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Mohamed Jendoubi
In this 1-hour long project-based course, you will create an end-to-end Regression model using PyCaret a low-code Python open-source Machine Learning library.
Read more
In this 1-hour long project-based course, you will create an end-to-end Regression model using PyCaret a low-code Python open-source Machine Learning library.
Read more
In this 1-hour long project-based course, you will create an end-to-end Regression model using PyCaret a low-code Python open-source Machine Learning library.
The goal is to build a model that can accurately predict the strength of concrete based on several fatures.
You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for regression.
Here are the main steps you will go through: frame the problem, get and prepare the data, discover and visualize the data, create the transformation pipeline, build, evaluate, interpret and deploy the model.
This guided project is for seasoned Data Scientists who want to build a accelerate the efficiency in building POC and experiments by using a low-code library. It is also for Citizen data Scientists (professionals working with data) by using the low-code library PyCaret to add machine learning models to the analytics toolkit
In order to be successful in this project, you should be familiar with Python and the basic concepts on Machine Learning
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/nqca50/build
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What's inside
Syllabus
Project Overview
By the end of this project, you will create an end-to-end regression model using PyCaret a low-code Python open-source Machine Learning library. You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for Regression.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for advanced students who want to advance their practice in machine learning
Taught by recognized instructors Mohamed Jendoubi
Teaches critical knowledge of regression modeling
Accelerates the building of POCs and experiments using low-code libraries
Requires familiarity with python and basic machine learning concepts
Save this course
Save Build a Regression Model using PyCaret 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 Build a Regression Model using PyCaret with these
activities:
Create a Study Guide
Show steps
Provides a centralized resource for reviewing and consolidating course materials, enhancing retention and recall.
Show steps
-
Review course notes, assignments, and readings.
-
Identify key concepts and summarize them in a concise format.
-
Organize the study guide into logical sections.
Connect with Experts in Regression Analysis
Show steps
Provides access to guidance and support from experienced professionals, fostering knowledge acquisition and career development.
Show steps
-
Identify potential mentors through professional networking events or online platforms.
-
Reach out to potential mentors and request their guidance.
-
Schedule regular meetings to discuss regression analysis and career goals.
Practice Guided Tutorial Code
Show steps
Reinforces the concepts and methods taught in the course by providing opportunities to apply them in a hands-on setting.
Show steps
-
Go through the guided tutorial code.
-
Run the code to see the output.
-
Modify the code to test different scenarios.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss Regression Modeling Techniques
Show steps
Provides an opportunity to discuss and clarify complex concepts with peers, fostering a deeper understanding of the material.
Show steps
-
Join a peer study group or online forum.
-
Initiate discussions on specific regression modeling techniques.
-
Share insights and experiences with peers.
Create a Presentation on Regression Analysis
Show steps
Provides an opportunity to synthesize and present the key concepts of regression analysis, reinforcing understanding and enhancing communication skills.
Show steps
-
Identify the key concepts of regression analysis to be covered.
-
Gather relevant information and examples.
-
Design and create slides for the presentation.
-
Practice delivering the presentation.
Contribute to PyCaret's Documentation
Show steps
Provides an opportunity to deepen understanding of PyCaret's capabilities and contribute to the community, fostering a sense of ownership and lifelong learning.
Show steps
-
Identify areas in PyCaret's documentation that need improvement.
-
Write or edit documentation to enhance clarity and comprehensiveness.
-
Submit a pull request to the PyCaret repository.
Build a Regression Model Using PyCaret
Show steps
Applies the concepts and skills learned in the course to a practical project, reinforcing understanding and demonstrating proficiency.
Show steps
-
Gather and prepare data for the regression task.
-
Use PyCaret to create a regression model.
-
Evaluate the performance of the model.
-
Write a report summarizing the project.
Participate in a Machine Learning Hackathon
Show steps
Provides an immersive and challenging environment to apply regression modeling skills, fostering innovation and problem-solving abilities.
Show steps
-
Find a relevant machine learning hackathon.
-
Form a team or participate individually.
-
Develop a solution to the hackathon challenge.
-
Present the solution and compete with other teams.
Create a Study Guide
Show steps
Provides a centralized resource for reviewing and consolidating course materials, enhancing retention and recall.
Show steps
Show steps
Show steps
- Review course notes, assignments, and readings.
- Identify key concepts and summarize them in a concise format.
- Organize the study guide into logical sections.
Connect with Experts in Regression Analysis
Show steps
Provides access to guidance and support from experienced professionals, fostering knowledge acquisition and career development.
Show steps
Show steps
Show steps
- Identify potential mentors through professional networking events or online platforms.
- Reach out to potential mentors and request their guidance.
- Schedule regular meetings to discuss regression analysis and career goals.
Practice Guided Tutorial Code
Show steps
Reinforces the concepts and methods taught in the course by providing opportunities to apply them in a hands-on setting.
Show steps
Show steps
Show steps
- Go through the guided tutorial code.
- Run the code to see the output.
- Modify the code to test different scenarios.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Discuss Regression Modeling Techniques
Show steps
Provides an opportunity to discuss and clarify complex concepts with peers, fostering a deeper understanding of the material.
Show steps
Show steps
Show steps
- Join a peer study group or online forum.
- Initiate discussions on specific regression modeling techniques.
- Share insights and experiences with peers.
Create a Presentation on Regression Analysis
Show steps
Provides an opportunity to synthesize and present the key concepts of regression analysis, reinforcing understanding and enhancing communication skills.
Show steps
Show steps
Show steps
- Identify the key concepts of regression analysis to be covered.
- Gather relevant information and examples.
- Design and create slides for the presentation.
- Practice delivering the presentation.
