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Mohamed Jendoubi
In this 1-hour long project-based course, you will create an end-to-end classification 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 classification 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 classification model using PyCaret a low-code Python open-source Machine Learning library.
The goal is to build a model that can accurately predict whether a teacher's project proposal was accepted, based on the data they provided in their application.
You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for classification.
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/cif8v5/build
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What's inside
Syllabus
Project Overview
By the end of this project, you will create an end-to-end classification model using PyCaret a low-code Python open-source Machine Learning library.
The goal is to build a model that can accurately predict whether a teacher's project proposal was accepted, based on the data they provided in their application.
You will learn how to automate the major steps for building, evaluating, comparing and interpreting Machine Learning Models for classification.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Automates major steps of building, evaluating, and interpreting ML models for classification, accelerating efficiency in building POCs and experiments
Emphasizes practical application with a project-based approach to building an end-to-end classification model
Utilizes PyCaret, a low-code Python library, making it accessible to seasoned data scientists and citizen data scientists alike
Save this course
Save Build a Classification 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 Classification Model using PyCaret with these
activities:
Review Python Basics
Show steps
Refresh your knowledge of Python basics, data types, and control flow to prepare for this course.
Browse courses on
Python Basics
Show steps
-
Review online tutorials on Python basics
-
Complete practice exercises on basic Python concepts
Explore PyCaret Documentation and Tutorials
Show steps
Familiarize yourself with the PyCaret library's features and capabilities by exploring its documentation and tutorials.
Browse courses on
Pycaret
Show steps
-
Review PyCaret's documentation on its website
-
Follow guided tutorials on using PyCaret for classification tasks
Practice Data Preprocessing and Feature Engineering
Show steps
Reinforce your understanding of data preprocessing techniques and feature engineering strategies commonly used in machine learning.
Browse courses on
Data Preprocessing
Show steps
-
Solve coding challenges on data preprocessing and feature engineering
-
Create a dataset and apply data preprocessing and feature engineering techniques
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Coding Challenges on Classification Models
Show steps
Sharpen your coding skills and reinforce your understanding of classification models by solving coding challenges.
Browse courses on
Coding Challenges
Show steps
-
Find coding challenges platforms
-
Solve coding challenges related to classification models
Attend a Machine Learning Meetup
Show steps
Connect with fellow learners, practitioners, and experts in the field of machine learning to expand your knowledge and stay up-to-date.
Browse courses on
Networking
Show steps
-
Find a local machine learning meetup
-
Attend the meetup and engage in discussions
Participate in a PyCaret Hands-On Workshop
Show steps
Accelerate your learning by participating in a hands-on workshop that provides practical experience with PyCaret and machine learning.
Show steps
-
Find a PyCaret workshop in your area
-
Attend the workshop and actively participate
Build a Simple Classification Model
Show steps
Solidify your understanding of building and evaluating classification models by creating your own simple model.
Browse courses on
Classification Modeling
Show steps
-
Choose a dataset and define the classification task
-
Load and preprocess the data
-
Train and evaluate a classification model
-
Interpret the model's results
Develop a Machine Learning Project Proposal
Show steps
Demonstrate your understanding of the machine learning life cycle by developing a project proposal that outlines a research question, methodology, and expected outcomes.
Show steps
-
Identify a real-world problem or dataset
-
Define the research question and objectives
-
Propose a machine learning methodology
-
Outline the expected outcomes and impact
Review Python Basics
Show steps
Refresh your knowledge of Python basics, data types, and control flow to prepare for this course.
Browse courses on
Python Basics
Show steps
Show steps
Show steps
- Review online tutorials on Python basics
- Complete practice exercises on basic Python concepts
Explore PyCaret Documentation and Tutorials
Show steps
Familiarize yourself with the PyCaret library's features and capabilities by exploring its documentation and tutorials.
Browse courses on
Pycaret
Show steps
Show steps
Show steps
- Review PyCaret's documentation on its website
- Follow guided tutorials on using PyCaret for classification tasks
Practice Data Preprocessing and Feature Engineering
Show steps
Reinforce your understanding of data preprocessing techniques and feature engineering strategies commonly used in machine learning.
Browse courses on
Data Preprocessing
Show steps
Show steps
Show steps
- Solve coding challenges on data preprocessing and feature engineering
- Create a dataset and apply data preprocessing and feature engineering techniques
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve Coding Challenges on Classification Models
Show steps
Sharpen your coding skills and reinforce your understanding of classification models by solving coding challenges.
