Simplifying Machine Learning with PyCaret
Simplifying Machine Learning with PyCaret
A Low-code Approach for Beginners and Experts!
About the Book
The field of machine learning has grown substantially in the past years due to technological and scientific advancements. Data scientists and machine learning engineers are among the best paid professionals in the modern job market, and demand for associated skills is extremely high. PyCaret is a low-code machine learning Python library that is easy to use, thus making it accessible to beginners. This book provides hands-on tutorials for each of the main PyCaret modules, such as classification, regression, clustering, anomaly detection and natural language processing. Furthermore, the book will teach you how to develop machine learning applications based on the Streamlit library, as well as deploy them on the cloud. Apart from beginners, this book can also be valuable to experts who want to familiarize themselves with the PyCaret library and its features.
- This book will be updated with additional chapters in the following months. Customers who buy it now are getting lifetime access to all future updates!
- The upcoming chapter will cover the Time Series Forecasting module of PyCaret.
- The book has been optimized for the PDF file format, so the Epub and Mobi versions may have some issues.
- I am donating 10% of the book royalties to Tree-Nation, a non-profit organization that allows citizens and companies to plant trees all around the world and offset their CO2 emissions.
About the Author
Table of Contents
- Preface
- About the Author
-
About the Book
- Who this Book is for
- Prerequisites
- Software Requirements
- Installing PyCaret
- Using JupyterLab
- Github Repository
-
1 A Brief Introduction to Machine Learning
- What is Machine Learning?
-
Machine Learning Categories
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
-
2 Regression
- The Linear Regression Model
-
Regression with PyCaret
- Importing the Necessary Libraries
- Loading the Dataset
- Exploratory Data Analysis
- Initializing the PyCaret Environment
- Comparing Regression Models
- Creating the Model
- Tuning the Model
- Making Predictions
- Plotting the Model
- Finalizing and Saving the Model
-
3 Classification
-
Classification with PyCaret
- Importing the Necessary Libraries
- Loading the Dataset
- Exploratory Data Analysis
- Initializing the PyCaret Environment
- Comparing Classification Models
- Creating the Model
- Tuning the Model
- Making Predictions
- Plotting the Model
- Finalizing and Saving the Model
-
Classification with PyCaret
-
4 Clustering
-
Clustering with PyCaret
- Importing the Necessary Libraries
- Generating a Synthetic Dataset
- Exploratory Data Analysis
- Initializing the PyCaret Environment
- Creating a Model
- Plotting the Model
- Saving and Assigning the Model
-
Clustering with PyCaret
-
5 Anomaly Detection
-
Anomaly Detection with PyCaret
- Importing the Necessary Libraries
- Loading the Dataset
- Exploratory Data Analysis
- Initializing the PyCaret Environment
- Creating and Assigning the Model
- Evaluating the Model
- Plotting the Model
- Saving the Model
-
Anomaly Detection with PyCaret
-
6 Natural Language Processing
-
Natural Language Processing with PyCaret
- Downloading the Additional Resources
- Importing the Necessary Libraries
- Loading the Dataset
- Exploratory Data Analysis
- Initializing the NLP Environment
- Creating and Assigning the Topic Model
- Plotting the Topic Model
- Initializing the Classification Environment
- Creating the Classification Model
- Finalizing and Saving the Models
-
Natural Language Processing with PyCaret
-
7 Deploying a Machine Learning Model
- The Streamlit Framework
-
The Insurance Charges Prediction App
- Developing the Web Application
- Running the Web Application Locally
-
The Iris Classification App
- Developing the Web Application
- Running the Web Application Locally
-
Deploying an Application to Streamlit Cloud
- Creating a Github Repository
- Deploying the Insurance Charges Prediction App
- 8 Closing Thoughts
- Notes
Causes Supported
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