Introduction to Machine Learning with Python

Machine Learning is transforming industries and creating new possibilities. Let's explore how to get started with ML using Python.

What is Machine Learning?

Machine Learning is a subset of AI that enables computers to learn and improve from experience without being explicitly programmed.

Types of Machine Learning:

• Supervised Learning: Learning with labeled data
• Unsupervised Learning: Finding patterns in unlabeled data
• Reinforcement Learning: Learning through trial and error

Essential Python Libraries

NumPy

Fundamental package for numerical computing:

import numpy as np

# Create arrays
arr = np.array([1, 2, 3, 4, 5])
matrix = np.array([[1, 2], [3, 4]])

# Mathematical operations
result = np.mean(arr)

Pandas

Data manipulation and analysis:

import pandas as pd

# Create DataFrame
df = pd.DataFrame(\{
    'name': ['Alice', 'Bob', 'Charlie'],
    'age': [25, 30, 35],
    'city': ['NYC', 'LA', 'Chicago']
\})

# Data exploration
print(df.info())
print(df.describe())

Scikit-learn

Machine learning library:

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

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

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

# Make predictions
predictions = model.predict(X_test)

Your First ML Project

Let's build a simple house price prediction model:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, r2_score
import matplotlib.pyplot as plt

# Load data
data = pd.read_csv('house_prices.csv')

# Explore data
print(data.head())
print(data.info())

# Prepare features
features = ['sqft', 'bedrooms', 'bathrooms', 'age']
X = data[features]
y = data['price']

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

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

# Make predictions
y_pred = model.predict(X_test)

# Evaluate model
mae = mean_absolute_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

print(f"Mean Absolute Error: \{mae\}")
print(f"R² Score: \{r2\}")

# Visualize results
plt.scatter(y_test, y_pred)
plt.xlabel('Actual Prices')
plt.ylabel('Predicted Prices')
plt.title('Actual vs Predicted Prices')
plt.show()

Data Preprocessing

Handling Missing Data

# Check for missing values
print(df.isnull().sum())

# Fill missing values
df['column'].fillna(df['column'].mean(), inplace=True)

# Drop rows with missing values
df.dropna(inplace=True)

Feature Scaling

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

Categorical Variables

# One-hot encoding
df_encoded = pd.get_dummies(df, columns=['category'])

# Label encoding
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['category_encoded'] = le.fit_transform(df['category'])

Model Evaluation

Cross-Validation

from sklearn.model_selection import cross_val_score

scores = cross_val_score(model, X, y, cv=5)
print(f"Average CV Score: \{scores.mean()\}")

Metrics for Different Problems

• Regression: MAE, MSE, R²
• Classification: Accuracy, Precision, Recall, F1-score

Next Steps

1. Explore more algorithms: Decision Trees, Random Forest, SVM
2. Learn feature engineering: Create better input features
3. Study deep learning: Neural networks with TensorFlow/PyTorch
4. Practice on real datasets: Kaggle competitions
5. Understand model interpretation: SHAP, LIME

Machine learning is a journey of continuous learning. Start with simple projects and gradually tackle more complex problems.