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Python Code Samples

Copy-ready Python snippets by topic and difficulty — short, focused, and runnable in the browser editor.

3 matches
ML engineering pipelines medium

Bayesian Optimization in Python: A Simplified Mock Implementation

A toy Bayesian optimization loop with a Gaussian process prior, expected improvement acquisition, and noisy sampling to find a function's minimum.

bayesian-optimization gaussian-process hyperparameter-tuning
Python 
import random
import math

class BayesianOptimizer:
    def __init__(self, noise=0.1):
        self.noise = noise
        self.observations = []
    
    def objective(self, x):
        return (math.sin(3*x) + 0.5*x) / (1 + x**2)
    
    def gaussian_process_prior(self, x1, x2, length_scale=0.5):
        return math.…
12 0 Open
ML engineering pipelines easy

Grid Search Hyperparameters in Python

Perform exhaustive grid search over hyperparameter combinations using itertools.product and a scoring function.

grid-search hyperparameters itertools
Python 
import itertools

def grid_search(param_grid, score_fn):
    """Perform exhaustive grid search over hyperparameter combinations."""
    keys = param_grid.keys()
    names = list(keys)
    values = [param_grid[name] for name in names]
    results = []

    for combination in itertools.product(*values):
        params =…
14 0 Open
ML engineering pipelines easy

How to Do Random Search for Hyperparameter Tuning in Python

A mock random search that samples hyperparameter combinations from a grid and ranks them by a dummy score, with a reproducible seed.

hyperparameter random-search ml
Python 
import random

# Mock random search over a small hyperparameter grid
param_grid = {
    "learning_rate": [0.001, 0.01, 0.1],
    "batch_size": [16, 32, 64],
    "num_layers": [1, 2, 3]
}

def random_search(grid, n_iter=5, seed=42):
    """Perform random search over a hyperparameter grid."""
    random.seed(seed)
    k…
13 0 Open

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PythonSkillset code samples are short, focused examples organised by topic and difficulty. Every snippet is server-rendered HTML — readable by search engines and easy to copy. Open any sample, read the notes, copy the code, then press Try in editor to run it in the browser with Pyodide.

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  2. Open a sample, read How it works, and copy the code block
  3. Run it in the IDE, tweak values, then take a related quiz or tutorial lesson

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Samples are quick reference — one concept per page. For step-by-step teaching, use our Python tutorials. To test yourself, try quizzes or coding challenges. Clean up style with the Python formatter.

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