Python for Artificial Intelligence: The Complete Guide

Core Libraries:

• NumPy: Numerical computing foundation
• Pandas: Data manipulation and analysis
• Matplotlib/Seaborn: Data visualization
• Scikit-learn: Traditional ML algorithms

Deep Learning Frameworks:

• TensorFlow: Google's production-grade framework
• PyTorch: Research favorite with dynamic graphs
• Keras: High-level API for rapid prototyping

Specialized Tools:

HuggingFace Transformers (NLP), OpenCV (computer vision), LangChain (LLM applications)

Complete ML Pipeline:

 
# Sample classification pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Load and prepare data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize and train model
model = RandomForestClassifier(n_estimators=100)
model.fit(X_train, y_train)

# Evaluate
predictions = model.predict(X_test)
print(f"Accuracy: {accuracy_score(y_test, predictions):.2f}")
        

Key Steps:

1. Data loading and cleaning (Pandas)
2. Feature engineering (NumPy, Scikit-learn)
3. Model training (Scikit-learn/TensorFlow)
4. Evaluation (Scikit-learn metrics)
5. Deployment (Flask/FastAPI)

Neural Network Implementation:

 
import torch.nn as nn

class NeuralNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(784, 256),
            nn.ReLU(),
            nn.Linear(256, 10)
    
    def forward(self, x):
        return self.layers(x)
            

 
from tensorflow.keras import layers

model = tf.keras.Sequential([
    layers.Dense(256, activation='relu', input_shape=(784,)),
    layers.Dense(10)
])
            

Training Process: