HackerTube

THE VIDEO SHELF · intro machine learning

Hello World - Machine Learning Recipes #1

The first episode of Google's Machine Learning Recipes series. It introduces machine learning and builds a first supervised-learning classifier in about six lines of Python using scikit-learn.

Why HN readers recommended it

HN commenters recommend it as an approachable first step into machine learning for programmers, with an apples-vs-oranges classifier.

This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.

Video & recommendation details

Creator / channel
Google for Developers
Topic
intro machine learning
Format
tutorial
Level
intro
HN recommenders in dataset
6
HN mentions in dataset
6
Recommendation years in dataset
2016–2017

These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.

From the original HN conversations

Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.

  • This series by Josh Grodin got started recently and looks promising. I assume they will eventually cover neural network / deep learning basics. Hello World - Machine Learning Recipes #1 [VIDEO] Visualizing a Decision Tree - Machine Learning Recipes #2 [LINK]

    simulate on Hacker News · 2016-04
    In TensorFlow Simplified Interface Examples
  • Hello, my background is just like yours. Assuming you are a total noob I learned that AI is not like programming, where anyone with basic knowledge of computation can dive in. You can NOT just learn AI by reading other people's code. Its like Physics, …

    max_ on Hacker News · 2016-06
    In Ask HN: From programming to AI, how?
  • Honestly, it doesn't seem that complicated. Just watch this educational video series [VIDEO] by Google, and you'll be able to do cool stuff with machine learning.

    mliker on Hacker News · 2016-09
    In How a Japanese cucumber farmer is using deep learning and TensorFlow

Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.