Simplifying Machine Learning with PyCaret
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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 and anomaly detection. 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!
  • Upcoming chapters include Natural Language Processing and Time Series Forecasting.
  • 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.
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About the Author

Giannis Tolios
Giannis Tolios

Giannis Tolios is a data scientist who is passionate about expanding his knowledge and evolving as a professional. He has collaborated with numerous companies worldwide as a freelancer, and completed projects related to machine learning, time series forecasting, data visualization and others. Giannis also enjoys writing about data science at established websites such as Towards Data Science and Analytics Vidhya. Giannis strongly believes that technology should be used for good, and is constantly looking for new ways to help mitigate challenges like climate change and economic inequality, by using data science. If you want to learn more about Giannis, you can visit his personal website or follow him on LinkedIn where he’s regularly posting content about data science and other topics.

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
  • 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
  • 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
  • 6 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
  • 7 Closing Thoughts
  • Notes

Causes Supported

Tree-Nation

You reforest the world
https://tree-nation.com

Tree-Nation is the largest reforestation platform enabling citizens and companies to plant trees around the world.

Tree-Nation’s mission is to reforest the world. The organisation offers people the possibility to select from over 300 different tree species which can be planted in 50 different reforestation projects located in 6 different continents. In every project, the species are carefully selected according to the specific benefits they bring for the environment and to the local population. Each tree planted on the Tree-Nation platform is assigned its own unique URL, which means the species, the location, the plantation project information and the CO2 compensation values can be tracked throughout the lifetime of the tree. Since its inception in 2006, more than 130,000 users and more than 2,200 companies have planted 5 million trees using its platform.

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