Want to get started with low-code machine learning FAST?

Well PyCaret is the library for you, it allows you to leverage state of the art ml pipeline to build your machine learning models. You can build a series of ML models using a single function call and automatically rank different models against each other.

You’ll learn how to build a heart disease prediction model using data from kaggle. As part of the process you’ll learn how to rapidly prototype a machine learning model to predict a binary outcome.

In this video you’ll learn how to:

1. Instal PyCaret (a low code machine learning library for Python)
2. Load in custom data from Kaggle using Pandas
3. Build a PyCaret ML classification model using automated pipelines

Get the code:
https://github.com/nicknochnack/PyCaretClassificationCrashCourse

Links
Data: https://www.kaggle.com/ronitf/heart-disease-uci
PyCaret: https://pycaret.org/
PyCaret create_model: https://pycaret.org/create-model/

Chapters
0:00 - Start
0:51 - Gameplan
1:19 - How it Works
2:38 - 1. Install ad Import Dependencies
5:57 - 2. Load Data
9:40 - 3. Train and Evaluate Model
17:02 - 4. Test Model
19:35 - 5. Saving and Reload Models
21:51 - Wrap Up

Oh, and don’t forget to connect with me!
LinkedIn: https://bit.ly/324Epgo
Facebook: https://bit.ly/3mB1sZD
GitHub: https://bit.ly/3mDJllD
Patreon: https://bit.ly/2OCn3UW
Join the Discussion on Discord: https://bit.ly/3dQiZsV

Happy coding!
Nick

P.s. Let me know how you go and drop a comment if you need a hand!

#pycaret