Ad – 728×90
📊 Data Science

Machine Learning with Python – Introduction and First Model

Machine learning (ML) is teaching computers to learn from data without being explicitly programmed. Python — through libraries like scikit-learn, TensorFlow, and PyTorch — is the dominant language for ML. This lesson introduces core ML concepts and walks you through building your first real model.

⏱️ 30 min read🎯 Advanced📅 Updated 2026

Key Machine Learning Concepts

Supervised learning: learn from labelled examples (classification, regression). Unsupervised learning: find patterns in unlabelled data (clustering, dimensionality reduction). Reinforcement learning: learn from rewards and penalties.

Python
# The ML workflow:
# 1. Collect and prepare data
# 2. Split into training and test sets
# 3. Choose and train a model
# 4. Evaluate on test set
# 5. Tune and improve
# 6. Deploy

print("Data → Model → Predictions")

scikit-learn – Python's ML Toolkit

scikit-learn provides consistent APIs for 50+ ML algorithms, plus data preprocessing, model evaluation, and pipelines.

Python
# pip install scikit-learn
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score

# Load example dataset
iris = load_iris()
X, y = iris.data, iris.target

print(f"Samples: {len(X)}, Features: {X.shape[1]}")
print(f"Classes: {iris.target_names}")
▶ Output
Samples: 150, Features: 4 Classes: ['setosa' 'versicolor' 'virginica']

Training Your First Model

Split data, train a classifier, and evaluate it.

Python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score, classification_report

iris = load_iris()
X, y = iris.data, iris.target

# Split: 80% train, 20% test
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=42
)

# Train
model = DecisionTreeClassifier(max_depth=3, random_state=42)
model.fit(X_train, y_train)

# Evaluate
y_pred = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, y_pred):.2%}")
▶ Output
Accuracy: 96.67%
Ad – 336×280

Linear Regression Example

Predict continuous values (price, temperature, salary) with linear regression.

Python
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, r2_score

# Synthetic house price data
np.random.seed(42)
sqft = np.random.randint(500, 3000, 200).reshape(-1, 1)
price = sqft * 250 + np.random.normal(0, 15000, (200,1))

X_train, X_test, y_train, y_test = train_test_split(sqft, price, test_size=0.2)

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

y_pred = model.predict(X_test)
print(f"R² Score: {r2_score(y_test, y_pred):.3f}")
print(f"Predict 2000 sqft: ${model.predict([[2000]])[0][0]:,.0f}")
▶ Output
R² Score: 0.985 Predict 2000 sqft: $500,234
Ad – 728×90