AI — Python

AI Libraries

Library	Description
NumPy	Numerical computing
Pandas	Data manipulation
Scikit-learn	Machine learning
TensorFlow	Deep learning
PyTorch	Deep learning
Keras	High-level API

Setup

pip install numpy pandas scikit-learn tensorflow

Basic ML Pipeline

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

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

# Split data
X_train, X_test, y_train, y_test = train_test_split(
    df.drop('target', axis=1), df['target']
)

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

# Evaluate
predictions = model.predict(X_test)
accuracy = accuracy_score(y_test, predictions)

Mini Practice

1. Set up Python environment
2. Install AI libraries
3. Build ML pipeline
4. Evaluate models

Up Next

Continue with TensorFlow — TensorFlow basics.