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Welcome to the iwantcoding.com Machine Learning Tutorial. Machine learning = algorithms that improve from data. Most production ML is supervised: x → y from labelled examples. This track teaches the practical playbook, not the maths-only theory.
What this tutorial covers
| Chapter | You will learn |
|---|---|
| ML Basics | Types of ML, end-to-end workflow, data splits, feature engineering, scaling & encoding, metrics, bias / variance, over/underfitting. |
| Classical Models | Linear & logistic regression, KNN, naive Bayes, SVM, trees, random forest, GBDT, K-Means, PCA. |
| Practical ML | scikit-learn API, pipelines, cross-validation, hyperparameter tuning, imbalance, explainability (SHAP), persistence, MLOps basics. |
| Examples | Cheatsheet, runnable snippets, quiz, exercises, bootcamp, certificate. |
Who this is for
- Backend / data devs adding ML features.
- Career-switchers entering data science.
- Engineers preparing for ML interviews.
How to use this tutorial: read the chapter, run the example with Try it Yourself », do the exercise, then take the quiz at the bottom. Hit Mark complete when you're done — the sidebar will track your progress.
Example
Example
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
X, y = load_iris(return_X_y=True)
Xtr, Xte, ytr, yte = train_test_split(X, y, random_state=0)
clf = LogisticRegression(max_iter=200).fit(Xtr, ytr)
print('accuracy:', clf.score(Xte, yte))
Try it Yourself »
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