1. What is Machine Learning? (ML)

Machine Learning is a modern technology that helps computers learn from data. This means a computer looks at many examples and slowly understands patterns on its own.

Why is ML important?

Because now:

Computers can recognize faces
Cars can drive automatically
Websites can recommend videos you like
Apps can translate languages instantly

Simple understanding

ML is like training a student:

Show many math problems → student learns pattern
Show many pictures → student learns differences

The more examples you give, the smarter the computer becomes.

Python Example (Simple ML Prediction)

from sklearn.neighbors import KNeighborsClassifier

X = [[1], [2], [3], [4], [5]]   # hours studied
y = [0, 0, 1, 1, 1]             # 0 = fail, 1 = pass

model = KNeighborsClassifier(n_neighbors=3)
model.fit(X, y)

print(model.predict([[3]]))

2. What is Artificial Intelligence (AI)?

Artificial Intelligence is a field in computer science that tries to make machines act like humans.

AI can:

Think
Learn
Plan
Speak
Understand images
Make decisions

Simple example

AI is used when:

Google Maps finds the fastest route
Siri answers your questions
Your camera detects your face

ML is a part of AI.

Python Example (Simple Rule-Based AI)

def smart_home(temp):
    if temp > 30:
        return "Turn ON AC"
    else:
        return "Temperature is OK"

print(smart_home(31))

3. ML vs AI vs Deep Learning

Concept	Meaning	Easy Example
AI	Smart machines	Self-driving car
ML	Learning from data	Car learns road signs
DL	ML + neural networks	Car detects people

Deep Learning

Deep Learning uses "neurons" like the human brain. This helps computers:

Detect faces
Translate languages
Understand speech

4. Types of Machine Learning

A. Supervised Learning

Computer learns from labeled data.

Example:

Image	Label
🍎	"apple"
🐶	"dog"

Computer learns the relationship.

Python Example

from sklearn.neighbors import KNeighborsClassifier

X = [[1],[2],[3],[4]]
y = [0,0,1,1]

model = KNeighborsClassifier()
model.fit(X, y)

print(model.predict([[3]]))

B. Unsupervised Learning

Computer learns without labels. It tries to group similar things together.

Simple example:

Group customers
Detect unusual activity in bank accounts

Python Example

from sklearn.cluster import KMeans

X = [[1], [2], [8], [9]]

kmeans = KMeans(n_clusters=2, n_init=10)
kmeans.fit(X)

print(kmeans.labels_)

C. Reinforcement Learning

Computer learns by trial and error and receives rewards.

Example:

Robot learning to walk
Game AI learning to win

Python Example

reward = 0
actions = ["left", "right"]

for step in range(6):
    action = actions[step % 2]
    if action == "right":
        reward += 1
    print("Action:", action, "Reward:", reward)

5. Python Basics for ML (VS Code Setup)

Step 1 — Install Python
From: https://python.org
Step 2 — Install VS Code
From: https://code.visualstudio.com
Step 3 — Install Python Extension
Search "Python" → Install
Step 4 — Create a Virtual Environment
python -m venv venv
Step 5 — Activate
Windows: venv\Scripts\activate
Step 6 — Install ML Libraries
pip install numpy pandas matplotlib scikit-learn

6. NumPy Basics

NumPy helps with:

Arrays
Math
Fast calculations

Python Example

import numpy as np

arr = np.array([10, 20, 30])
print("Mean:", arr.mean())
print("Max:", arr.max())

7. Pandas Basics

Pandas is used for table-like data.

Python Example

import pandas as pd

df = pd.DataFrame({
    "name": ["Ali", "Sara"],
    "age": [15, 16]
})

print(df)

8. Data Cleaning & Visualization

Cleaning data

import pandas as pd

df = pd.read_csv("students.csv")
df = df.dropna()
print(df.head())

Visualizing Data

import matplotlib.pyplot as plt

plt.plot([1,2,3],[2,4,6])
plt.title("Study Hours vs Marks")
plt.show()

9. First ML Model — Linear Regression

Linear Regression predicts numbers.

Python Example

import numpy as np
from sklearn.linear_model import LinearRegression

X = np.array([[1],[2],[3],[4]])
y = np.array([50, 60, 70, 80])

model = LinearRegression()
model.fit(X, y)

print("Prediction for 5 hours:", model.predict([[5]]))

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