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Simplifying Machine Learning with PyCaret in Python

Name: Simplifying Machine Learning with PyCaret in Python
Price: 59 USD
Author: Giannis Tolios

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

1 project

2h 15min

Created by Giannis Tolios

Simplifying Machine Learning with PyCaret in Python

What's included

48 lessons across 9 modules
6 quizzes with instant feedback
1 project in preconfigured environments
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

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

48 Lessons6 Quizzes

Introduction to Machine Learning

Get familiar with PyCaret, machine learning types, and applications in this beginner course.

Getting Started

Machine Learning and its Types

Regression

Get started with regression techniques, EDA, PyCaret setup, model building, and practical coding challenges.

Regression with PyCaret

Exploratory Data Analysis

Getting Familiar with the PyCaret Environment

Building the Model

Coding Challenge: Train Random Forest Model

Solution Review: Train Random Forest Model

Quiz: Regression

Classification

7 Lessons

Go hands-on with understanding and implementing classification using PyCaret in Python.

Clustering

7 Lessons

Build a foundation in clustering techniques using PyCaret, from setup to model evaluation.

Customer Segmentation with K-Means Clustering

Project

Premium

Anomaly Detection

7 Lessons

Map out the steps for building and evaluating anomaly detection models using PyCaret.

Natural Language Processing

9 Lessons

See how it works to conduct NLP analysis, topic modeling, and classification with PyCaret.

Deploying a Machine Learning Model

7 Lessons

Master the steps to deploy machine learning models using Streamlit, build web apps, and visualize data.

Conclusion

Sharpen your skills in machine learning basics and explore further with PyCaret and Streamlit.

Closing Thoughts

Appendix

Get started with installing PyCaret and its dependencies for efficient machine learning.

Installation of PyCaret

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE 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.

Learn more about Giannis

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