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mleveryday/100-days-of-ml-code

★ 22,250Jupyter NotebookAudience · vibe coderComplexity · 1/5DormantLicenseSetup · easy

TLDR

A 100-day structured learning challenge teaching machine learning basics through daily Jupyter Notebooks with Python code, infographics, and explanations in Chinese.

Mindmap

mindmap
  root((repo))
    What it does
      Daily ML lessons
      Runnable code
      Visual diagrams
    Topics covered
      Supervised learning
      Unsupervised learning
      Deep learning intro
    Learning format
      Jupyter Notebooks
      Python examples
      External resources
    Audience
      Chinese speakers
      ML beginners
      Self-paced learners

Things people build with this

USE CASE 1

Follow a structured 100-day plan to learn machine learning fundamentals from scratch with daily coding exercises.

USE CASE 2

Run Python code examples in Jupyter Notebooks to understand supervised learning algorithms like regression and classification.

USE CASE 3

Study unsupervised learning techniques such as clustering with working code and visual explanations.

USE CASE 4

Use infographics and daily lessons as a reference guide while building your first machine learning projects.

Tech stack

PythonJupyter NotebookNumPyPandasScikit-learn

Getting it running

Difficulty · easy Time to first run · 5min
Use freely for any purpose including commercial, as long as you keep the copyright notice.

In plain English

This repository is the Chinese-translation companion to the popular "100 Days of ML Code" challenge originally created by Avik-Jain. The README explains that this is the Chinese version of that English-language project, and points readers to the original repo for the English source and to a separate location for the datasets used in the exercises. The project's stated purpose is to learn machine learning a little at a time, day by day, for a hundred days. The way it works is that the repository is organised as a day-by-day index. Each day corresponds to a specific topic, and most days link out to either an implementation walk-through written in Markdown, an associated Jupyter notebook, or an infographic image summarising the day's lesson. The index groups topics into supervised learning (data preprocessing, simple linear regression, multiple linear regression, logistic regression, k-nearest neighbours, support vector machines, decision trees, random forests) and unsupervised learning (k-means clustering, hierarchical clustering). Interspersed with these are days dedicated to following along with outside resources: Coursera's deep-learning specialisation, Caltech's CS156 machine-learning course taught by Yaser Abu-Mostafa, Bloomberg's foundations of machine learning course, the 3Blue1Brown YouTube series on linear algebra and the essence of calculus, web scraping with Beautiful Soup, and JK VanderPlas' "Python Data Science Handbook" for a deeper look at NumPy. The implementation days use Python with the scikit-learn library; for example, the SVM day uses scikit-learn's SVC classifier on linearly-related data. The deep-learning sections introduce TensorFlow and Keras through linked video tutorials with accompanying Chinese-text notebooks. You would use this repo if you are a Chinese-speaking beginner who wants a structured, mostly self-paced path through classical machine learning and the first steps of deep learning, with each topic broken into bite-sized daily chunks and supported by infographics, code, and links to longer free courses. The bulk of the runnable content lives in Jupyter notebooks. The full README is longer than what was provided.

Copy-paste prompts

Prompt 1
I'm starting to learn machine learning. Walk me through Day 1 of this 100-day challenge and explain what linear regression does with a simple example.
Prompt 2
Show me how to run the Jupyter Notebook for k-nearest neighbors from this repo and explain what the code is doing step by step.
Prompt 3
I want to understand the difference between supervised and unsupervised learning. Use the examples from this 100-days-of-ml-code repo to explain both.
Prompt 4
Help me set up Jupyter Notebook and run the first week of Python code examples from this machine learning learning challenge.
Prompt 5
Explain what a decision tree is using the infographic and code example from this 100-day ML learning resource.
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