# tirthajyoti/Machine-Learning-with-Python

Practice and tutorial-style notebooks  covering wide variety of machine learning techniques

Repository: https://github.com/tirthajyoti/Machine-Learning-with-Python
Canonical: https://ross.abutalabs.com/products/tirthajyoti-machine-learning-with-python
Homepage: https://machine-learning-with-python.readthedocs.io/en/latest/
Language: Jupyter Notebook
License: BSD-2-Clause
License Family: permissive
Topics: numpy, statistics, pandas, matplotlib, regression, scikit-learn, classification, clustering, decision-trees, random-forest, dimensionality-reduction, neural-network, deep-learning, artificial-intelligence, data-science, machine-learning, k-nearest-neighbours, naive-bayes, pytest, flask
Last push: 2023-05-22T22:28:39+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": 3334, "days_push": 1199, "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 3325, forks 1831 (observed 2026-08-28T04:07:56.385144+00:00)

## What it is
A collection of practice and tutorial-style Jupyter notebooks covering a wide variety of machine learning techniques with Python. It includes notebooks on NumPy, Pandas, scikit-learn, regression, classification, clustering, neural networks, and deep learning.

## Use cases
- learn machine learning with python notebooks
- practice scikit-learn classification and regression
- tutorial on pandas and numpy operations
- examples of clustering and decision trees
- intro to neural networks with keras and tensorflow
- study machine learning algorithms hands-on

## When to choose
- you want hands-on notebook-based tutorials for ML fundamentals
- you are learning Python data science libraries like NumPy, Pandas, and scikit-learn
- you need example code for classic ML algorithms and deep learning basics

## When to avoid
- you need production-ready ML code or a maintained library
- you want a structured course with graded exercises rather than standalone notebooks
- you need up-to-date coverage of the latest deep learning frameworks

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, data-science, deep-learning, data-visualization
- domain: machine-learning, data-science, tutorials, artificial-intelligence
- platform: python, cross-platform
- tags: jupyter-notebooks, scikit-learn, pandas, numpy, tutorial, practice-notebooks

## Member repositories
- tirthajyoti/Machine-Learning-with-Python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:56.385144+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 2026-08-29T18:41:47.797794+00:00, confidence not recorded.
  - readme: https://github.com/tirthajyoti/Machine-Learning-with-Python (fetched 2026-08-28T04:07:56.385144+00:00, sha c86ca5a97df8)
- Data as of 2026-08-30T08:39:29.467469+00:00.