Build a Classification Model using PyCaret from Coursera

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.

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

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

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

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Refresh your knowledge of Python basics, data types, and control flow to prepare for this course.

Browse courses on Python Basics

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Review online tutorials on Python basics
Complete practice exercises on basic Python concepts

Explore PyCaret Documentation and Tutorials

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Familiarize yourself with the PyCaret library's features and capabilities by exploring its documentation and tutorials.

Browse courses on Pycaret

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Review PyCaret's documentation on its website
Follow guided tutorials on using PyCaret for classification tasks

Practice Data Preprocessing and Feature Engineering

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Reinforce your understanding of data preprocessing techniques and feature engineering strategies commonly used in machine learning.

Browse courses on Data Preprocessing

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Solve coding challenges on data preprocessing and feature engineering
Create a dataset and apply data preprocessing and feature engineering techniques

Five other activities

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Show all eight activities

Solve Coding Challenges on Classification Models

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Sharpen your coding skills and reinforce your understanding of classification models by solving coding challenges.

Browse courses on Coding Challenges

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Find coding challenges platforms
Solve coding challenges related to classification models

Attend a Machine Learning Meetup

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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

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Find a local machine learning meetup
Attend the meetup and engage in discussions

Participate in a PyCaret Hands-On Workshop

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Accelerate your learning by participating in a hands-on workshop that provides practical experience with PyCaret and machine learning.

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Find a PyCaret workshop in your area
Attend the workshop and actively participate

Build a Simple Classification Model

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Solidify your understanding of building and evaluating classification models by creating your own simple model.

Browse courses on Classification Modeling

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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

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Demonstrate your understanding of the machine learning life cycle by developing a project proposal that outlines a research question, methodology, and expected outcomes.

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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

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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Data Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Product Manager

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.

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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.

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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.

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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.

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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.

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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.

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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.

See salaries and explore the career path for Customer Success 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.

See salaries and explore the career path for Operations Manager