Scikit-Learn || Python Tutorial || Learn Python Programming

Published: 20 August 2026
on channel: Socratica

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Scikit-Learn is a collection of tools that turn raw data into knowledge through machine learning.

In this tutorial we work through two complete classification projects. The classic 1936 Iris flower dataset: we scale the features, split into training and test sets, train a logistic regression model, and evaluate it with a confusion matrix. Then we look inside the model to see which measurements it actually relies on.

Next, handwritten digits: a simplified version of MNIST. We train a support vector machine on 8×8 grayscale images, dig into precision and recall, inspect the digits our model got wrong, and then improve everything with a Pipeline and GridSearchCV, using PCA to cut 64 dimensions down to 40 while keeping 95% of the variance.

Along the way we cover the design principles that make Scikit-Learn so pleasant to use: a consistent estimator interface, public parameters you can inspect, composable pipelines, and sensible defaults that let beginners get real results.

Chapters
00:00 Introduction
01:05 Why not just write if-statements?
01:36 Scikit-Learn's design principles
02:23 Project 1 — The Iris dataset
03:21 Normalizing data with StandardScaler
03:50 Training a logistic regression classifier
04:24 Reading the confusion matrix
05:43 Which features matter most?
06:07 Visualizing with a pair plot
07:09 Project 2 — Handwritten digits
07:26 64 pixels, 1,797 samples
09:21 Training a support vector machine
10:17 Precision, recall & the classification report
11:44 Tuning with grid search
12:07 Building a Pipeline with PCA
13:48 The winning parameters
14:55 Final thoughts

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