Data Quality & Feature Engineering for AI/ML Training Course

Introduction

High-quality data is the bedrock of effective Artificial Intelligence (AI) and Machine Learning (ML) models. The Data Quality & Feature Engineering for AI/ML Training Course is an essential program designed for data scientists, machine learning engineers, and data analysts who understand that the success of any AI initiative hinges on the integrity and relevance of its input data. This course provides an in-depth exploration of the critical processes involved in transforming raw, imperfect data into the clean, structured, and insightful features that drive superior model performance.

Participants will master advanced techniques for data cleansing, validation, and imputation, ensuring data accuracy and consistency. A significant focus will be placed on feature engineering, where learners will discover how to creatively derive new, highly predictive attributes from existing datasets, including handling imbalanced data and unstructured formats. By the end of this practical training, you will be equipped with the expertise to identify and resolve common data quality issues, craft impactful features, and significantly enhance the robustness and predictive power of your AI and machine learning models, thereby accelerating your data-driven innovation.

Target Audience

• Data Scientists and Machine Learning Engineers.
• Data Analysts and Business Intelligence Professionals.
• Data Engineers responsible for data pipelines.
• AI/ML Developers looking to improve model performance.
• Professionals involved in data governance and data quality initiatives.
• Anyone interested in maximizing the value of data for AI.

Duration

10 days

Course Objectives

1. Understand the profound impact of data quality on AI and Machine Learning model performance.
2. Master techniques for identifying, cleaning, and validating inconsistent or erroneous data.
3. Implement effective strategies for handling missing values and outliers in diverse datasets.
4. Learn the principles and advanced techniques of feature engineering to create highly predictive features.
5. Apply various methods for dimensionality reduction and feature selection.
6. Develop proficiency in managing and preparing data for different AI/ML tasks and model types.
7. Address challenges related to imbalanced datasets and apply appropriate handling techniques.
8. Establish robust data pipelines and quality checks to ensure continuous data integrity for AI/ML workflows.

Course Content

Module 1. The Criticality of Data Quality for AI/ML

• Why data quality is the foundation for successful AI and ML projects
• Common data quality issues: Inaccuracy, inconsistency, incompleteness, duplication, timeliness
• The cost of poor data quality in AI applications
• Data-centric AI vs. Model-centric AI approaches
• Introduction to data quality dimensions and metrics

Module 2. Data Profiling and Assessment

• Techniques for assessing the "health" of your datasets
• Identifying data patterns, anomalies, and distributions
• Tools and libraries for automated data profiling (e.g., Pandas Profiling, Great Expectations)
• Setting data quality benchmarks and Key Performance Indicators (KPIs)
• Practical exercise: Conducting a data quality audit on a sample dataset

Module 3. Data Cleaning and Validation

• Principles of effective data cleansing and standardization
• Handling missing values: Imputation strategies (mean, median, mode, regression, machine learning-based)
• Outlier detection and treatment methods (e.g., IQR, Z-score, Isolation Forest)
• Data validation rules and constraint checking
• Automating cleaning processes and building reusable cleaning functions

Module 4. Data Transformation and Normalization

• Data transformation techniques for different data types
• Feature scaling: Min-Max scaling, Standardization (Z-score normalization), Robust scaling
• Encoding categorical variables: One-Hot Encoding, Label Encoding, Target Encoding
• Date and time feature extraction and transformation
• Text data preprocessing: Tokenization, stemming, lemmatization, stop word removal

Module 5. Fundamentals of Feature Engineering

• What is Feature Engineering: Definition, importance, and best practices
• The role of domain knowledge in feature creation
• Common feature engineering strategies: Aggregation, binning, interaction features
• Creating new features from existing ones using mathematical operations
• Feature engineering for tabular, time-series, and categorical data

Module 6. Advanced Feature Engineering Techniques

• Polynomial features and non-linear transformations
• Feature hashing for high-cardinality categorical variables
• Embedding techniques for categorical and textual data
• Feature engineering for specific ML models (e.g., tree-based vs. linear models)
• Automated Feature Engineering (AutoFE) tools and concepts (e.g., Featuretools)

Module 7. Feature Selection and Dimensionality Reduction

• Why feature selection is important: Reducing noise, improving interpretability, combating overfitting
• Filter methods: Correlation, Chi-squared, Mutual Information
• Wrapper methods: Recursive Feature Elimination (RFE), Sequential Feature Selection
• Embedded methods: L1 regularization (Lasso), tree-based feature importances
• Dimensionality reduction techniques: PCA, t-SNE, UMAP for visualization and noise reduction

Module 8. Handling Imbalanced Data

• Understanding imbalanced datasets: Impact on model performance and evaluation metrics
• Resampling techniques: Oversampling minority class (SMOTE, ADASYN), Undersampling majority class
• Ensemble methods for imbalanced data (e.g., BalancedBaggingClassifier, EasyEnsemble)
• Cost-sensitive learning and adjusting class weights
• Using appropriate evaluation metrics for imbalanced data (Precision, Recall, F1-Score, AUC-PR)

Module 9. Data Annotation and Labeling for Supervised Learning

• The role of high-quality labels in supervised AI/ML
• Data annotation types: Image classification, object detection, segmentation, text annotation (NER, sentiment)
• Best practices for data labeling: Guidelines, consistency, quality control
• Human-in-the-loop (HITL) strategies for annotation
• Tools and platforms for efficient data labeling

Module 10. Data Lineage and Metadata Management

• Understanding data lineage: Tracking data origin, transformations, and usage
• The importance of metadata for data discovery and quality
• Metadata management tools and strategies
• Ensuring transparency and reproducibility in AI/ML data pipelines
• Building data dictionaries and glossaries for shared understanding

Module 11. Data Pipelines and MLOps for Data Quality

• Designing robust data ingestion and preprocessing pipelines
• Implementing automated data quality checks within pipelines
• Versioning data and features for reproducibility
• Integrating data quality management into MLOps workflows
• Monitoring data drift and concept drift in production AI/ML systems

Module 12. Case Studies and Best Practices

• Real-world examples of data quality and feature engineering impacting AI projects
• Lessons learned from industry successes and failures
• Best practices for continuous data quality improvement
• Building a data-driven culture in organizations
• Future trends in data quality, feature engineering, and automated data preparation for AI.

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank