Machine Learning Skill

PURPOSE

ML model development, training, and deployment.

CORE COMPETENCIES

Algorithms:
├── Linear/Logistic Regression
├── Decision Trees, Random Forest
├── Gradient Boosting (XGBoost, LightGBM)
├── Neural Networks
└── Clustering (K-Means, DBSCAN)

Deep Learning:
├── CNNs (Computer Vision)
├── RNNs/LSTMs (Sequences)
├── Transformers (NLP)
├── Transfer Learning
└── Fine-tuning

MLOps:
├── Experiment tracking
├── Model registry
├── Feature stores
├── Model serving
└── Monitoring

CODE PATTERNS

Training Pipeline

import mlflow
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, f1_score

with mlflow.start_run():
    # Split data
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=42
    )

    # Train
    model = RandomForestClassifier(n_estimators=100)
    model.fit(X_train, y_train)

    # Evaluate
    predictions = model.predict(X_test)
    accuracy = accuracy_score(y_test, predictions)
    f1 = f1_score(y_test, predictions, average='weighted')

    # Log
    mlflow.log_params({"n_estimators": 100})
    mlflow.log_metrics({"accuracy": accuracy, "f1": f1})
    mlflow.sklearn.log_model(model, "model")

PyTorch Training Loop

for epoch in range(epochs):
    model.train()
    for batch in train_loader:
        optimizer.zero_grad()
        outputs = model(batch['input'])
        loss = criterion(outputs, batch['target'])
        loss.backward()
        optimizer.step()

    # Validation
    model.eval()
    with torch.no_grad():
        val_loss = evaluate(model, val_loader)

    scheduler.step(val_loss)

TROUBLESHOOTING

METRICS

Classification: Accuracy, Precision, Recall, F1, AUC-ROC
Regression: MSE, RMSE, MAE, R²
Ranking: MRR, NDCG
NLP: BLEU, ROUGE, Perplexity