🐘 🤖 TuskLang PHP Machine Learning Guide
🤖 TuskLang PHP Machine Learning Guide
"We don't bow to any king" - PHP Edition
Master TuskLang machine learning in PHP! This guide covers ML integration, model training, prediction, and AI patterns that will make your applications intelligent, adaptive, and data-driven.
🎯 Machine Learning Overview
TuskLang provides sophisticated machine learning features that transform your applications into intelligent, self-learning systems. This guide shows you how to implement enterprise-grade ML while maintaining TuskLang's power.
<?php
// config/ml-overview.tsk
[ml_features]
model_training: @ml.model.train(@request.training_config)
prediction_engine: @ml.prediction.engine(@request.prediction_config)
feature_engineering: @ml.feature.engineer(@request.feature_config)
ai_integration: @ml.ai.integrate(@request.ai_config)🧠 Model Training and Management
Basic Model Training
<?php
// config/ml-model-training-basic.tsk
[model_training]
Basic model training configuration
training_config: @ml.training.configure({
"model_type": "classification",
"algorithm": "random_forest",
"dataset": "user_behavior.csv",
"features": ["age", "income", "location", "browsing_history"],
"target": "purchase_likelihood"
})[training_parameters]
Training parameters
training_params: @ml.training.parameters({
"test_size": 0.2,
"random_state": 42,
"n_estimators": 100,
"max_depth": 10,
"cross_validation": 5
})[model_evaluation]
Model evaluation
model_evaluation: @ml.evaluation.metrics({
"accuracy": true,
"precision": true,
"recall": true,
"f1_score": true,
"roc_auc": true
})Advanced Model Training
<?php
// config/ml-model-training-advanced.tsk
[advanced_training]
Advanced model training
hyperparameter_tuning: @ml.training.hyperparameter({
"method": "grid_search",
"parameters": {
"n_estimators": [50, 100, 200],
"max_depth": [5, 10, 15],
"min_samples_split": [2, 5, 10]
},
"cv_folds": 5
})[ensemble_learning]
Ensemble learning
ensemble_models: @ml.training.ensemble({
"models": [
{"type": "random_forest", "weight": 0.4},
{"type": "gradient_boosting", "weight": 0.3},
{"type": "neural_network", "weight": 0.3}
],
"voting_method": "soft"
})[auto_ml]
AutoML configuration
auto_ml: @ml.training.automl({
"enabled": true,
"time_limit": 3600,
"algorithms": ["random_forest", "xgboost", "lightgbm", "neural_network"],
"feature_selection": true,
"feature_engineering": true
})🔮 Prediction and Inference
Real-Time Prediction
<?php
// config/ml-prediction.tsk
[prediction_engine]
Prediction engine configuration
prediction_config: @ml.prediction.configure({
"model_path": "models/user_behavior_model.pkl",
"preprocessing_pipeline": "models/preprocessor.pkl",
"prediction_threshold": 0.5,
"batch_size": 100
})[real_time_prediction]
Real-time prediction
real_time_prediction: @ml.prediction.realtime({
"endpoint": "/api/predict",
"input_schema": {
"user_id": "integer",
"age": "integer",
"income": "float",
"location": "string",
"browsing_history": "array"
},
"output_schema": {
"prediction": "float",
"confidence": "float",
"recommendations": "array"
}
})[batch_prediction]
Batch prediction
batch_prediction: @ml.prediction.batch({
"input_file": "data/predictions_input.csv",
"output_file": "data/predictions_output.csv",
"batch_size": 1000,
"parallel_processing": true
})Model Serving
<?php
// config/ml-model-serving.tsk
[model_serving]
Model serving configuration
model_server: @ml.serving.server({
"host": "0.0.0.0",
"port": 8080,
"models": {
"user_behavior": "models/user_behavior_model.pkl",
"recommendation": "models/recommendation_model.pkl",
"fraud_detection": "models/fraud_detection_model.pkl"
}
})[model_versioning]
Model versioning
model_versioning: @ml.serving.versioning({
"version_control": true,
"model_registry": "models/registry/",
"rollback_enabled": true,
"a_b_testing": true
})[model_monitoring]
