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

c.tar.gz

jupyter

RunCode

spa1

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

We’ll perform EDA on the BBC News dataset.

# Bar chart
By using the `value_counts()` and `plot()` pandas functions, we can create a bar chart that visualizes class proportions.

# Plotting the bar chart

color = ['C0', 'C1', 'C2', 'C3', 'C4']
categories = data['category'].value_counts()
categories.plot(kind = 'bar', figsize = (12,8), color = color)
plt.show()

As we can see in the output, the dataset is imbalanced because the classes aren’t evenly distributed. We’ll deal with this issue later because right now it may cause problems with classification model training. The two most common categories are sport and business, each with about $500$ articles. On the other hand, entertainment is the least popular with fewer than $400$ articles.

# Word cloud
The word frequencies of a given text can be easily visualized with a **word cloud**. This type of visualization displays every word with a different font size and color depending on the number of its occurrences. It thus highlights the prominence of common words.

Learn how to perform exploratory data analysis for natural language processing on a dataset and plot data on bar charts and word clouds.

Introduction to Machine Learning

Regression

Classification

Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Exploratory Data Analysis

Bar chart