Machine Learning Basics with Python

Machine Learning (ML) is a subset of AI that enables systems to learn from data without explicit programming. Python's Scikit-learn library is the most popular tool for classical ML.

Load and Explore Data

import pandas as pd
from sklearn.datasets import load_iris

# Load the Iris dataset
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['target'] = iris.target

print(df.head())
print(df.info())
print(df['target'].value_counts())

Train-Test Split

Always split data into training and testing sets to evaluate model performance on unseen data.

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

X = df.drop('target', axis=1)
y = df['target']

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

# Scale features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

Train a Classification Model

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report

# Train Random Forest
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train_scaled, y_train)

# Make predictions
y_pred = model.predict(X_test_scaled)

# Evaluate
print("Accuracy:", accuracy_score(y_test, y_pred))
print(classification_report(y_test, y_pred, target_names=iris.target_names))

Regression Example

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score

boston = load_boston()
X = pd.DataFrame(boston.data, columns=boston.feature_names)
y = boston.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

reg = LinearRegression()
reg.fit(X_train, y_train)

y_pred = reg.predict(X_test)
print("MSE:", mean_squared_error(y_test, y_pred))
print("R² Score:", r2_score(y_test, y_pred))

Summary

You built your first ML models: a classifier with Random Forest and a regressor with Linear Regression. Next, we'll explore deep learning with neural networks.