Model monitoring
model_monitoring: @ml.serving.monitoring({
"prediction_drift": true,
"data_drift": true,
"model_performance": true,
"alert_threshold": 0.1
})🔧 Feature Engineering
Data Preprocessing
<?php
// config/ml-feature-engineering.tsk
[feature_engineering]
Feature engineering configuration
preprocessing_pipeline: @ml.feature.preprocessing({
"missing_value_handling": "impute",
"categorical_encoding": "one_hot",
"numerical_scaling": "standard",
"feature_selection": "mutual_info"
})[data_cleaning]
Data cleaning
data_cleaning: @ml.feature.cleaning({
"outlier_detection": "isolation_forest",
"duplicate_removal": true,
"noise_reduction": "smoothing",
"data_validation": true
})[feature_extraction]
Feature extraction
feature_extraction: @ml.feature.extraction({
"text_features": {
"tfidf": true,
"word_embeddings": "word2vec",
"sentiment_analysis": true
},
"temporal_features": {
"seasonality": true,
"trends": true,
"cyclical_features": true
}
})Advanced Feature Engineering
<?php
// config/ml-feature-engineering-advanced.tsk
[advanced_features]
Advanced feature engineering
domain_features: @ml.feature.domain({
"business_rules": [
"user_engagement_score = (page_views 0.3) + (time_spent 0.7)",
"purchase_probability = (browsing_frequency 0.4) + (cart_adds 0.6)"
],
"interaction_features": true,
"aggregation_features": true
})[feature_selection]
Feature selection
feature_selection: @ml.feature.selection({
"methods": ["mutual_info", "chi_square", "recursive_elimination"],
"threshold": 0.01,
"max_features": 50,
"correlation_threshold": 0.8
})🎯 AI Integration Patterns
Recommendation Systems
<?php
// config/ml-recommendation-systems.tsk
[recommendation_system]
Recommendation system configuration
collaborative_filtering: @ml.recommendation.collaborative({
"algorithm": "matrix_factorization",
"similarity_metric": "cosine",
"neighborhood_size": 50,
"min_ratings": 5
})[content_based_filtering]
Content-based filtering
content_based: @ml.recommendation.content({
"feature_extraction": "tfidf",
"similarity_metric": "cosine",
"content_types": ["text", "tags", "categories"]
})[hybrid_recommendation]
Hybrid recommendation
hybrid_recommendation: @ml.recommendation.hybrid({
"collaborative_weight": 0.6,
"content_weight": 0.4,
"ensemble_method": "weighted_average"
})Natural Language Processing
<?php
// config/ml-nlp.tsk
[nlp_models]
NLP models configuration
text_classification: @ml.nlp.classification({
"model": "bert",
"task": "sentiment_analysis",
"labels": ["positive", "negative", "neutral"],
"fine_tuning": true
})[named_entity_recognition]
Named entity recognition
ner_model: @ml.nlp.ner({
"model": "spacy",
"entities": ["person", "organization", "location", "date"],
"confidence_threshold": 0.8
})[text_generation]
Text generation
text_generation: @ml.nlp.generation({
"model": "gpt2",
"max_length": 100,
"temperature": 0.7,
"top_p": 0.9
})Computer Vision
<?php
// config/ml-computer-vision.tsk
[computer_vision]
Computer vision configuration
image_classification: @ml.vision.classification({
"model": "resnet50",
"classes": ["cat", "dog", "bird", "fish"],
"input_size": [224, 224],
"preprocessing": "imagenet"
})[object_detection]
Object detection
object_detection: @ml.vision.detection({
"model": "yolo",
"confidence_threshold": 0.5,
"nms_threshold": 0.4,
"max_detections": 100
})[image_segmentation]
Image segmentation
image_segmentation: @ml.vision.segmentation({
"model": "unet",
"classes": ["background", "person", "car", "building"],
"output_format": "mask"
})📊 Model Performance and Monitoring
Performance Metrics
<?php
// config/ml-performance-monitoring.tsk
[performance_monitoring]
Performance monitoring
model_performance: @ml.monitoring.performance({
"metrics": ["accuracy", "precision", "recall", "f1", "auc"],
"tracking_frequency": "daily",
"alert_threshold": 0.05
})[data_drift_detection]
