# ZuzooVn/machine-learning-for-software-engineers

A complete daily plan for studying to become a machine learning engineer.

Repository: https://github.com/ZuzooVn/machine-learning-for-software-engineers
Canonical: https://ross.abutalabs.com/products/machine-learning-for-software-engineers
Homepage: https://www.codementor.io/zuzoovn/how-i-plan-to-become-a-machine-learning-engineer-a4metbcuk
License: CC-BY-SA-4.0
License Family: other
Topics: machine-learning, deep-learning, artificial-intelligence, software-engineer, machine-learning-algorithms
Last push: 2024-06-11T04:49:29+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3615, "days_push": 813, "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 28864, forks 6146 (observed 2026-08-28T04:11:52.917454+00:00)

## What it is
A curated, multi-month study plan that guides software engineers from a programming background to machine learning engineering using a top-down, hands-on approach. It aggregates books, MOOCs, video series, Kaggle competitions, and a daily schedule rather than providing any code.

## Use cases
- become a machine learning engineer without a CS degree
- find a structured daily study plan for machine learning
- learn ML top-down with minimal math upfront
- transition from software developer to ML engineer
- find books, courses, and Kaggle practice resources for ML
- self-study machine learning curriculum

## When to choose
- you are a self-taught or working software engineer wanting a structured path into ML
- you prefer a results-first, hands-on approach over math-heavy theory
- you want a curated list of free courses, books, and practice competitions

## When to avoid
- you need an ML library, framework, or runnable code
- you want a rigorous bottom-up mathematical foundation first
- you need an up-to-date resource - the plan is largely maintained rather than actively expanded

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, developer-tools
- domain: machine-learning, artificial-intelligence, tutorials, education
- platform: cross-platform
- tags: study-plan, curriculum, self-learning, career-change, awesome-list

## Member repositories
- ZuzooVn/machine-learning-for-software-engineers (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:11:52.917454+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 2026-08-29T16:53:16.984852+00:00, confidence not recorded.
  - readme: https://github.com/ZuzooVn/machine-learning-for-software-engineers (fetched 2026-08-28T04:11:52.917454+00:00, sha 47161beb0c5b)
  - homepage: https://www.codementor.io/zuzoovn/how-i-plan-to-become-a-machine-learning-engineer-a4metbcuk (fetched 2026-08-29T07:50:49.307126+00:00, sha 44136fa355b3)
- Data as of 2026-08-30T08:39:29.467469+00:00.