Learn what PyCaret is and how it can be helpful in machine learning.

Machine Learning with Python Libraries

PyCaret is an open-source machine learning library in Python. It can be used in machine learning tasks such as data preparation and model deployment. 

In this module, we’ll cover multiple topics related to machine learning. First, we’ll start with a brief introduction to the basic theoretical concepts of machine learning. Then we’ll continue with the implementation of regression, classification, clustering, and anomaly detection using the respective modules of the PyCaret library. In the end, we’ll learn to use the Streamlit library to deploy web applications on the Streamlit cloud.

PyCaret

Before we start a machine learning project, we need a solid grasp on the theory behind that technology.  In this lesson, we’ll briefly learn about the basic theoretical concepts necessary to understand the rest of the course. If you’re familiar with them, feel free to skip this lesson.

## What is machine learning

The term **machine learning** was popularized by Arthur Samuel, a prominent figure in artificial intelligence. Samuel defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed.” This was a revolutionary approach: traditional computer algorithms included all the necessary steps to be executed and were explicitly defined by a human programmer. Unfortunately, some problems are simply too difficult and complex, making it almost impossible to define an algorithm for them.

Machine learning can be used to circumvent that obstacle by training the computer to find the solution rather than giving it a set of explicit instructions. You may see machine learning being used interchangeably with artificial intelligence and deep learning. Although it is usually associated with the aforementioned terms, those terms aren’t identical. Machine learning is generally accepted as a subfield of artificial intelligence, while deep learning is a subfield of machine learning.


Before we start a machine learning project, we need a solid grasp on the theory behind that technology.  In this lesson, we’ll briefly learn about the basic theoretical concepts necessary to understand the rest of the course. If you’re familiar with them, feel free to skip this lesson.

# What is machine learning

The term **machine learning** was popularized by Arthur Samuel, a prominent figure in artificial intelligence. Samuel defined machine learning as “the field of study that gives computers the ability to learn without being explicitly programmed.” This was a revolutionary approach: traditional computer algorithms included all the necessary steps to be executed and were explicitly defined by a human programmer. Unfortunately, some problems are simply too difficult and complex, making it almost impossible to define an algorithm for them.

Machine learning can be used to circumvent that obstacle by training the computer to find the solution rather than giving it a set of explicit instructions. You may see machine learning being used interchangeably with artificial intelligence and deep learning. Although it is usually associated with the aforementioned terms, those terms aren’t identical. Machine learning is generally accepted as a subfield of artificial intelligence, while deep learning is a subfield of machine learning.


Let’s familiarize ourselves with machine learning and its types, such as supervised, unsupervised, and reinforcement learning.

Machine Learning and its Types

What is machine learning