AI and Machine Learning Fundamentals

Understanding the basics of AI and Machine Learning is crucial for modern developers. Let’s explore the fundamental concepts and practical applications.

Basic Neural Network

import numpy as np

class NeuralNetwork:
    def __init__(self, layers):
        self.layers = layers
        self.weights = []
        self.biases = []
        
        for i in range(len(layers)-1):
            w = np.random.randn(layers[i], layers[i+1])
            b = np.zeros((1, layers[i+1]))
            self.weights.append(w)
            self.biases.append(b)

Common ML Algorithms

1. Linear Regression
2. Decision Trees
3. Random Forests
4. Support Vector Machines

Data Preprocessing

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

Model Evaluation

from sklearn.metrics import accuracy_score, precision_score

def evaluate_model(y_true, y_pred):
    accuracy = accuracy_score(y_true, y_pred)
    precision = precision_score(y_true, y_pred)
    return accuracy, precision

Key concepts to understand:

• Supervised vs Unsupervised Learning
• Training and Testing
• Overfitting and Underfitting
• Model Selection