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Mohamed Jendoubi
In this 1-hour long project-based course, you will create an end-to-end Topic 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 Topic 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 Topic model using PyCaret a low-code Python open-source Machine Learning library.
You will learn how to automate the major steps for preprocessing, building, evaluating and deploying Machine Learning Models for Topic .
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/rt0evh/topic
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What's inside
Syllabus
Project Overview
By the end of this project, you will create an end-to-end topic model using PyCaret a low-code Python open-source Machine Learning library. The goal is to build a model that can detect topics from Wikipedia users comments. You will learn how to automate the major steps for preprocessing, building, evaluating and deploying Topic Model.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Suitable for seasoned Data Scientists aiming to improve efficiency in building POCs and experiments using low-code libraries
Provides a comprehensive overview of major steps involved in building a Topic model
Framework includes data preprocessing, transformation, model building, evaluation, interpretation, and deployment
Guided project format is designed for hands-on learning and immediate application
Save this course
Save Topic Modeling 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 Topic Modeling using PyCaret with these
activities:
Compile Your Learning Resources
Show steps
Organize your notes and course materials for future reference and reinforcement
Show steps
-
Gather your notes
-
Organize your notes
-
Review your notes
Review Python
Show steps
This course is most effective when taken with a solid understanding of Python
Browse courses on
Python
Show steps
-
Review Python syntax
-
Solve Python exercises
Review Machine Learning
Show steps
This course is most effective when taken with a solid understanding of Machine Learning
Browse courses on
Machine Learning
Show steps
-
Review Machine Learning algorithms
-
Solve Machine Learning problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow an Online Topic Modeling Tutorial
Show steps
Supplement your learning with guided tutorials to reinforce concepts
Show steps
-
Find a tutorial
-
Complete each step
-
Apply what you have learned
Join a Topic Modeling Study Group
Show steps
Collaborate with others to enhance your learning and understanding
Show steps
-
Find a study group
-
Participate in discussions
-
Work on projects together
Practice Topic Modeling
Show steps
Practice Topic Modeling to improve understanding and skills
Show steps
-
Find a Topic Modeling dataset
-
Apply different Topic Modeling algorithms
-
Evaluate the results
Build a Topic Model
Show steps
Build a Topic Model to apply your skills and knowledge in a practical setting
Show steps
-
Collect data
-
Preprocess the data
-
Build the Topic Model
-
Evaluate the Topic Model
-
Deploy the Topic Model
Participate in a Topic Modeling Competition
Show steps
Challenge yourself and gain recognition by participating in a Topic Modeling competition
Show steps
-
Find a competition
-
Prepare for the competition
-
Submit your solution
Compile Your Learning Resources
Show steps
Organize your notes and course materials for future reference and reinforcement
Show steps
Show steps
Show steps
- Gather your notes
- Organize your notes
- Review your notes
Review Python
Show steps
This course is most effective when taken with a solid understanding of Python
Browse courses on
Python
Show steps
Show steps
Show steps
- Review Python syntax
- Solve Python exercises
Review Machine Learning
Show steps
This course is most effective when taken with a solid understanding of Machine Learning
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review Machine Learning algorithms
- Solve Machine Learning problems
Five other activities
Expand to see all activities and additional details
Show all eight activities
Follow an Online Topic Modeling Tutorial
Show steps
Supplement your learning with guided tutorials to reinforce concepts
Show steps
Show steps
Show steps
- Find a tutorial
- Complete each step
- Apply what you have learned
Join a Topic Modeling Study Group
Show steps
Collaborate with others to enhance your learning and understanding
Show steps
Show steps
Show steps
- Find a study group
- Participate in discussions
- Work on projects together
Practice Topic Modeling
Show steps
Practice Topic Modeling to improve understanding and skills
Show steps
Show steps
Show steps
- Find a Topic Modeling dataset
- Apply different Topic Modeling algorithms
- Evaluate the results
Build a Topic Model
Show steps
Build a Topic Model to apply your skills and knowledge in a practical setting
Show steps
Show steps
Show steps
- Collect data
- Preprocess the data
- Build the Topic Model
- Evaluate the Topic Model
- Deploy the Topic Model
Participate in a Topic Modeling Competition
Show steps
Challenge yourself and gain recognition by participating in a Topic Modeling competition
Show steps
Show steps
Show steps
- Find a competition
- Prepare for the competition
- Submit your solution
Career center
Learners who complete Topic Modeling using PyCaret will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you design a workflow that involves gathering data, cleaning, and conducting data analysis to find actionable insights that a company can use to improve its products and services. By taking this course, you will learn how to create machine learning models to detect topics from Wikipedia user comments. This skill is highly relevant to the job of a Data Scientist, and can help you succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. By taking this course, you will learn how to use PyCaret, a low-code Python open-source Machine Learning library, to build, evaluate, and deploy machine learning models for Topic Modeling. This skill is highly relevant to the job of a Machine Learning Engineer, and can help you succeed in this field.
Data Analyst
Data Analyst
As a Data Analyst, you uncover helpful information to aid companies in making better business decisions. By taking this project-based course, you will learn how to create an end-to-end Topic model to build a model that can detect topics from Wikipedia users comments. This skill can help you succeed in the role of a Data Analyst.
Business Analyst
Business Analyst
As a Business Analyst, you provide solutions to business problems. This often involves analyzing data and developing recommendations for improvement. By taking this course, you will learn how to use PyCaret to automate the major steps for preprocessing, building, evaluating, and deploying Machine Learning Models for Topic Modeling. This skill can help you succeed in the role of a Business Analyst.
Consultant
Consultant
As a Consultant, you provide expert advice to businesses and organizations on a variety of topics, including data science and machine learning. By taking this course, you will learn how to build and deploy machine learning models for Topic Modeling using PyCaret. This skill can help you succeed in the role of a Consultant.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you use mathematical and statistical models to analyze data and make predictions. By taking this course, you will learn how to use PyCaret to automate the major steps for preprocessing, building, evaluating, and deploying Machine Learning Models for Topic Modeling. This skill can help you succeed in the role of a Quantitative Analyst.
Software Engineer
Software Engineer
As a Software Engineer, you design, develop, and maintain software applications. By taking this course, you will learn how to use PyCaret, a low-code Python open-source Machine Learning library, to build, evaluate, and deploy machine learning models for Topic Modeling. This skill can help you succeed in the role of a Software Engineer.
Product Manager
Product Manager
As a Product Manager, you are responsible for the development and launch of new products. By taking this course, you will learn how to use PyCaret to automate the major steps for preprocessing, building, evaluating, and deploying Machine Learning Models for Topic Modeling. This skill can help you succeed in the role of a Product Manager.
Project Manager
Project Manager
As a Project Manager, you plan, execute, and close projects. By taking this course, you will learn how to use PyCaret, a low-code Python open-source Machine Learning library, to build, evaluate, and deploy machine learning models for Topic Modeling. This skill can help you succeed in the role of a Project Manager.
Operations Research Analyst
Operations Research Analyst
As an Operations Research Analyst, you use mathematical and analytical techniques to solve business problems. By taking this course, you will learn how to use PyCaret to automate the major steps for preprocessing, building, evaluating, and deploying Machine Learning Models for Topic Modeling. This skill can help you succeed in the role of an Operations Research Analyst.
Statistician
Statistician
As a Statistician, you collect, analyze, interpret, and present data. By taking this course, you will learn how to use PyCaret to automate the major steps for preprocessing, building, evaluating, and deploying Machine Learning Models for Topic Modeling. This skill can help you succeed in the role of a Statistician.
Data Engineer
Data Engineer
As a Data Engineer, you design, build, and maintain data pipelines. By taking this course, you will learn how to use PyCaret, a low-code Python open-source Machine Learning library, to build, evaluate, and deploy machine learning models for Topic Modeling. This skill can help you succeed in the role of a Data Engineer.
Data Science Manager
Data Science Manager
As a Data Science Manager, you lead and manage a team of data scientists. By taking this course, you will learn how to use PyCaret to automate the major steps for preprocessing, building, evaluating, and deploying Machine Learning Models for Topic Modeling. This skill can help you succeed in the role of a Data Science Manager.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
As an Artificial Intelligence Engineer, you design, develop, and deploy AI systems. By taking this course, you will learn how to use PyCaret, a low-code Python open-source Machine Learning library, to build, evaluate, and deploy machine learning models for Topic Modeling. This skill can help you succeed in the role of an Artificial Intelligence Engineer.
Machine Learning Scientist
Machine Learning Scientist
As a Machine Learning Scientist, you research and develop new machine learning algorithms. By taking this course, you will learn how to use PyCaret to automate the major steps for preprocessing, building, evaluating, and deploying Machine Learning Models for Topic Modeling. This skill can help you succeed in the role of a Machine Learning Scientist.
For more career information including salaries, visit:
OpenCourser.com/course/rt0evh/topic
Reading list
We've selected eight 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
Topic Modeling using PyCaret.
Provides a hands-on guide to building and deploying machine learning models using popular Python libraries such as Scikit-Learn, Keras, and TensorFlow.
Provides a comprehensive overview of advanced machine learning techniques, including ensemble methods, support vector machines, and kernel methods.
Provides a rigorous mathematical treatment of information theory, inference, and learning algorithms. It valuable resource for understanding the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning using Python. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
Provides a comprehensive guide to machine learning in Python. It covers a wide range of topics, including supervised and unsupervised learning, natural language processing, and computer vision.
Save
Provides a comprehensive overview of machine learning using Java. It covers a wide range of topics, including supervised and unsupervised learning, natural language processing, and computer vision.
Provides a detailed introduction to deep learning, with a focus on the Python programming language. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Provides a comprehensive overview of machine learning using R. It covers a wide range of topics, including data preprocessing, feature engineering, model selection, and model evaluation.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/rt0evh/topic
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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 seasoned Data Scientists aiming to improve efficiency in building POCs and experiments using low-code libraries
Provides a comprehensive overview of major steps involved in building a Topic model
Framework includes data preprocessing, transformation, model building, evaluation, interpretation, and deployment
Guided project format is designed for hands-on learning and immediate application
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