scikit-learn is one of the easiest Python libraries for getting started with machine learning.

It is best known for classic machine learning rather than deep learning.

Typical use cases:

• classification
• regression
• clustering
• preprocessing
• model evaluation

Common beginner ideas:

• train_test_split(...): split data into training and test sets
• fit(...): train a model
• predict(...): generate predictions
• score(...): quick accuracy or model score check

Simple example:

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

model = LogisticRegression()

Very small mental model:

1. Prepare a table of features and labels.
2. Split the data.
3. Train a model with fit.
4. Predict with predict.
5. Evaluate how well it worked.

Why people use scikit-learn:

• simple API
• great for beginners
• strong set of classical ML algorithms
• useful utilities for preprocessing and evaluation

Good first goal:

Train a basic classification model on tabular data before moving on to more advanced model types.