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Mohamed Jendoubi
In this 1-hour long project-based course, you will create an end-to-end clustering model using PyCaret a low-code Python open-source Machine Learning library.
The goal is to build a model that can segment a wholesale customers based on their historical purchases.
You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for clustering.
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.
Read more
In this 1-hour long project-based course, you will create an end-to-end clustering model using PyCaret a low-code Python open-source Machine Learning library.
The goal is to build a model that can segment a wholesale customers based on their historical purchases.
You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for clustering.
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.
Read more
In this 1-hour long project-based course, you will create an end-to-end clustering model using PyCaret a low-code Python open-source Machine Learning library.
The goal is to build a model that can segment a wholesale customers based on their historical purchases.
You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for clustering.
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.
To be successful in this project, you should be familiar with Python and the basic concepts on Machine Learning.
Register for this course and see more details by visiting:
OpenCourser.com/course/jhls2g/build
What's inside
Syllabus
Project Overview
By the end of this project, you will create an end-to-end clustering model using PyCaret a low-code Python open-source Machine Learning library.The goal is to build a model that can segment a wholesale customers.You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for clustering.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Accelerates the efficiency in building POC and experiments by using a low-code library
Teaches how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for clustering
Segments a wholesale customers based on their historical purchases
Provides steps to frame the problem, get and prepare the data, discover and visualize the data, create the transformation pipeline, build, evaluate, interpret and deploy the model
Uses a low-code Python open-source Machine Learning library called PyCaret
Helps seasoned Data Scientists and Citizen data Scientists add machine learning models to the analytics toolkit
Save this course
Save Build a Clustering 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 Clustering Model using PyCaret with these
activities:
Mentorship: Seeking Guidance in Clustering
Show steps
Connect with experienced professionals to gain valuable insights and guidance in clustering.
Show steps
-
Identify potential mentors who have expertise in clustering or data science.
-
Reach out to your mentors, expressing your interest in learning more.
-
Schedule meetings or discussions to seek guidance on specific topics or projects.
-
Actively listen and engage with your mentors, absorbing their knowledge and experience.
Mentorship: Supporting Aspiring Data Scientists
Show steps
Contribute to the community by guiding and supporting aspiring data scientists.
Show steps
-
Identify a platform or organization where you can connect with mentees.
-
Offer your expertise and guidance to those seeking support in clustering or data science.
-
Provide constructive feedback and encouragement to help mentees develop their skills.
-
Learn from the experiences and perspectives of your mentees.
Course Materials Compilation
Show steps
Organize and enhance your course materials for improved retention and understanding.
Show steps
-
Gather all relevant course materials, including notes, assignments, and quizzes.
-
Organize the materials into a logical structure, such as by topic or module.
-
Review and summarize key concepts to reinforce your understanding.
-
Identify any gaps or areas where further clarification is needed.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Peer Discussion: Clustering Techniques
Show steps
Engage with peers to exchange knowledge and gain diverse perspectives on clustering techniques.
Show steps
-
Identify a peer group or online forum for discussion.
-
Propose discussion topics related to clustering algorithms, evaluation metrics, or case studies.
-
Actively participate in discussions, sharing your knowledge and learning from others.
-
Summarize key points and insights gained from the discussion.
Clustering Algorithms Drill
Show steps
Enhance your understanding of clustering algorithms through repetitive practice.
Browse courses on
Clustering
Show steps
-
Identify a set of clustering algorithms to practice.
-
Find practice problems or datasets that involve clustering.
-
Apply the chosen algorithms to the practice problems, comparing their performance and outcomes.
-
Analyze the results, identifying patterns and areas for improvement.
Guided Tutorial: Creating a Data Preparation Pipeline
Show steps
Build a strong foundation in data preparation techniques by following a guided tutorial.
Browse courses on
Data Preprocessing
Show steps
-
Locate a suitable guided tutorial on data preparation.
-
Follow the tutorial step-by-step, applying the techniques to a dataset of your choice.
-
Document the steps and any insights gained during the tutorial.
End-to-End Clustering Model Project
Show steps
Gain practical experience by building a complete clustering model from scratch.
Show steps
-
Define the project goals and objectives.
-
Gather and preprocess the data.
-
Apply clustering algorithms to the data.
-
Evaluate and interpret the results.
-
Document the project and share your findings.
Mentorship: Seeking Guidance in Clustering
Show steps
Connect with experienced professionals to gain valuable insights and guidance in clustering.
Show steps
Show steps
Show steps
- Identify potential mentors who have expertise in clustering or data science.
- Reach out to your mentors, expressing your interest in learning more.
- Schedule meetings or discussions to seek guidance on specific topics or projects.
- Actively listen and engage with your mentors, absorbing their knowledge and experience.
Mentorship: Supporting Aspiring Data Scientists
Show steps
Contribute to the community by guiding and supporting aspiring data scientists.
Show steps
Show steps
Show steps
- Identify a platform or organization where you can connect with mentees.
- Offer your expertise and guidance to those seeking support in clustering or data science.
- Provide constructive feedback and encouragement to help mentees develop their skills.
- Learn from the experiences and perspectives of your mentees.
Course Materials Compilation
Show steps
Organize and enhance your course materials for improved retention and understanding.
Show steps
Show steps
Show steps
- Gather all relevant course materials, including notes, assignments, and quizzes.
- Organize the materials into a logical structure, such as by topic or module.
- Review and summarize key concepts to reinforce your understanding.
- Identify any gaps or areas where further clarification is needed.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Peer Discussion: Clustering Techniques
Show steps
Engage with peers to exchange knowledge and gain diverse perspectives on clustering techniques.
Show steps
Show steps
Show steps
- Identify a peer group or online forum for discussion.
- Propose discussion topics related to clustering algorithms, evaluation metrics, or case studies.
- Actively participate in discussions, sharing your knowledge and learning from others.
- Summarize key points and insights gained from the discussion.
Clustering Algorithms Drill
Show steps
Enhance your understanding of clustering algorithms through repetitive practice.
Browse courses on
Clustering
Show steps
Show steps
Show steps
- Identify a set of clustering algorithms to practice.
- Find practice problems or datasets that involve clustering.
- Apply the chosen algorithms to the practice problems, comparing their performance and outcomes.
- Analyze the results, identifying patterns and areas for improvement.
Guided Tutorial: Creating a Data Preparation Pipeline
Show steps
Build a strong foundation in data preparation techniques by following a guided tutorial.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Locate a suitable guided tutorial on data preparation.
- Follow the tutorial step-by-step, applying the techniques to a dataset of your choice.
- Document the steps and any insights gained during the tutorial.
End-to-End Clustering Model Project
Show steps
Gain practical experience by building a complete clustering model from scratch.
Show steps
Show steps
Show steps
- Define the project goals and objectives.
- Gather and preprocess the data.
- Apply clustering algorithms to the data.
- Evaluate and interpret the results.
- Document the project and share your findings.
Career center
Learners who complete Build a Clustering Model using PyCaret will develop knowledge and skills
that may be useful to these careers:
Statistician
Statistician
Statisticians interpret and analyze data to help businesses and organizations make informed decisions. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Statisticians.
Data Analyst
Data Analyst
Data Analysts use their skills in data analysis and modeling to identify trends and patterns in data. The Build a Clustering Model using PyCaret course provides a comprehensive overview of the data analysis process, from data preparation to model deployment. By taking this course, Data Analysts can enhance their skills in data clustering, which is a valuable technique for segmenting data and identifying patterns.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, build, and deploy machine learning models. The Build a Clustering Model using PyCaret course provides a practical introduction to machine learning and clustering techniques. By taking this course, Machine Learning Engineers can gain hands-on experience in building and evaluating clustering models, which is a key skill for this role.
