Variables

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1. What is a Variable?

A variable is like a labeled box that stores data in memory. In ML, variables help us manage:

• Data (features, labels)

• Hyperparameters (learning rate, epochs)

• Results (accuracy, loss)

👉 Think: variable = value

x = 5
name = "Mr Nerd"
price = 19.99
is_active = True

Notes:

• x → integer (int)

• name → string (str)

• price → float (float)

• is_active → Boolean (bool)

• Python auto-detects types (no need to declare).

2. Naming Variables (Very Important in ML!)

Python allows flexible names, but ML projects need clarity.

Valid:

data = [1, 2, 3]
learning_rate = 0.01
model_name = "LinearRegression"

Invalid:

1model = "wrong"   # Cannot start with a number
learning-rate = 0.01  # Hyphen not allowed

Best Practices (PEP8 style):

• Use lowercase + underscores → train_data, test_accuracy

• Be descriptive → input_vector instead of iv

• Avoid keywords (class, def, etc.)

3. Updating Variables

Variables can be updated anytime.

accuracy = 0.85
accuracy = accuracy + 0.05  # Now 0.90

# Shortcut
accuracy += 0.05  # same result

4. Common Data Types in ML

integer_example = 10           # int
float_example = 3.14           # float
string_example = "NeuralNet"   # str
boolean_example = False        # bool
list_example = [1, 2, 3]       # list
tuple_example = (1, 2)         # tuple
dict_example = {"lr": 0.01}    # dict

ML Use Cases:

• int, float → parameters (epochs, learning rate)

• str → model names, labels

• list, tuple → feature vectors

• dict → configs, hyperparameters

5. Practical Use in ML

features = [5.1, 3.5, 1.4, 0.2]   # input features
label = "Iris-setosa"            # target label
params = {"learning_rate": 0.01, "epochs": 100}  # config

6. Multiple Assignments

You can assign multiple values at once:

x, y, z = 1, 2, 3

# Example in ML
train_data, test_data = [1, 2], [3, 4]

7. Constants

Python doesn’t enforce constants, but conventionally:

LEARNING_RATE = 0.01
EPOCHS = 100

👉 Uppercase = do not change.

8. Type Checking (Debugging in ML)

data = [1, 2, 3]
print(type(data))  # <class 'list'>

Helps avoid type errors in training code.

9. Variable Scope

Variables inside functions are local, outside are global.

score = 90  # global

def update_score():
    score = 100  # local
    print("Inside function:", score)

update_score()
print("Outside function:", score)

Output:

Inside function: 100
Outside function: 90

10. Dynamic Typing

Python allows changing variable types:

x = 10      # int
x = "hi"    # str

⚠️ Be careful in ML — type mix-ups cause bugs.

11. Variables in NumPy

NumPy is essential in ML.

import numpy as np

x = np.array([1, 2, 3])
w = np.array([0.4, 0.3, 0.2])
bias = 0.5

y = np.dot(x, w) + bias
print("Output:", y)

Output:

Output: 2.1

12. Summary Table

Feature	Example	Notes
Basic variable	x = 5	Auto-detects type
Multiple assign	x, y = 1, 2	Split values
Type check	type(x)	Debugging help
Update value	x += 1	Shortcut
Store params	params = {"lr": 0.01}	Common in ML configs
Constants	EPOCHS = 100	Use ALL_CAPS

13. Video Tutorial

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