Data drift detection
data_drift: @ml.monitoring.drift({
"detection_method": "ks_test",
"features": ["age", "income", "location"],
"drift_threshold": 0.05,
"alert_enabled": true
})[model_drift_detection]
Model drift detection
model_drift: @ml.monitoring.model_drift({
"detection_method": "prediction_drift",
"window_size": 1000,
"drift_threshold": 0.1,
"retraining_trigger": true
})A/B Testing
<?php
// config/ml-ab-testing.tsk
[ab_testing]
A/B testing configuration
model_ab_testing: @ml.ab_testing.configure({
"test_name": "recommendation_model_v2",
"traffic_split": {
"control": 0.5,
"treatment": 0.5
},
"metrics": ["click_through_rate", "conversion_rate", "revenue"],
"test_duration": 30
})[statistical_analysis]
Statistical analysis
statistical_analysis: @ml.ab_testing.statistics({
"confidence_level": 0.95,
"power": 0.8,
"minimum_sample_size": 1000,
"analysis_method": "bayesian"
})🔄 Automated Machine Learning
AutoML Pipeline
<?php
// config/ml-automl.tsk
[automl_pipeline]
AutoML pipeline configuration
automl_config: @ml.automl.configure({
"time_limit": 3600,
"algorithms": ["random_forest", "xgboost", "lightgbm", "neural_network"],
"feature_engineering": true,
"hyperparameter_optimization": true,
"ensemble_methods": true
})[automl_optimization]
AutoML optimization
optimization_config: @ml.automl.optimization({
"optimization_method": "bayesian",
"max_trials": 100,
"early_stopping": true,
"patience": 10
})[automl_deployment]
AutoML deployment
automl_deployment: @ml.automl.deployment({
"auto_deploy": true,
"deployment_criteria": "best_performance",
"rollback_enabled": true,
"monitoring_enabled": true
})🔐 ML Security and Privacy
Model Security
<?php
// config/ml-security.tsk
[model_security]
Model security configuration
model_protection: @ml.security.protection({
"model_encryption": true,
"access_control": true,
"audit_logging": true,
"version_control": true
})[adversarial_protection]
Adversarial protection
adversarial_protection: @ml.security.adversarial({
"input_validation": true,
"robustness_testing": true,
"adversarial_training": true,
"defense_methods": ["input_preprocessing", "model_ensemble"]
})[privacy_preservation]
Privacy preservation
privacy_preservation: @ml.security.privacy({
"differential_privacy": true,
"federated_learning": true,
"data_anonymization": true,
"secure_multiparty_computation": true
})📚 Best Practices
ML Best Practices
<?php
// config/ml-best-practices.tsk
[best_practices]
ML best practices
data_quality: @ml.best_practice("data_quality", {
"data_validation": true,
"outlier_detection": true,
"missing_value_handling": true,
"feature_engineering": true
})model_selection: @ml.best_practice("model_selection", {
"cross_validation": true,
"hyperparameter_tuning": true,
"ensemble_methods": true,
"interpretability": true
})
[anti_patterns]
ML anti-patterns
avoid_overfitting: @ml.anti_pattern("overfitting", {
"regularization": true,
"cross_validation": true,
"early_stopping": true
})avoid_data_leakage: @ml.anti_pattern("data_leakage", {
"proper_splitting": true,
"feature_isolation": true,
"temporal_validation": true
})
📚 Next Steps
Now that you've mastered TuskLang's machine learning features in PHP, explore:
1. Advanced ML Patterns - Implement sophisticated machine learning patterns 2. Deep Learning Integration - Build deep learning models with TuskLang 3. MLOps Pipeline - Implement complete ML operations pipeline 4. Edge ML - Deploy models to edge devices 5. Federated Learning - Implement distributed machine learning
🆘 Need Help?
- Documentation: https://tuskt.sk/documents/php/machine-learning - Examples: https://github.com/tusklang/php-examples - Community: https://community.tuskt.sk
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Ready to build intelligent applications with TuskLang? You're now a TuskLang machine learning master! 🚀