and train regression models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means.
Read more
Hello everyone and welcome to this new hands-on project on Scikit-Learn for solving machine learning regression problems. In this project, we will learn how to build and train regression models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means.
Read more
Hello everyone and welcome to this new hands-on project on Scikit-Learn for solving machine learning regression problems. In this project, we will learn how to build and train regression models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means.
This project is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your next job interview.
Register for this course and see more details by visiting:
OpenCourser.com/course/hg53pr/scikit
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What's inside
Syllabus
Project Overview
Hello everyone and welcome to this new hands-on project on Scikit-Learn for solving machine learning regression problems. In this project, we will learn how to build and train regression models using Scikit-Learn library. Scikit-learn is a free machine learning library developed for python. Scikit-learn offers several algorithms for classification, regression, and clustering. Several famous machine learning models are included such as support vector machines, random forests, gradient boosting, and k-means. This project is practical and directly applicable to many industries. You can add this project to your portfolio of projects which is essential for your next job interview.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches essential skills like linear regression and prediction modeling in Python
Can serve as a refresher for those who already have foundational knowledge in ML
Provides practical and industry-applicable knowledge to enhance your resume
May require additional prerequisite knowledge in Python and ML
Utilizes the widely-used Scikit-learn library
Save this course
Save Scikit-Learn to Solve Regression Machine Learning Problems 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 Scikit-Learn to Solve Regression Machine Learning Problems with these
activities:
Follow online tutorials on regression models
Show steps
Online tutorials provide additional support and guidance in understanding regression models.
Browse courses on
Regression
Show steps
-
Search for reputable online tutorials on regression models.
-
Follow the tutorials step-by-step and complete any exercises or assignments.
-
Take notes and review the material regularly.
-
Discuss the concepts with classmates or online forums.
Complete practice exercises
Show steps
Practice exercises help reinforce understanding of regression models and machine learning concepts.
Browse courses on
Regression
Show steps
-
Work through the practice exercises provided in the course materials.
-
Use online resources to find additional practice exercises.
-
Form a study group with classmates to work through practice exercises together.
Attend a machine learning conference or meetup
Show steps
Attending industry events allows for networking and learning from experts in the field.
Browse courses on
Regression
Show steps
-
Research upcoming machine learning conferences or meetups.
-
Register for the event and prepare to actively participate.
-
Attend the event and engage with speakers and attendees.
-
Follow up with new connections and explore potential collaborations.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a presentation on regression models
Show steps
Creating a presentation helps solidify understanding of regression models and their applications.
Browse courses on
Regression
Show steps
-
Choose a specific regression model to focus on.
-
Research the model and its applications.
-
Develop a presentation outline.
-
Create slides and visuals to support your presentation.
-
Practice delivering your presentation.
Develop a regression model for a real-world problem
Show steps
Hands-on experience in applying regression models to solve real-world problems.
Browse courses on
Regression
Show steps
-
Identify a real-world problem that can be addressed using a regression model.
-
Gather and prepare the necessary data.
-
Choose a suitable regression model and train it on the data.
-
Evaluate the performance of the model and make adjustments as needed.
-
Deploy the model and monitor its performance.
Contribute to an open-source regression project
Show steps
Contributing to an open-source project provides hands-on experience with regression models and open-source development.
Browse courses on
Regression
Show steps
-
Find an open-source regression project to contribute to.
-
Review the project documentation and codebase.
-
Identify an area where you can contribute.
-
Make a pull request with your contributions.
-
Review feedback and make necessary changes.
Follow online tutorials on regression models
Show steps
Online tutorials provide additional support and guidance in understanding regression models.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Search for reputable online tutorials on regression models.
- Follow the tutorials step-by-step and complete any exercises or assignments.
- Take notes and review the material regularly.
- Discuss the concepts with classmates or online forums.
Complete practice exercises
Show steps
Practice exercises help reinforce understanding of regression models and machine learning concepts.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Work through the practice exercises provided in the course materials.
- Use online resources to find additional practice exercises.
- Form a study group with classmates to work through practice exercises together.
Attend a machine learning conference or meetup
Show steps
Attending industry events allows for networking and learning from experts in the field.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Research upcoming machine learning conferences or meetups.
- Register for the event and prepare to actively participate.
- Attend the event and engage with speakers and attendees.
- Follow up with new connections and explore potential collaborations.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a presentation on regression models
Show steps
Creating a presentation helps solidify understanding of regression models and their applications.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Choose a specific regression model to focus on.
- Research the model and its applications.
- Develop a presentation outline.
- Create slides and visuals to support your presentation.
- Practice delivering your presentation.
Develop a regression model for a real-world problem
Show steps
Hands-on experience in applying regression models to solve real-world problems.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Identify a real-world problem that can be addressed using a regression model.
- Gather and prepare the necessary data.
- Choose a suitable regression model and train it on the data.
- Evaluate the performance of the model and make adjustments as needed.
- Deploy the model and monitor its performance.
Contribute to an open-source regression project
Show steps
Contributing to an open-source project provides hands-on experience with regression models and open-source development.
Browse courses on
Regression
Show steps
Show steps
Show steps
- Find an open-source regression project to contribute to.
- Review the project documentation and codebase.
- Identify an area where you can contribute.
- Make a pull request with your contributions.
- Review feedback and make necessary changes.
