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Muhammad Saad uddin
In this 2 hour 10 mins long project-based course, you will learn how to set up PyCaret Environment and become familiar with the variety of data preparing tasks done during setup, be able to create, see and compare performance of several models, learn how to tune your model without doing an exhaustive search, create impressive visuals of models, feature importance and much more.
Read more
In this 2 hour 10 mins long project-based course, you will learn how to set up PyCaret Environment and become familiar with the variety of data preparing tasks done during setup, be able to create, see and compare performance of several models, learn how to tune your model without doing an exhaustive search, create impressive visuals of models, feature importance and much more.
Read more
In this 2 hour 10 mins long project-based course, you will learn how to set up PyCaret Environment and become familiar with the variety of data preparing tasks done during setup, be able to create, see and compare performance of several models, learn how to tune your model without doing an exhaustive search, create impressive visuals of models, feature importance and much more.
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/70pho9/pycaret
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What's inside
Syllabus
Project Overview: Working with PyCaret
This guided project is all about learning the fundamentals of an essential low code machine learning library PyCaret. By the end of this project you will acquire fundamental skills of installing & setting up the PyCaret environment. Create, compare & visualize models and how to use multiple models with Stacking, Blending and Ensemble. All this with just a few lines of code.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Offers a gentle introduction to the low-code Python machine learning library known as PyCaret
Suitable for beginners seeking to establish a foundation in low-code machine learning with PyCaret
Provides a comprehensive overview of PyCaret's capabilities, including model creation, comparison, and tuning
Empowers learners to leverage advanced techniques such as stacking, blending, and ensemble modeling with ease
Designed for learners based in North America, with plans to expand accessibility to other regions
Save this course
Save PyCaret: Anatomy of Classification to your list so you can find it easily later:
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Reviews summary
Pycaret classification anatomy
According to students, PyCaret: Anatomy of Classification is well-received for being informative for novice data scientists. One learner describes the material as helpful.
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 PyCaret: Anatomy of Classification with these
activities:
Join a PyCaret Study Group
Show steps
Joining a study group will provide you with opportunities to discuss course material, share knowledge, and learn from others.
Show steps
-
Find a study group or forum dedicated to PyCaret and machine learning.
-
Participate in discussions, ask questions, and share your own experiences.
-
Collaborate with others on projects or assignments.
Practice PyCaret Environment Setup
Show steps
Practicing setting up the PyCaret environment will help you become familiar with the process and troubleshoot any issues.
Browse courses on
Data Preparation
Show steps
-
Install PyCaret and its dependencies using pip or conda.
-
Import the necessary libraries and set up a Python environment for your machine learning project.
-
Create a new Jupyter notebook or Python script and import the PyCaret library.
-
Load a sample dataset and explore its contents using PyCaret's data inspection functions.
-
Perform basic data cleaning and preprocessing steps, such as handling missing values and outliers.
Explore PyCaret's Advanced Features
Show steps
Exploring PyCaret's advanced features will help you expand your skillset and tackle more complex machine learning challenges.
Show steps
-
Learn about PyCaret's ensemble modeling capabilities, such as blending, stacking, and bagging.
-
Use PyCaret's feature engineering functions to transform and create new features from your data.
-
Follow tutorials or read documentation to gain practical experience with these advanced features.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a PyCaret Model Comparison Report
Show steps
Creating a model comparison report will help you evaluate and compare different models and make informed decisions about which model to use.
Browse courses on
Model Comparison
Show steps
-
Train multiple machine learning models using PyCaret's automated machine learning capabilities.
-
Use PyCaret's model comparison functions to evaluate the performance of each model on metrics such as accuracy, precision, and recall.
-
Create a table or visualization to compare the performance of different models.
-
Write a summary of your findings and recommend the best model for your specific use case.
Build a Machine Learning Project with PyCaret
Show steps
Building a machine learning project will allow you to apply your skills and knowledge in a practical setting.
Show steps
-
Define the problem statement and gather the necessary data.
-
Use PyCaret to preprocess the data, train models, and evaluate their performance.
-
Deploy the best model and monitor its performance in a production environment.
-
Document your project and share your findings with others.
Contribute to the PyCaret Community
Show steps
Contributing to the PyCaret community will allow you to give back to the community and enhance your skills.
