Homemade Machine Learning Skill

Machine learning skill: find, explain, and implement ML algorithms with interactive Jupyter Notebook links. Covers linear regression, logistic regression, neural network, K-Means clustering, anomaly detection — with math, Python code, and skill demos.

Machine learning skill: learn, explain, and implement ML algorithms from scratch. Based on trekhleb/homemade-machine-learning (MIT, 22k+ ⭐)

📦 Install: clawhub install homemade-machine-learning-skill

5 algorithms · 11 interactive notebooks · math explained · Python code included

Commands

explain — 解释算法原理 + 数学 + 代码

bash scripts/ml-notebook-finder.sh explain "linear regression"
bash scripts/ml-notebook-finder.sh explain "neural network"
bash scripts/ml-notebook-finder.sh explain "kmeans"

notebook — 获取交互式 Jupyter Notebook 链接

bash scripts/ml-notebook-finder.sh notebook "logistic regression"
bash scripts/ml-notebook-finder.sh notebook "anomaly detection"

code — 获取 Python 实现代码片段

bash scripts/ml-notebook-finder.sh code "linear regression"
bash scripts/ml-notebook-finder.sh code "kmeans"

path — 生成学习路径（按难度排序）

bash scripts/ml-notebook-finder.sh path beginner
bash scripts/ml-notebook-finder.sh path intermediate
bash scripts/ml-notebook-finder.sh path advanced

list — 列出所有算法

bash scripts/ml-notebook-finder.sh list

Algorithms

Algorithm	Type	Notebooks	Use Case
linear regression	supervised	3	price prediction, forecasting
logistic regression	supervised	4	classification, MNIST
neural network (MLP)	supervised	2	image recognition, deep learning
k-means	unsupervised	1	clustering, segmentation
anomaly detection	unsupervised	1	fraud detection, monitoring

Source

MIT License — Original author: trekhleb Indexed by BytesAgain — AI skill discovery platform