Career center
Learners who complete Scikit-Learn to Solve Regression Machine Learning Problems will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. This course would be very useful for aspiring Machine Learning Engineers as it provides hands-on experience with Scikit-Learn, a widely-used library for machine learning tasks. The course covers various regression algorithms, which are essential for building predictive models.
Data Scientist
Data Scientist
Data Scientists use machine learning algorithms to extract insights from data. This course is a great fit for aspiring Data Scientists as it can help them build a foundation in regression modeling using Scikit-Learn. Regression is a fundamental technique used by Data Scientists to predict continuous outcomes, such as sales or revenue.
Statistician
Statistician
Statisticians use statistical methods to analyze data and draw conclusions. This course would be useful for aspiring Statisticians as it provides hands-on experience with regression modeling using Scikit-Learn. Regression is a fundamental statistical technique used to predict continuous outcomes based on historical data.
Epidemiologist
Epidemiologist
Epidemiologists use statistical methods to study the distribution and patterns of health-related events. This course would be useful for aspiring Epidemiologists as it provides hands-on experience with regression modeling using Scikit-Learn. Regression is a fundamental statistical technique used in epidemiology to predict the occurrence of health-related events based on historical data.
Biostatistician
Biostatistician
Biostatisticians use statistical methods to analyze data in the field of biology. This course would be useful for aspiring Biostatisticians as it provides hands-on experience with regression modeling using Scikit-Learn. Regression is a fundamental statistical technique used in biology to predict outcomes based on historical data.
Econometrician
Econometrician
Econometricians use statistical methods to analyze economic data. This course would be useful for aspiring Econometricians as it provides hands-on experience with regression modeling using Scikit-Learn. Regression is a fundamental statistical technique used in econometrics to predict economic outcomes based on historical data.
Data Analyst
Data Analyst
Data Analysts use data to identify trends and patterns. This course may be useful for aspiring Data Analysts as it provides a foundation in regression modeling. Regression is a technique used by Data Analysts to predict outcomes and make data-driven decisions.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data. This course can be useful for aspiring Quantitative Analysts as it provides a good foundation in regression modeling. Regression is a powerful technique used in finance to predict future trends and make informed investment decisions.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve business problems. This course would be useful for aspiring Operations Research Analysts as it provides hands-on experience with regression modeling using Scikit-Learn. Regression is a technique used by Operations Research Analysts to predict outcomes and make data-driven decisions.
Data Engineer
Data Engineer
A Data Engineer is responsible for building and maintaining data pipelines, as well as designing and implementing data storage systems. This course may be useful for aspiring Data Engineers as it provides a solid foundation in Scikit-Learn, a popular machine learning library used for regression tasks. Understanding how regression works is essential for data engineers to build accurate models that can predict outcomes based on historical data.
Business Analyst
Business Analyst
Business Analysts use data to identify and solve business problems. This course may be useful for aspiring Business Analysts as it provides a foundation in regression modeling. Regression is a technique used by Business Analysts to predict outcomes and make data-driven decisions.
Risk Analyst
Risk Analyst
Risk Analysts use data to assess and manage risk. This course may be useful for aspiring Risk Analysts as it provides a foundation in regression modeling. Regression is a technique used by Risk Analysts to predict future outcomes and make informed decisions.
Market Researcher
Market Researcher
Market Researchers use data to understand consumer behavior and market trends. This course may be useful for aspiring Market Researchers as it provides a foundation in regression modeling. Regression is a technique used by Market Researchers to predict consumer behavior and make informed marketing decisions.
Financial Analyst
Financial Analyst
Financial Analysts use financial data to make investment recommendations. This course can be useful for aspiring Financial Analysts as it provides a good foundation in regression modeling. Regression is a commonly used technique in finance to predict future financial trends.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and testing software applications. This course may be useful for Software Engineers who want to build machine learning functionality into their applications. Scikit-Learn is a popular library for machine learning tasks, and this course can help Software Engineers learn how to use it effectively.
For more career information including salaries, visit:
OpenCourser.com/course/hg53pr/scikit
Reading list
We've selected nine 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
Scikit-Learn to Solve Regression Machine Learning Problems.
Provides a comprehensive overview of statistical learning, including regression models. It good reference for learners who want to learn more about the theory behind regression models.
Save
Provides a comprehensive overview of deep learning, including regression models. It good reference for learners who want to learn more about the theory behind regression models.
Provides a more advanced treatment of statistical learning, including regression models. It good reference for learners who want to learn more about the theory behind regression models.
Provides a practical guide to building and training machine learning models using Scikit-Learn, Keras, and TensorFlow. It good reference for learners who want to learn more about regression models.
Provides a practical guide to building and training machine learning models using PHP. It good reference for learners who want to learn more about regression models.
Provides a practical guide to building and training machine learning models using Python. It good reference for learners who want to learn more about regression models.
Provides a practical guide to building and training machine learning models using Python. It good reference for learners who want to learn more about regression models.
Provides a practical guide to building and training machine learning models using R. It good reference for learners who want to learn more about regression models.
Provides a gentle introduction to machine learning, including regression models. It good starting point for learners who are new to machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/hg53pr/scikit
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Ryan Ahmed
Language
English
Good to know
Teaches essential skills like linear regression and prediction modeling in Python
Can serve as a refresher for those who already have foundational knowledge in ML
Provides practical and industry-applicable knowledge to enhance your resume
May require additional prerequisite knowledge in Python and ML
Utilizes the widely-used Scikit-learn library
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