Browse courses on
Open Source
Show steps
-
Review the PyCaret GitHub repository and identify areas where you can contribute.
-
Submit bug fixes, feature requests, or documentation improvements.
-
Collaborate with other contributors on the project.
Join a PyCaret Study Group
Show steps
Joining a study group will provide you with opportunities to discuss course material, share knowledge, and learn from others.
Show steps
Show steps
Show steps
- Find a study group or forum dedicated to PyCaret and machine learning.
- Participate in discussions, ask questions, and share your own experiences.
- Collaborate with others on projects or assignments.
Practice PyCaret Environment Setup
Show steps
Practicing setting up the PyCaret environment will help you become familiar with the process and troubleshoot any issues.
Browse courses on
Data Preparation
Show steps
Show steps
Show steps
- Install PyCaret and its dependencies using pip or conda.
- Import the necessary libraries and set up a Python environment for your machine learning project.
- Create a new Jupyter notebook or Python script and import the PyCaret library.
- Load a sample dataset and explore its contents using PyCaret's data inspection functions.
- Perform basic data cleaning and preprocessing steps, such as handling missing values and outliers.
Explore PyCaret's Advanced Features
Show steps
Exploring PyCaret's advanced features will help you expand your skillset and tackle more complex machine learning challenges.
Show steps
Show steps
Show steps
- Learn about PyCaret's ensemble modeling capabilities, such as blending, stacking, and bagging.
- Use PyCaret's feature engineering functions to transform and create new features from your data.
- Follow tutorials or read documentation to gain practical experience with these advanced features.
Three other activities
Expand to see all activities and additional details
Show all six activities
Create a PyCaret Model Comparison Report
Show steps
Creating a model comparison report will help you evaluate and compare different models and make informed decisions about which model to use.
Browse courses on
Model Comparison
Show steps
Show steps
Show steps
- Train multiple machine learning models using PyCaret's automated machine learning capabilities.
- Use PyCaret's model comparison functions to evaluate the performance of each model on metrics such as accuracy, precision, and recall.
- Create a table or visualization to compare the performance of different models.
- Write a summary of your findings and recommend the best model for your specific use case.
Build a Machine Learning Project with PyCaret
Show steps
Building a machine learning project will allow you to apply your skills and knowledge in a practical setting.
Show steps
Show steps
Show steps
- Define the problem statement and gather the necessary data.
- Use PyCaret to preprocess the data, train models, and evaluate their performance.
- Deploy the best model and monitor its performance in a production environment.
- Document your project and share your findings with others.
Contribute to the PyCaret Community
Show steps
Contributing to the PyCaret community will allow you to give back to the community and enhance your skills.
Browse courses on
Open Source
Show steps
Show steps
Show steps
- Review the PyCaret GitHub repository and identify areas where you can contribute.
- Submit bug fixes, feature requests, or documentation improvements.
- Collaborate with other contributors on the project.
Career center
Learners who complete PyCaret: Anatomy of Classification will develop knowledge and skills
that may be useful to these careers:
Chief Data Officer
Chief Data Officer
Chief data officers use their skills in data science, management, and leadership to lead data science initiatives for organizations. They work with stakeholders to define data strategies, develop and implement data governance policies, and track progress. This course would be helpful for someone who wants to become a chief data officer because it teaches how to work with and analyze data.
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers use their skills to apply machine learning models to solve business problems. They work alongside data scientists to help organizations create new products and services, as well as helping to improve existing ones. If someone who wants to be a machine learning engineer were to take this course, they would learn the basics of building and evaluating machine learning models.
Data Science Manager
Data Science Manager
Data science managers use their skills in data science, management, and leadership to lead teams of data scientists and other professionals. They work with stakeholders to define data science goals, develop and implement data science strategies, and track progress. This course would be helpful for someone who wants to become a data science manager because it teaches how to work with and analyze data.
Data Scientist
Data Scientist
Data scientists help organizations to make informed decisions by collecting and analyzing data. This is a great career path for someone who enjoys working with data and solving problems. Data scientists utilize their skills in machine learning and artificial intelligence to help companies gain advantages over their competition. Understanding how to build good machine learning models is important for a data scientist, which is why this course would be a great addition for someone who wants to become a data scientist.