Browse courses on
Coding Challenges
Show steps
Show steps
Show steps
- Find coding challenges platforms
- Solve coding challenges related to classification models
Attend a Machine Learning Meetup
Show steps
Connect with fellow learners, practitioners, and experts in the field of machine learning to expand your knowledge and stay up-to-date.
Browse courses on
Networking
Show steps
Show steps
Show steps
- Find a local machine learning meetup
- Attend the meetup and engage in discussions
Participate in a PyCaret Hands-On Workshop
Show steps
Accelerate your learning by participating in a hands-on workshop that provides practical experience with PyCaret and machine learning.
Show steps
Show steps
Show steps
- Find a PyCaret workshop in your area
- Attend the workshop and actively participate
Build a Simple Classification Model
Show steps
Solidify your understanding of building and evaluating classification models by creating your own simple model.
Browse courses on
Classification Modeling
Show steps
Show steps
Show steps
- Choose a dataset and define the classification task
- Load and preprocess the data
- Train and evaluate a classification model
- Interpret the model's results
Develop a Machine Learning Project Proposal
Show steps
Demonstrate your understanding of the machine learning life cycle by developing a project proposal that outlines a research question, methodology, and expected outcomes.
Show steps
Show steps
Show steps
- Identify a real-world problem or dataset
- Define the research question and objectives
- Propose a machine learning methodology
- Outline the expected outcomes and impact
Career center
Learners who complete Build a Classification Model using PyCaret will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys Machine Learning models. The course will help you learn how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Data Scientist
Data Scientist
A Data Scientist uses a variety of tools, including Python and R, to analyze data and build models. The course will help you learn how to automate the major steps for building, evaluating, comparing, and interpreting Machine Learning Models for classification.
Data Analyst
Data Analyst
A Data Analyst uses SQL and Python to transform raw data into understandable formats. The course will help you understand the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Business Analyst
Business Analyst
A Business Analyst analyzes business data to identify opportunities and solve problems. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Product Manager
Product Manager
A Product Manager plans, develops, and launches new products. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Consultant
Consultant
A Consultant provides advice and guidance to businesses and organizations. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Teacher
Teacher
A Teacher educates students in a variety of subjects. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Researcher
Researcher
A Researcher conducts research in a variety of fields. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Financial Analyst
Financial Analyst
A Financial Analyst analyzes financial data to make investment recommendations. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Marketing Manager
Marketing Manager
A Marketing Manager plans and executes marketing campaigns. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Sales Manager
Sales Manager
A Sales Manager leads a team of salespeople. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Customer Success Manager
Customer Success Manager
A Customer Success Manager helps customers get the most value from a product or service. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
Operations Manager
Operations Manager
An Operations Manager oversees the day-to-day operations of a business or organization. The course will help you learn the basic concepts of Machine Learning and how to build, evaluate, compare, and interpret Machine Learning Models for classification.
For more career information including salaries, visit:
OpenCourser.com/course/cif8v5/build
Reading list
We've selected 11 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 Classification Model using PyCaret.
A comprehensive introduction to ML using Python and its popular libraries, including preprocessing, feature engineering, model selection, and evaluation.
A comprehensive guide to ML with Python, covering essential concepts and best practices in model building.
An accessible introduction to data science and ML, covering data collection, cleaning, analysis, and visualization.
A theoretical and mathematical treatment of ML, providing a deep understanding of the underlying principles.
While not specific to ML, this book is highly recommended for beginners who need to brush up on their Python programming skills.
Provides a strong mathematical foundation for ML, covering linear algebra, calculus, probability, and optimization.
Save
An in-depth reference on deep learning, covering advanced topics such as neural networks, convolutional neural networks, and recurrent neural networks.
Explores techniques for making ML models more interpretable, providing insights into model behavior and predictions.
Covers advanced deep learning techniques for natural language processing, such as text classification, machine translation, and question answering.
Focuses on ML applications in the financial industry, providing insights into risk management, trading strategies, and portfolio optimization.
A seminal work on decision tree algorithms, providing a theoretical and practical understanding of their construction and use.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cif8v5/build
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Effort
2 hours
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Mohamed Jendoubi
Language
English
Good to know
Automates major steps of building, evaluating, and interpreting ML models for classification, accelerating efficiency in building POCs and experiments
Emphasizes practical application with a project-based approach to building an end-to-end classification model
Utilizes PyCaret, a low-code Python library, making it accessible to seasoned data scientists and citizen data scientists alike
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