# greyhatguy007/Machine-Learning-Specialization-Coursera

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Repository: https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera
Canonical: https://ross.abutalabs.com/products/machine-learning-specialization-coursera
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: andrew-ng, andrew-ng-machine-learning, coursera, coursera-assignment, coursera-specialization, deep-learning, linear-regression, logistic-regression, machine-learning, python, solutions, supervised-machine-learning, unsupervised-machine-learning, tensorflow, recommendation-system, unsupervised-learning, decision-trees, neural-network, mooc
Last push: 2025-12-28T12:57:37+00:00

## Health v2 (maintenance only)
Score: 59/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 59, release rhythm 35, longevity 100
- inputs: {"age_days": 1539, "days_push": 248, "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 7821, forks 3731 (observed 2026-08-28T04:10:05.269856+00:00)

## What it is
A collection of solutions and notes for the Machine Learning Specialization by Stanford University and DeepLearning.AI on Coursera, taught by Andrew Ng. It organizes quizzes, labs, and assignments as Jupyter notebooks covering regression, classification, neural networks, decision trees, unsupervised learning, and recommender systems.

## Use cases
- find solutions for Andrew Ng machine learning specialization assignments
- study notes for supervised machine learning course
- practice gradient descent and linear regression in Jupyter notebooks
- learn neural networks and decision trees with worked examples
- review unsupervised learning and recommender system labs
- prepare for Coursera machine learning quizzes

## When to choose
- you are enrolled in the Machine Learning Specialization and want reference solutions or notes
- you want hands-on Jupyter notebook examples of core ML concepts
- you prefer following Andrew Ng's curriculum with Python and TensorFlow

## When to avoid
- you need production machine learning code or a library
- you want to submit copied solutions without doing assignments yourself
- you need advanced or research-level ML material beyond the course scope

## Facets
- artifact type: learning-resource
- maturity: active
- function: machine-learning, deep-learning, data-science
- domain: machine-learning, deep-learning, tutorials, education
- platform: python
- tags: coursera, andrew-ng, jupyter-notebooks, course-solutions, mooc, study-notes

## Member repositories
- greyhatguy007/Machine-Learning-Specialization-Coursera (main) score 59

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:05.269856+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 2026-08-29T17:34:09.317266+00:00, confidence not recorded.
  - readme: https://github.com/greyhatguy007/Machine-Learning-Specialization-Coursera (fetched 2026-08-28T04:10:05.269856+00:00, sha dda11ae1b28f)
- Data as of 2026-08-30T08:39:29.467469+00:00.