explaingit

ageron/handson-ml

★ 25,616Jupyter NotebookAudience · developerComplexity · 2/5MaintainedLicenseSetup · moderate

TLDR

Jupyter Notebooks with machine learning code and exercises from the first edition of the O'Reilly book 'Hands-on Machine Learning.' Now deprecated; use the third edition instead.

Mindmap

mindmap
  root((repo))
    What it does
      Chapter-by-chapter notebooks
      ML fundamentals in Python
      Book exercise solutions
    How to run
      Google Colaboratory
      Binder online
      Local Anaconda setup
    Tech stack
      Python
      Scikit-Learn
      TensorFlow
      Jupyter Notebooks
    Audience
      Book readers
      ML learners
      Deprecated resource

Things people build with this

USE CASE 1

Work through the first edition of 'Hands-on Machine Learning' with runnable code examples.

USE CASE 2

Learn machine learning fundamentals in Python using Scikit-Learn and TensorFlow.

USE CASE 3

Run interactive notebooks online via Google Colaboratory or Binder without local setup.

Tech stack

PythonJupyter NotebookScikit-LearnTensorFlowAnaconda

Getting it running

Difficulty · moderate Time to first run · 30min

Requires Anaconda/conda environment setup and TensorFlow/Scikit-Learn installation before notebooks run.

Use freely for any purpose including commercial. Keep the notice and disclose changes to the patent grant.

In plain English

This repository contains the Jupyter Notebook code and exercise solutions from the first edition (2017) of the O'Reilly book "Hands-on Machine Learning with Scikit-Learn and TensorFlow." It is now deprecated, the README points readers to the third edition at a separate repository, and the first edition's code is outdated. The notebooks cover the fundamentals of machine learning in Python, working through concepts and examples from the book chapter by chapter. Jupyter Notebooks are interactive documents where you can read explanations, run code, and see results all in one place, they are a common format for data science and machine learning tutorials. The repo supports running online in Google Colaboratory or Binder without installing anything, or locally using Anaconda (a Python distribution for data science) with TensorFlow. You would only visit this repository if you are following along with the first edition of the book specifically. For anyone starting fresh, the README strongly recommends the third edition instead. The tech stack is Python, Scikit-Learn (a machine learning library), TensorFlow (a deep learning framework), and Jupyter Notebooks.

Copy-paste prompts

Prompt 1
Show me how to set up and run the first notebook from this repo in Google Colab.
Prompt 2
Walk me through the Scikit-Learn examples in these notebooks to understand classification.
Prompt 3
How do I use TensorFlow in these notebooks to build a simple neural network?
Prompt 4
Help me adapt one of these notebook exercises to work with my own dataset.
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