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Simplifying Machine Learning with PyCaret in Python
Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.
48 Lessons
2h 15min
Join 2.9 million developers at
Join 2.9 million developers at
LEARNING OBJECTIVES
- Knowledge of PyCaret
- Expertise in Streamlit and app deployment on Streamlit Cloud
- Regression with PyCaret and familiarity with exploratory data analysis, environment setup and building machine learning models
- Classification with PyCaret and creating, tuning, plotting and saving machine learning models
- Clustering tasks with PyCaret
- Anomaly Detection with PyCaret
Learning Roadmap
1.
Introduction to Machine Learning
Introduction to Machine Learning
Get familiar with PyCaret, machine learning types, and applications in this beginner course.
2.
Regression
Regression
Get started with regression techniques, EDA, PyCaret setup, model building, and practical coding challenges.
3.
Classification
Classification
7 Lessons
7 Lessons
Go hands-on with understanding and implementing classification using PyCaret in Python.
4.
Clustering
Clustering
7 Lessons
7 Lessons
Build a foundation in clustering techniques using PyCaret, from setup to model evaluation.
5.
Anomaly Detection
Anomaly Detection
7 Lessons
7 Lessons
Map out the steps for building and evaluating anomaly detection models using PyCaret.
6.
Natural Language Processing
Natural Language Processing
9 Lessons
9 Lessons
See how it works to conduct NLP analysis, topic modeling, and classification with PyCaret.
7.
Deploying a Machine Learning Model
Deploying a Machine Learning Model
7 Lessons
7 Lessons
Master the steps to deploy machine learning models using Streamlit, build web apps, and visualize data.
Certificate of Completion
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Developed by MAANG Engineers
ABOUT THIS COURSE
PyCaret is an low-code, open-source, machine learning library for Python. It can be used in machine learning tasks such as data preparation and model deployment.
In this course, you will learn multiple topics related to machine learning. You will start with a brief introduction to the basic concepts of machine learning, and then continue with case studies of regression, classification, clustering, and anomaly detection based on the respective modules of the PyCaret library. Finally, we will focus on using the Streamlit library to develop and deploy machine learning applications.
By the end of this course, you will have the skills to deploy the robust PyCaret library for any of your machine learning projects.
ABOUT THE AUTHOR
Giannis Tolios
Giannis Tolios is a data scientist who is passionate about expanding his knowledge, evolving as a professional and using machine learning for good.
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