# dipanjanS/practical-machine-learning-with-python

Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

Repository: https://github.com/dipanjanS/practical-machine-learning-with-python
Canonical: https://ross.abutalabs.com/products/practical-machine-learning-with-python
Language: Jupyter Notebook
License: Apache-2.0
License Family: permissive
Topics: machine-learning, deep-learning, python, classification, clustering, natural-language-processing, computer-vision, spacy, nltk, scikit-learn, prophet, time-series-analysis, convolutional-neural-networks, tensorflow, keras, statsmodels, pandas, jupyter, notebook, jupyter-notebook
Last push: 2024-03-31T20:24:30+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": 3322, "days_push": 885, "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 2385, forks 1653 (observed 2026-08-28T04:06:42.578803+00:00)

## What it is
A companion repository for the Apress book 'Practical Machine Learning with Python', containing all code, Jupyter notebooks, and examples from the book. It teaches machine learning and deep learning through real-world case studies using the Python ML ecosystem (scikit-learn, TensorFlow, Keras, NLTK, spaCy, pandas).

## Use cases
- learn machine learning with python from scratch
- jupyter notebooks for deep learning examples
- hands-on NLP tutorials with nltk and spacy
- time series forecasting examples with prophet
- image classification with CNNs in keras
- study real-world ML case studies
- practice clustering and classification in scikit-learn

## When to choose
- you want structured, book-backed learning material with runnable notebooks
- you prefer learning ML through real-world case studies rather than theory
- you want coverage spanning classical ML, deep learning, NLP, and computer vision in Python

## When to avoid
- you need a production-ready ML library or framework rather than educational material
- you want actively updated content for the latest framework versions
- you need a quick reference rather than a full course-style resource

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning, nlp, computer-vision, data-science
- domain: machine-learning, deep-learning, computer-vision, data-science, tutorials
- platform: python, cross-platform
- tags: jupyter-notebooks, book-companion, scikit-learn, tensorflow, keras, nltk, spacy, time-series-analysis, hands-on-examples, natural-language-processing

## Member repositories
- dipanjanS/practical-machine-learning-with-python (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:42.578803+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 2026-08-30T02:34:38.429757+00:00, confidence not recorded.
  - readme: https://github.com/dipanjanS/practical-machine-learning-with-python (fetched 2026-08-28T04:06:42.578803+00:00, sha df97bdb5e78e)
- Data as of 2026-08-30T08:39:29.467469+00:00.