Machine Learning with Scikit-learn: From Basics to Preprocessing

About this path

This learning path is built from a YouTube playlist by CodersArts. It starts with an introduction to Scikit-learn, then covers key machine learning algorithms like linear regression, logistic regression, K-nearest neighbors, decision trees, and random forests. After that, it moves into essential data preprocessing techniques, including handling missing data, categorical and numerical values, scaling, normalization, and polynomial features. Each lesson includes code examples and practical explanations, so you can follow along and build your skills step by step.

What you will learn

• Understand the basics of Scikit-learn and its role in machine learning
• Implement linear and multiple linear regression models
• Apply logistic regression for classification tasks
• Use K-nearest neighbors, decision trees, and random forests
• Preprocess data by handling missing values, categorical data, and numerical features
• Apply standardization, normalization, and polynomial features

Who this is for

Beginners who want a structured free introduction before deeper practice.

Curriculum