Machine Learning Engineer: Free Skill Evaluation

What Is Machine Learning? ML Engineer vs Data Scientist Production ML Lifecycle Python Fundamentals OOP Advanced Python Production Python Linear Algebra Calculus Probability Descriptive Statistics Statistical Relationships Sampling Hypothesis Testing Concepts Statistics for ML Decisions Arrays Vectorized Computation Matrix Operations DataFrames Cleaning Transformations Large Dataset Practices SQL Fundamentals Joins Advanced SQL SQL for Feature Extraction EDA Visualization Data Quality Training Data Validation Data Test Data Data Leakage Time-Based Splits Linear Regression Logistic Regression KNN Naive Bayes Decision Trees Random Forest Support Vector Machines Gradient Boosting XGBoost LightGBM CatBoost K-Means Hierarchical Clustering DBSCAN PCA Missing Values Categorical Encoding Numerical Transformations Date/Time Features Interaction Features Domain Features Feature Leakage Filter Methods Wrapper Methods Embedded Methods Dimensionality Management Classification Metrics Regression Metrics Ranking Metrics Concepts Threshold Selection Business-Cost Evaluation Class Imbalance Resampling Class Weights Correct Metrics K-Fold Stratified K-Fold Time-Series Validation Concepts Group-Based Validation Hyperparameters Grid Search Random Search Bayesian Optimization Concepts Early Stopping Scikit-Learn Pipelines ColumnTransformer Training/Serving Consistency Why Experiments Need Tracking Experiment Metadata MLflow Comparing Experiments Model Registry Model Metadata Model Lineage Code Versioning Data Versioning Concepts Environment Reproducibility Random Seeds Prediction API Request Validation Response Model Error Handling API Documentation Real-Time Inference Batch Inference Asynchronous Inference Inference Architecture Selection Containers Containerizing ML APIs Docker Compose MLOps Fundamentals MLOps Components Development Environments Training Pipeline Pipeline Failure Handling Idempotency Concepts CI Pipeline ML Quality Gate CD Pipeline Rollback Blue/Green Canary Deployment Shadow Deployment A/B Testing Production Monitoring Data Quality Monitoring Data Drift Concept Drift Prediction Drift Feature Attribution Drift Model Quality Monitoring Technical vs Business Metrics Business Thresholds Alert Rules Alert Severity Response Playbooks Retraining Triggers Automated Retraining Human Approval Feature Store Concepts Offline Features Online Features Training-Serving Skew Schema Validation Statistical Validation Pipeline Data Contracts Feature Importance Local Explanations SHAP Concepts Explainability UI Bias Fairness Privacy Model Documentation API Security Model Abuse Data Poisoning Concepts Adversarial Input Concepts Artifact Security AWS Fundamentals ML Data Storage Training Architecture Model Endpoint Architecture AWS ML Service Concepts Load Balancing Horizontal Scaling Autoscaling Model Caching Prediction Caching Latency Throughput Memory Model Complexity vs Performance Training Cost Serving Cost Cost vs Accuracy Recommendations Content-Based Filtering Collaborative Filtering Cold Start Ranking Concepts What Is an Anomaly? Statistical Methods Isolation Forest Business Applications Time-Series Basics Time Features Forecast Validation ML-Based Forecasting ML System Design Framework Design Fraud Detection System Design Recommendation Platform Design Churn Prediction Platform Design Credit Risk System Design Real-Time Prediction Service Production Machine Learning Platform