Data Engineer
Data Engineer
Data engineers use their skills in computer science and engineering to design, build, and maintain data pipelines. They work with data scientists and other stakeholders to ensure that data is reliable, secure, and accessible. This course would be helpful for someone who wants to become a data engineer because it teaches how to work with and analyze data.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use their skills in mathematics, statistics, and computer science to solve business problems. They work with stakeholders to develop and implement solutions that improve efficiency and productivity. This course would be helpful for someone who wants to become an operations research analyst because it teaches how to work with and analyze data.
Statistician
Statistician
Statisticians use their skills in mathematics and statistics to collect, analyze, interpret, and present data. They can work in a variety of industries, such as healthcare, finance, and government. Someone who wants to become a statistician would find this course to be helpful because it teaches how to work with and analyze data.
Quantitative Analyst
Quantitative Analyst
This course would be helpful for a quantitative analyst because it teaches how to work with and analyze data. Quantitative analysts use mathematical and statistical models to analyze data and make recommendations for businesses. They can work in a variety of industries, such as finance, insurance, and consulting.
Risk Analyst
Risk Analyst
Risk analysts use their skills in finance, accounting, and economics to identify and assess risks for businesses and investors. They work with stakeholders to develop risk management plans and strategies. This course would be helpful for someone who wants to become a risk analyst because it teaches how to work with and analyze data.
Business Analyst
Business Analyst
Business analysts use their skills in data analysis, problem solving, and communication to help businesses improve their operations. They work with stakeholders to identify business needs, collect and analyze data, and recommend solutions. This course would be helpful for someone who wants to become a business analyst because it teaches how to work with and analyze data.
Marketing Analyst
Marketing Analyst
Marketing analysts use their skills in data analysis, marketing, and communication to help businesses improve their marketing campaigns. They work with stakeholders to identify marketing opportunities, collect and analyze data, and recommend strategies. This course would be helpful for someone who wants to become a marketing analyst because it teaches how to work with and analyze data.
Financial Analyst
Financial Analyst
Financial analysts use their skills in finance, accounting, and economics to analyze financial data and make recommendations for businesses and investors. They work with stakeholders to identify financial opportunities, assess risks, and develop financial plans. This course would be helpful for someone who wants to become a financial analyst because it teaches how to work with and analyze data.
Product Manager
Product Manager
Product managers use their skills in business, technology, and marketing to develop and launch new products. They work with stakeholders to define product requirements, prioritize features, and track progress. This course would be helpful for someone who wants to become a product manager because it teaches how to work with and analyze data.
Data Analyst
Data Analyst
Data analysts use their training to ensure that data is reliable and secure. Using their skills, they can identify and solve problems by examining large amounts of data. If someone who wants to become a data analyst were to take this course, they will understand ways to prepare data for analysis and gain a fundamental understanding of how to build models and use visualizations.
Software Engineer
Software Engineer
Software engineers design and develop computer applications. They use their skills in programming, mathematics, and engineering to create software that meets the needs of businesses and consumers. While this course would not directly help someone become a software engineer, it may be helpful for someone who wants to work on the development of machine learning applications.
For more career information including salaries, visit:
OpenCourser.com/course/70pho9/pycaret
Reading list
We've selected six 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
PyCaret: Anatomy of Classification.
Provides an introduction to data mining techniques using Python. It covers topics such as data preprocessing, clustering, and classification.
Save
Provides a comprehensive overview of deep learning techniques. It covers topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Provides an introduction to natural language processing techniques using Python. It covers topics such as text preprocessing, text classification, and text generation.
Provides an introduction to computer vision techniques using Python. It covers topics such as image processing, object detection, and image classification.
Save
Provides a comprehensive overview of speech and language processing techniques. It covers topics such as speech recognition, natural language understanding, and speech synthesis.
Provides a comprehensive overview of reinforcement learning techniques. It covers topics such as Markov decision processes, value functions, and policy gradient methods.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/70pho9/pycaret
Share
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Effort
2 hours 10 minutes
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Muhammad Saad uddin
Language
English
Good to know
Offers a gentle introduction to the low-code Python machine learning library known as PyCaret
Suitable for beginners seeking to establish a foundation in low-code machine learning with PyCaret
Provides a comprehensive overview of PyCaret's capabilities, including model creation, comparison, and tuning
Empowers learners to leverage advanced techniques such as stacking, blending, and ensemble modeling with ease
Designed for learners based in North America, with plans to expand accessibility to other regions
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