Data Scientist
Data Scientist
Data Scientists use their skills in data analysis, modeling, and machine learning to solve business problems. The Build a Clustering Model using PyCaret course provides a comprehensive overview of the data science process, from data collection to model deployment. By taking this course, Data Scientists can enhance their skills in data clustering, which is a valuable technique for segmenting data and identifying patterns.
Business Analyst
Business Analyst
Business Analysts use their skills in data analysis and modeling to help businesses make informed decisions. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Business Analysts.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their skills in data analysis and modeling to make investment decisions. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Quantitative Analysts.
Risk Analyst
Risk Analyst
Risk Analysts use their skills in data analysis and modeling to identify and assess risks. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Risk Analysts.
Market Researcher
Market Researcher
Market Researchers use their skills in data analysis and modeling to understand customer behavior. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Market Researchers.
Financial Analyst
Financial Analyst
Financial Analysts use their skills in data analysis and modeling to make investment decisions. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Financial Analysts.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use their skills in data analysis and modeling to improve business processes. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Operations Research Analysts.
Actuary
Actuary
Actuaries use their skills in data analysis and modeling to assess and manage risk. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Actuaries.
Econometrician
Econometrician
Econometricians use their skills in data analysis and modeling to study economic data. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Econometricians.
Biostatistician
Biostatistician
Biostatisticians use their skills in data analysis and modeling to study biological data. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Biostatisticians.
Epidemiologist
Epidemiologist
Epidemiologists use their skills in data analysis and modeling to study the distribution and determinants of health-related states or events (including disease), and the application of this study to the control of diseases and other health problems. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Epidemiologists.
Survey Researcher
Survey Researcher
Survey Researchers use their skills in data analysis and modeling to design and conduct surveys. The Build a Clustering Model using PyCaret course provides a solid foundation in data analysis and modeling, which are essential skills for success in this role. Additionally, the course covers topics such as data preparation, model evaluation, and interpretation, which are all critical for Survey Researchers.
For more career information including salaries, visit:
OpenCourser.com/course/jhls2g/build
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
Build a Clustering Model using PyCaret.
Comprehensive guide to building Machine Learning models with Python libraries such as Scikit-Learn, Keras, and TensorFlow. It provides detailed explanations of the algorithms used in ML.
Comprehensive introduction to statistical learning methods. It covers a wide range of topics, including linear and logistic regression, decision trees, support vector machines, and clustering.
Save
Comprehensive introduction to Deep Learning. It covers a wide range of topics, including convolutional neural networks, recurrent neural networks, and generative adversarial networks.
Classic text on statistical pattern recognition and Machine Learning. It covers a wide range of topics, including supervised and unsupervised learning, dimensionality reduction, and Bayesian inference.
Save
Practical guide to building predictive models with R. It covers a wide range of topics, including data preparation, feature engineering, and model evaluation.
Teaches you to implement Machine Learning algorithms in Python. It great reference book for someone familiar with Machine Learning looking to learn how to code ML algorithms in Python.
Provides a probabilistic perspective on Machine Learning. It covers a wide range of topics, including Bayesian inference, graphical models, and reinforcement learning.
Save
Provides an algorithmic perspective on Machine Learning. It covers a wide range of topics, including supervised and unsupervised learning, dimensionality reduction, and Bayesian inference.
Provides a practical introduction to Machine Learning. It covers a wide range of topics, including supervised and unsupervised learning, dimensionality reduction, and Bayesian inference.
Gentle introduction to Machine Learning. It provides a broad overview of the ML landscape, including different types of algorithms, evaluation methods, and applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/jhls2g/build
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Mohamed Jendoubi
Language
English
Good to know
Accelerates the efficiency in building POC and experiments by using a low-code library
Teaches how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for clustering
Segments a wholesale customers based on their historical purchases
Provides steps to frame the problem, get and prepare the data, discover and visualize the data, create the transformation pipeline, build, evaluate, interpret and deploy the model
Uses a low-code Python open-source Machine Learning library called PyCaret
Helps seasoned Data Scientists and Citizen data Scientists add machine learning models to the analytics toolkit
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