Learn SCIKIT-LEARN with Real Code Examples

Learn Python and NumPy basics

Understand ML concepts (supervised, unsupervised)

Explore estimators, transformers, pipelines

Practice model evaluation and selection

Integrate into real-world workflows

Week 1: regression and classification

Week 2: preprocessing and feature engineering

Week 3: model evaluation and cross-validation

Week 4: pipelines and ensemble methods

Week 5: deployment and integration with other libraries

What is an estimator in scikit-learn?

Explain the purpose of a pipeline

How do you perform cross-validation?

Difference between fit(), transform(), and predict()

How do you handle categorical data?

fit() = train model

predict() = make predictions

transform() = preprocess/modify data

Pipeline() = chain transformers + estimator

GridSearchCV = hyperparameter tuning

Introduction to Machine Learning with Python by Andreas Müller & Sarah Guido

Python Machine Learning by Sebastian Raschka

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Mastering Machine Learning with scikit-learn

Machine Learning Yearning by Andrew Ng

Official scikit-learn tutorials

Jupyter notebooks online

MOOCs like Python for Data Science

Community blog guides

Example projects on GitHub

https://scikit-learn.org/

https://scikit-learn.org/stable/documentation.html

https://github.com/scikit-learn/scikit-learn

Scikit-learn GitHub repository

Mailing lists and forums

StackOverflow

Reddit /r/MachineLearning

Tutorials and blog posts online

Scikit-learn GitHub repository

Mailing lists and forums

StackOverflow

Reddit /r/MachineLearning

Tutorials, MOOCs, and blog posts