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

**Classification** is one of the fundamental supervised learning tasks. Its goal is to predict a categorical variable known as the class label. This task is known as binary classification when there are only two classes ($0$ and $1$), or **multiclass classification** in case there are more. One of the most widely used binary classification models is **logistic regression**. It is defined in the following equation:

$$\log (\frac{p_{n}}{1-p_{n}})=\beta_{0}+\beta_{1} x_{n 1}+\cdots+\beta_{p} x_{n p}=\beta^{T} X_{n}
$$


* $\log (\frac{p_{n}} {1-p_{n}})$ is the natural logarithm of the odds, known as the ***logit function***.
* $x_1$ to $x_p$ are the feature variables.
* $\beta_{0}$ is the ***intercept*** term.
* $\beta_{1}$ to $\beta_{p}$ are the coefficients of the feature variables.
* $\beta^{T} X_{n}$ is the vectorized form of the equation.
Our goal is to calculate $p_{n}$ which is the probability that an instance of the given dataset belongs to class $1$. The **logistic function $\sigma(z)$** is the inverse of the logit (or log-odds) function, so we can apply it and get the desired result.
$$
p_{n}=\sigma (\beta^{T} X_{n})=\frac{1}{1+\exp (-\beta^{T} X_{n})}
$$


Learn how to import necessary libraries and datasets for classification with PyCaret.

Introduction to Machine Learning

Regression

Classification

Clustering

Customer Segmentation with K-Means Clustering

Anomaly Detection

Natural Language Processing

Deploying a Machine Learning Model

Conclusion

Appendix

Classification with PyCaret