Contribute to PyCaret's Documentation
Show steps
Provides an opportunity to deepen understanding of PyCaret's capabilities and contribute to the community, fostering a sense of ownership and lifelong learning.
Show steps
Show steps
Show steps
- Identify areas in PyCaret's documentation that need improvement.
- Write or edit documentation to enhance clarity and comprehensiveness.
- Submit a pull request to the PyCaret repository.
Build a Regression Model Using PyCaret
Show steps
Applies the concepts and skills learned in the course to a practical project, reinforcing understanding and demonstrating proficiency.
Show steps
Show steps
Show steps
- Gather and prepare data for the regression task.
- Use PyCaret to create a regression model.
- Evaluate the performance of the model.
- Write a report summarizing the project.
Participate in a Machine Learning Hackathon
Show steps
Provides an immersive and challenging environment to apply regression modeling skills, fostering innovation and problem-solving abilities.
Show steps
Show steps
Show steps
- Find a relevant machine learning hackathon.
- Form a team or participate individually.
- Develop a solution to the hackathon challenge.
- Present the solution and compete with other teams.
Career center
Learners who complete Build a Regression Model using PyCaret will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for developing and applying machine learning models to solve business problems. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses solve complex problems and make better decisions using data.
Machine Learning Researcher
Machine Learning Researcher
Machine Learning Researchers are responsible for developing new machine learning algorithms and techniques. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help advance the field of machine learning and develop new solutions to real-world problems.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts are responsible for developing and applying mathematical and statistical models to improve business operations. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses optimize their operations, reduce costs, and improve efficiency.
Statistician
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses make better decisions based on data.
Fraud Analyst
Fraud Analyst
Fraud Analysts are responsible for detecting and preventing fraud. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses detect and prevent fraud more effectively.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, building, and deploying machine learning models. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses develop and deploy machine learning solutions that can automate tasks, improve decision-making, and create new products and services.
Risk Analyst
Risk Analyst
Risk Analysts are responsible for identifying, assessing, and mitigating risks. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses identify and mitigate risks more effectively.
Data Engineer
Data Engineer
Data Engineers are responsible for designing, building, and maintaining data pipelines. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses build and maintain data pipelines that are more efficient, reliable, and scalable.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts are responsible for developing and applying mathematical and statistical models to financial data. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help financial institutions make better decisions about investments, risk management, and trading.
Actuary
Actuary
Actuaries are responsible for assessing and managing financial risks. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help insurance companies and other financial institutions assess and manage risks more effectively.
Business Analyst
Business Analyst
Business Analysts are responsible for understanding business needs and translating them into technical requirements. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses develop and implement machine learning solutions that align with their business goals.
Data Analyst
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to help businesses make informed decisions. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses gain insights from their data and make better decisions.
Software Engineer
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses develop software applications that are more efficient, effective, and user-friendly.
Consultant
Consultant
Consultants are responsible for providing advice and guidance to businesses on a variety of topics. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help businesses make better decisions about how to use machine learning to solve business problems.
Professor
Professor
Professors are responsible for teaching and conducting research in their field of expertise. This course can help you develop the skills needed to succeed in this role by teaching you how to use Python and PyCaret to build and evaluate machine learning models. With these skills, you can help students learn about machine learning and develop the skills needed to succeed in the field.
For more career information including salaries, visit:
OpenCourser.com/course/nqca50/build
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
Build a Regression Model using PyCaret.
Provides a practical guide to machine learning using Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides detailed explanations of how to use popular machine learning libraries such as Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive overview of statistical learning methods that are designed to handle high-dimensional data. It covers a wide range of topics, including sparse linear regression, sparse logistic regression, and sparse support vector machines.
Provides a comprehensive overview of statistical learning methods. It covers a wide range of topics, including linear regression, logistic regression, and support vector machines. It also provides detailed explanations of how to use popular statistical learning libraries such as R and Python.
Provides a comprehensive overview of machine learning from a probabilistic perspective. It covers a wide range of topics, including Bayesian inference, graphical models, and reinforcement learning.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It also provides detailed explanations of how to use popular machine learning libraries such as Python and R.
Save
Provides a comprehensive overview of machine learning from an algorithmic perspective. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It also provides detailed explanations of how to use popular machine learning libraries such as Python and R.
Save
Provides a comprehensive overview of machine learning for the web. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning. It also provides detailed explanations of how to use popular machine learning libraries such as Python and R.
Provides a practical guide to data science using Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides detailed explanations of how to use popular data science libraries such as NumPy, Pandas, and Scikit-Learn.
Provides a comprehensive overview of machine learning concepts and techniques using R. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides detailed explanations of how to use popular machine learning libraries such as caret.
Provides a practical guide to machine learning for hackers. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides detailed explanations of how to use popular machine learning libraries such as Python and R.
Provides a comprehensive overview of deep learning concepts and techniques. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks. It also provides detailed explanations of how to use popular deep learning libraries such as Keras.
Provides a comprehensive overview of machine learning concepts and techniques using Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides detailed explanations of how to use popular machine learning libraries such as Scikit-Learn.
Provides a gentle introduction to machine learning using Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides detailed explanations of how to use popular machine learning libraries such as Python.
Provides a gentle introduction to machine learning using Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation. It also provides detailed explanations of how to use popular machine learning libraries such as Scikit-Learn.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/nqca50/build
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Mohamed Jendoubi
Language
English
Good to know
Suitable for advanced students who want to advance their practice in machine learning
Taught by recognized instructors Mohamed Jendoubi
Teaches critical knowledge of regression modeling
Accelerates the building of POCs and experiments using low-code libraries
Requires familiarity with python and basic machine learning concepts
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