Build a Clustering Model using PyCaret

Course Overview

What does this course tell?
(Please note that the following overview content is from the original platform)
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.We consider the value of this course from multiple aspects, and finally summarize it for you from three aspects: personal skills, career development, and further study:
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What skills and knowledge will you acquire during this course?
During this course, the learner will acquire several skills and knowledge related to building a clustering model using PyCaret. They will learn how to frame the problem and understand the task of segmenting wholesale customers based on their historical purchases. The learner will also gain knowledge on how to obtain and prepare the necessary data for the clustering model.

Furthermore, the learner will learn how to discover and visualize the data, which will help them gain insights and understand the patterns within the dataset. They will also learn how to create a transformation pipeline, which involves preprocessing the data and applying necessary transformations to prepare it for the clustering model.

The course will also cover the major steps for building, evaluating, interpreting, and deploying the clustering model. The learner will gain skills in using PyCaret, a low-code Python open-source Machine Learning library, to automate these steps and compare different machine learning models for clustering.

Overall, this course will equip the learner with the skills and knowledge to efficiently build proof-of-concept projects and experiments using a low-code library. It is suitable for seasoned Data Scientists looking to enhance their efficiency and for Citizen Data Scientists who want to add machine learning models to their analytics toolkit. Familiarity with Python and basic concepts of Machine Learning is recommended for success in this project.

How does this course contribute to professional growth?
This course on building a clustering model using PyCaret contributes to professional growth by providing a comprehensive understanding of the major steps involved in building, evaluating, comparing, and interpreting machine learning models for clustering. By completing this course, professionals can enhance their skills in automating these steps, thereby accelerating the efficiency in building proof of concepts (POC) and experiments.

For seasoned data scientists, this course offers an opportunity to leverage a low-code library like PyCaret to streamline their workflow and improve productivity. By using PyCaret, they can quickly prototype and experiment with different clustering models, saving time and effort in the development process.

Additionally, this course is also beneficial for citizen data scientists, professionals working with data, who can leverage PyCaret to add machine learning models to their analytics toolkit. By gaining proficiency in PyCaret, they can expand their capabilities and effectively apply clustering techniques to segment wholesale customers based on their historical purchases.

Overall, this course enables professionals to enhance their technical skills in Python and machine learning, while also providing practical knowledge on framing the problem, data preparation, data visualization, transformation pipeline creation, model building, evaluation, interpretation, and deployment. By mastering these skills, professionals can contribute to their professional growth and become more proficient in their roles as data scientists or data professionals.

Is this course suitable for preparing further education?
Yes, this course is suitable for preparing further education. By taking this course, individuals will gain hands-on experience in building an end-to-end clustering model using PyCaret, a low-code Python open-source Machine Learning library. They will learn how to automate various steps involved in building, evaluating, comparing, and interpreting Machine Learning Models for clustering. This project is designed for seasoned Data Scientists who want to enhance their efficiency in building proof-of-concepts and experiments using a low-code library. It is also suitable for Citizen Data Scientists who work with data and want to add machine learning models to their analytics toolkit. To succeed in this project, individuals should already have familiarity with Python and basic concepts of Machine Learning.