data-ai-guide
Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps. Use when building AI models, data pipelines, or machine learning systems.
npx skills add majiayu000/claude-skill-registry --skill data-ai --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data Science & AI Guide Master data science, machine learning, generative AI, and modern AI engineering practices. ## Quick Start ### Python Data Science Stack ```python import pandas as pd import numpy as np from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier # Load and prepare data df = pd.read_csv('data.csv') X = df.drop('target', axis=1) y = df['target'] # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Train model model = RandomForestClassifier(n_estimators=100) model.fit(X_train, y_train) # Evaluate accuracy = model.score(X_test, y_test) ``` ### Deep Learning with PyTorch ```python import torch import torch.nn as nn class SimpleNN(nn.Module): def __init__(self): super().__init__() self.linear1 = nn.Linear(784, 128) self.linear2 = nn.Linear(128, 10) def forward(self, x): x = torch.relu(self.linear1(x)) return self.linear2(x) # Training loop model = SimpleNN() optimizer = torch.optim.Adam(model.parameters()) criterion = nn.CrossEntropyLoss() ``` ### LLM Prompt Engineering ```python from openai import OpenAI client = OpenAI() response = client.chat.completions.create( model="gpt-4", mess
- Quick Start
- Python Data Science Stack
- Deep Learning with PyTorch
- LLM Prompt Engineering
- Data Science Path
- Fundamentals
- Machine Learning
- Deep Learning
- Natural Language Processing
- Generative AI & LLMs
- Large Language Models
- Prompt Engineering
- AI Agents
- Data Engineering
What does the data-ai-guide skill do?
Comprehensive data science, machine learning, and AI guide covering Python, deep learning, NLP, LLMs, prompt engineering, and MLOps. Use when building AI models, data pipelines, or machine learning systems.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill data-ai --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.