How does a machine actually learn from data?

Source: Dev.to

🎯 The Correct Order (Beginner‑Optimal)

You should not fully learn scikit‑learn before understanding:

• what a model is
• what loss is
• what training means
• what overfitting is

Otherwise, scikit‑learn becomes a black box.

🧠 Think of scikit‑learn like this

Concepts → why something works
scikit‑learn → how to apply it quickly

If you reverse this order:

from sklearn.linear_model import LinearRegression

model = LinearRegression()
model.fit(X, y)

you can run the code, but you don’t actually know what happened.

✅ What You SHOULD Do Instead (Best Approach)

Step 1️⃣ – Learn learning concepts (no scikit‑learn yet)

Focus on the fundamentals:

• Supervised learning
• Regression vs. classification
• Model = function
• Loss function
• Overfitting vs. underfitting
• Train vs. test behavior

This can be done with math intuition + NumPy.

Step 2️⃣ – Implement Linear Regression from scratch

Use only:

• NumPy
• A few lines of math
• No ML libraries

This answers the question: “How does the model actually learn?”

Step 3️⃣ – THEN introduce scikit‑learn (lightly)

Once the concept clicks, scikit‑learn becomes:

• Clean
• Logical
• Easy

You’ll instantly understand:

• .fit()
• .predict()
• .score()

❌ What NOT to Do (Common Beginner Mistake)

• Deep dive into the scikit‑learn API
• Memorize classifiers and their parameters
• Jump to advanced models too early

These habits create a fragile understanding.

🧭 Minimal scikit‑learn You May Peek At (Optional)

It’s fine to recognize these utilities without mastering them yet:

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

(You’ve likely used them in previous projects.)
Don’t start learning full models until the earlier steps are solid.

Further Reading

• Understanding NumPy in the context of Python for Machine Learning
• The next basic concept of Machine Learning after NumPy: Pandas
• Understanding Data Preprocessing
• Beginner‑friendly exercises on NumPy, Pandas and Data Preprocessing