5.1.1 Machine Learning Basics Roadmap: Task, Data, Model, Score

Machine learning starts when you stop hand-writing every rule and let a model learn patterns from data. The first habit is not algorithm memorization. It is a small project loop.

Look at the Map First

Keep this loop:

define task -> split data -> fit model -> predict -> score -> decide next step

Word	First meaning
feature	input column used by the model
label / target	answer the model learns to predict
train set	data used to learn
test set	data kept aside to check generalization
baseline	a simple first model used for comparison

Run the Smallest sklearn Loop

Create ml_first_loop.py and run it after installing scikit-learn.

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42, stratify=y
)

model = LogisticRegression(max_iter=200)
model.fit(X_train, y_train)
predictions = model.predict(X_test)

print("task: classification")
print("test_accuracy:", round(model.score(X_test, y_test), 3))
print("prediction_count:", len(predictions))

Expected output:

task: classification
test_accuracy: 0.967
prediction_count: 30

This is the smallest useful machine learning loop: split first, train only on the training set, evaluate on the test set.

Learn in This Order

Order	Read	What to practice
1	5.1.2 What Is Machine Learning?	task types, features, labels
2	5.1.3 Scikit-learn Introduction	fit, predict, score
3	5.1.4 How Math Flows Into ML	vectors, probability, loss, optimization
4	5.1.5 Machine Learning History	why major algorithms appeared
5	5.1.6 sklearn and Matplotlib Workshop	run, plot, explain a baseline

Pass Check

You pass this roadmap when you can name the task type, identify X and y, explain why train/test split matters, and keep one baseline score as evidence.