Gain insights into simplifying machine learning with PyCaret. Delve into regression, classification, clustering, and anomaly detection, and learn to deploy applications using Streamlit.

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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.

Simplifying Machine Learning with PyCaret in Python

# Load the automobile dataset to a variable named `data`

 data = get_data('automobile')

# Initialize PyCaret environment and assign result to a variable named `reg1`

 reg1 = setup(data, target = 'price', silent = True)

# Create a Random Forest model and assign it to a variable named `model`.

 model = create_model('rf')

# Explanation
* **Line 3**: We load the `automobile` dataset into the `data` variable by using the `get_data()` function.
* **Line 7**: We initialize the PyCaret environment by using the `setup()` function. We set the `target` variable to `price` and the `silent` parameter to `True` to avoid any prompts from stopping code execution. We assign the result to the `reg1` variable.
* **Line 11**: We create a random forest model by using the `create_model()` function and assign it to a variable named `model`.

This is the in-depth solution review of the "Train Random Forest Model" coding challenge.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Solution Review: Train Random Forest Model

Solution

Explanation