scikit-learn: machine learning in Python — scikit-learn 1.6.1 documentation
Scikit-Learn 101: Exploring Important Functions - The Data Scientist
Introduction to k-Means Clustering with scikit-learn in Python
Scikit-learn is a powerful Python library widely used for performing complex AI and machine learning (ML) tasks. It is an open-source library that provides numerous robust algorithms, which include regression, classification, dimensionality reduction, and clustering techniques. In this tutorial, we will explore some powerful functions of scikit-learn using scikit-learn toy datasets. Apart from building machine learning models, you will also learn data preprocessing and model evaluation techniques using Python.
Scikit-learn is one of Python’s most popular machine learning libraries. The library is enriched with many incredible data preprocessing, model training, and evaluation features. Some of the reasons why AI practitioners prefer scikit-learn are listed below.
Through scikit-learn, we can quickly implement many basic and advanced statistical and ML techniques. It provides efficient methods that accelerate your workflow with high accuracy.
Install Pandas on PyCharm:
pip install scikit-learn in Terminal
Or:
Look for the scikit-learn package in Python Packages
Use: import sklearn
DATASET EXPLORATION