Reference library

ML engineering pipelines

Feature prep, batch inference, model-serving hooks, and production ML workflow glue.

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ML engineering pipelines medium

Train Logistic Regression From Scratch in Python

Trains a binary logistic regression model using gradient descent on mock data, printing learned weights and probabilities.

logistic-regression machine-learning gradient-descent
Python 
import numpy as np

# Mock data: 2 features, binary classification
X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
y = np.array([0, 0, 1, 1, 1])

# Add bias term (column of ones)
X_b = np.c_[np.ones((X.shape[0], 1)), X]

# Initialize parameters
theta = np.zeros(X_b.shape[1])

# Hyperparameters
learning_rate = 0…
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