ml-fundamentals
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
npx skills add majiayu000/claude-skill-registry --skill ml-fundamentals --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.
# ML Fundamentals Skill > Master the building blocks of machine learning: from raw data to trained models. ## Quick Start ```python from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.pipeline import Pipeline from sklearn.ensemble import RandomForestClassifier # 1. Load and split data X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=42, stratify=y ) # 2. Create pipeline pipeline = Pipeline([ ('scaler', StandardScaler()), ('classifier', RandomForestClassifier(random_state=42)) ]) # 3. Train and evaluate pipeline.fit(X_train, y_train) score = pipeline.score(X_test, y_test) print(f"Accuracy: {score:.4f}") ``` ## Key Topics ### 1. Data Preprocessing | Step | Purpose | Implementation | |------|---------|----------------| | **Missing Values** | Handle NaN/None | `SimpleImputer(strategy='median')` | | **Scaling** | Normalize ranges | `StandardScaler()` or `MinMaxScaler()` | | **Encoding** | Convert categories | `OneHotEncoder()` or `LabelEncoder()` | | **Outliers** | Remove extremes | IQR method or Z-score | ```python from sklearn.compose import ColumnTransformer from sklearn.preprocessing
- Quick Start
- Key Topics
- 1. Data Preprocessing
- 2. Feature Engineering
- 3. Model Evaluation
- 4. Cross-Validation Strategies
- Best Practices
- DO
- DON'T
- Exercises
- Exercise 1: Basic Pipeline
- Exercise 2: Cross-Validation
- Unit Test Template
- Troubleshooting
What does the ml-fundamentals skill do?
Master machine learning foundations - algorithms, preprocessing, feature engineering, and evaluation
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill ml-fundamentals --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.