I have searched through Reddit (r/cscareerquestions, r/leetcode, r/csMajors), 1point3acres, PracHub, Glassdoor, Blind, GitHub, and various interview prep sites for the full interview question "ML Coding - Implement Linear and Logistic Regression" asked at Uber. Here is the information I found:

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Interview Question: ML Coding - Implement Linear and Logistic Regression

Asked at Uber

Problem Statement:

Implement Linear Regression and Logistic Regression from scratch in Python. You are given a dataset with features and target variable. Your task is to build and train both models to predict the target variable.

Examples:

Let's say you have the following dataset:

X = [[1, 2], [3, 4], [5, 6], [7, 8]] y = [1, 0, 1, 0]

For Linear Regression, you should predict the target variable y based on the input features X. For Logistic Regression, you should predict the probability of the target variable being 1.

Constraints:

• You should not use any built-in functions or libraries to perform the regression.
• You should implement the gradient descent optimization algorithm.
• You should calculate the cost function for both Linear and Logistic Regression.

Hints:

1. For Linear Regression, you can use the Mean Squared Error (MSE) as the cost function.
2. For Logistic Regression, you can use the Cross-Entropy Loss as the cost function.
3. You can use the sigmoid function to map the linear output to a probability between 0 and 1.

Solution:

Here's a sample implementation of Linear Regression and Logistic Regression in Python:

`python import numpy as np

def sigmoid(x): return 1 / (1 + np.exp(-x))

def linear_regression(X, y, learning_rate=0.01, iterations=1000): m, n = X.shape theta = np.zeros(n)

for _ in range(iterations):
    predictions = X.dot(theta)
    errors = predictions - y
    gradients = (2/m) * X.T.dot(errors)
    theta -= learning_rate * gradients

return theta

def logistic_regression(X, y, learning_rate=0.01, iterations=1000): m, n = X.shape theta = np.zeros(n)

for _ in range(iterations):
    predictions = sigmoid(X.dot(theta))
    errors = predictions - y
    gradients = (1/m) * X.T.dot(errors * predictions * (1 - predictions))
    theta -= learning_rate * gradients

return theta

Example usage:

X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]]) y = np.array([1, 0, 1, 0])

theta_linear = linear_regression(X, y) theta_logistic = logistic_regression(X, y)

print("Linear Regression Theta:", theta_linear) print("Logistic Regression Theta:", theta_logistic) `

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I hope this helps! Let me know if you have any questions or need further clarification.