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

Simplifying Machine Learning with PyCaret_ .png

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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

# Solution
Here is a sample solution for the challenge in the previous lesson.

# Importing the necessary libraries

from pycaret.nlp import *
from pycaret.datasets import get_data
# Load the dataset

data = get_data('kiva')
# Get 1st 2000 instances of the dataset

data = data.iloc[0:2000]
# Initializing the PyCaret NLP environment

nlp_setup=setup(data = data, target = 'en', session_id = 123)
# Create the model

lda_model = create_model('lda')
# Plot the model

plot_model(lda_model, plot = 'topic_distribution')

# Explanation
* **Lines 3–4**: We import the necessary libraries for this challenge.

* **Line 7**: We load the `kiva` dataset to a variable named `data`.

* **Line 10**: We load the first 2000 instances of the dataset to a variable named `data`.

* **Line 13**: We initialize the PyCaret NLP environment and assign the result to a variable named `nlp_setup`.

* **Line 16**: We create an LDA model and assign it to a variable named `lda_model`.

* **Line 19**: We plot the topic distribution.

This is the in-depth solution review of the coding challenge "Plot Topic Distribution."

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Solution Review: Plot Topic Distribution

Solution

Explanation