🔹 int → Whole numbers

age = 25 

Real-life analogy

Counting cricket runs: 1, 2, 4, 6.

ML use-case

Storing epochs, batch size, etc.

🔹 float → Decimal numbers

learning_rate = 0.001 

Real-life analogy

Measuring petrol in liters: 1.5 L.

ML use-case

Model metrics: accuracy = 92.57, loss = 0.024

🔹 bool → True/False

is_training = True 

Real-life analogy

Switch ON/OFF.

ML use-case

Checking if model is in training or evaluation mode.

🔹 str → Text

name = "Ali" 

Real-life analogy

Your WhatsApp message.

ML use-case

Text datasets for NLP models.

🚀 2. List → Ordered, changeable (mutable), allows duplicates

fruits = ["apple", "banana", "mango"] 

✔ Properties

• Ordered
• Mutable (you can change elements)
• Allows duplicates

✔ Real-life analogy

A shopping list → you can rearrange, add, remove items.

✔ Operations

fruits.append("orange") fruits[1] = "grapes" print(fruits) 

✔ ML use-case

Storing multiple predictions:

preds = [0.1, 0.4, 0.8, 0.3] 

🚀 3. Tuple → Ordered, NOT changeable (immutable), allows duplicates

point = (3, 5) 

✔ Properties

• Ordered
• Immutable
• Fast
• Allows duplicates

✔ Real-life analogy

Your Aadhaar details — once fixed, cannot modify easily.

✔ ML use-case

Coordinates in feature space (x, y), image pixel shape:

shape = (224, 224, 3) 

✔ Why use tuple?

• Faster than list
• Safe from accidental modification

🚀 4. Set → Unordered, unique elements only, no duplicates

unique_ids = {101, 102, 103} 

✔ Properties

• Unordered
• Only unique items
• Super fast for membership test (in)

✔ Real-life analogy

Your Instagram followers list automatically removes duplicates.

✔ ML use-case

Getting unique labels/class names:

labels = set(["cat", "dog", "cat", "mouse"]) 

Output:

{'cat', 'dog', 'mouse'} 

🚀 5. Dictionary (dict) → Key–value pairs

student = {"name": "Ali", "age": 22} 

✔ Properties

• Key–value mapping
• Fast lookup
• Ordered (Python 3.7+)

✔ Real-life analogy

A contact list:

• “Maa” → 9876543210
• “Papa” → 1234567890

✔ Operations

student["age"] = 23 student["city"] = "Nashik" 

✔ ML use-case

Hyperparameters:

config = { "learning_rate": 0.001, "batch_size": 32, "optimizer": "adam" } 

🔥 Side-by-side summary table

🧠 Mini Exercises (Try these)

Q1

Create a list of 5 movies and replace the 3rd one.

Q2

Convert this list to a set:

nums = [1, 2, 2, 3, 4, 4, 5] 

Q3

Create a dictionary for a model config with keys:

• model_name
• epochs
• lr
• optimizer

Q4

Why would you use a tuple instead of a list?

Source: DEV Community.