Learn statistical and model-based methods like RFE and Lasso, and implement them in Python with scikit-learn to improve model performance.
Feature Selection: Statistical and Model-based Methods Read More »
Learn to create polynomial features, interaction terms, and temporal features using Python, scikit-learn, and pandas to improve model performance.
Feature Engineering: Creating New Features from Raw Data Read More »
Automated data quality monitoring systems for production ML pipelines. Learn to detect data drift, validate schemas, and build robust MLOps workflows.
Automated Data Quality Monitoring Read More »
Automated data validation and quality assurance pipelines. Learn to use Pandera and Great Expectations to prevent bad data from impacting model performance.
Data Validation and Quality Assurance for Machine Learning Read More »
Outlier detection and treatment for AI. Learn statistical methods, Isolation Forest, and DBSCAN with Python examples to build robust machine learning models.
Outlier Detection and Treatment Methods Read More »
Handling missing data in AI engineering. Learn theoretical foundations (MCAR, MAR, MNAR) and practical data cleaning techniques.
Data Cleaning Techniques: Handling Missing Data Read More »
Design and implement real-time data streaming architectures for machine learning using Apache Kafka and Apache Storm, with practical Python examples.
Real-time Data Streaming Architecture Read More »
Data privacy in AI. Learn to implement GDPR and CCPA compliance, use anonymization, differential privacy, and federated learning.
Data Privacy and Compliance (GDPR, CCPA) Read More »
Data quality assessment and profiling for AI. Learn to implement systematic data validation and profiling using Python, Great Expectations & other tools.
Data Quality Assessment and Profiling Read More »
Learn to gather high-quality data using APIs, web scraping with Python, and database queries. Includes practical code examples and industry best practices.
Data Collection Strategies and Sources Read More »
