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Machine Learning From Scratch
A Machine Learning From Scratch topic hub for K-means, Iris, K-means++, SSE, feature engineering, training, and evaluation.

Machine Learning From Scratch

Topic hub

Machine Learning From Scratch Tutorials

A project-based hub for K-means, the Iris dataset, K-means++, SSE, feature engineering, model training, and evaluation.

Covers long-tail searches around K-means in C, Iris clustering tutorials, machine learning workflow, feature engineering, and evaluation.

Open K-means playground Open resources Copy share links

What you will build

You will follow a reproducible ML workflow: inspect data, process features, train models, evaluate output, and study K-means from scratch.

  • K-means from scratch in C
  • Iris clustering tutorial
  • K-means++ initialization
  • machine learning workflow tutorial

Recommended reading order

Start with concepts, then move into runnable projects

Machine Learning Workflow

Follow the practical path from data and features to training, prediction, and evaluation.

Level: Beginner Reading time: 9 min
  • Machine Learning
  • Features
  • scikit-learn

Model Training and Evaluation

Understand loss, overfitting, train/test splits, accuracy, recall, and F1.

Level: Beginner Reading time: 9 min
  • Model Training
  • Metrics
  • Evaluation

Python AI Mini Practice

Run a small scikit-learn classification task and read the experiment output.

Level: Practice Reading time: 10 min
  • Python
  • scikit-learn
  • Classification

K-means clustering on the Iris dataset in C

Study standardization, K-means++ initialization, restart selection, SSE, and the final clustering result.

Level: Beginner Reading time: 10 min
  • C
  • Python
  • Backtracking
  • K-means

Resources and distribution assets

Code, data, diagrams, and share assets in one place

FAQ

Direct answers to common search questions

Should I study K-means or the ML workflow first?

If you are starting out, read the ML workflow first. If you already understand training and evaluation, jump into K-means in C.

Are the dataset and source code downloadable?

Yes. The resources include Iris.csv, C source, flowchart, visualization SVG, and a zip bundle.