# devAmoghS/Machine-Learning-with-Python

Small scale machine learning projects to understand the core concepts . Give a Star 🌟If it helps you. BONUS: Interview Bank coming up..!

Repository: https://github.com/devAmoghS/Machine-Learning-with-Python
Canonical: https://ross.abutalabs.com/products/devamoghs-machine-learning-with-python
Language: Python
License: MIT
License Family: permissive
Topics: machine-learning, python, exercises, practice-project, beginner-friendly, scikit-learn, deep-learning, python-3, data-science
Last push: 2026-04-14T23:31:42+00:00

## Health v2 (maintenance only)
Score: 67/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 77, release rhythm 35, longevity 100
- inputs: {"age_days": 3106, "days_push": 141, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1332, forks 204 (observed 2026-08-28T04:04:24.469353+00:00)

## What it is
A collection of small-scale machine learning projects in Python designed to teach core ML concepts, covering regression, classification, clustering, neural networks, topic modeling, and statistics. It is a beginner-friendly educational repository built with Scikit-Learn, Keras, and Python 3.

## Use cases
- learn machine learning fundamentals through hands-on python projects
- practice implementing algorithms like random forest and k-means from scratch
- find beginner machine learning project examples with scikit-learn
- understand linear and logistic regression with simple datasets
- study neural network examples using keras on mnist
- prepare for machine learning interviews with practice exercises
- learn statistics concepts like a/b testing and bayesian inference in python

## When to choose
- you are a beginner wanting small, readable projects to learn ML concepts
- you want to see algorithms implemented from scratch in Python
- you need practice exercises covering classic ML topics like regression, clustering, and NLP

## When to avoid
- you need production-ready, well-maintained ML libraries or pipelines
- you require up-to-date code compatible with the latest Python and library versions
- you want large-scale or deep-learning-focused projects beyond toy examples

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, nlp, deep-learning
- domain: machine-learning, data-science, tutorials, education
- platform: python
- tags: beginner-friendly, scikit-learn, keras, practice-projects, exercises, interview-prep

## Member repositories
- devAmoghS/Machine-Learning-with-Python (main) score 67

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:24.469353+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:45:11.221969+00:00, confidence not recorded.
  - readme: https://github.com/devAmoghS/Machine-Learning-with-Python (fetched 2026-08-28T04:04:24.469353+00:00, sha 78081fd1c941)
- Data as of 2026-08-30T08:39:29.467469+00:00.