15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
- Business Intelligence Professionals
- Predictive Analytics Specialists
- Statistical Modelers
- Data Mining Professionals
- Python Developers working with machine learning
- Researchers working with predictive models
- MLOps and AI Platform Professionals
- Quantitative Analysts
- Risk Analytics Professionals
- Marketing and Customer Analytics Professionals
- Professionals involved in building predictive and classification models
- Managers and technical leads overseeing AI and machine learning projects
Session Objectives
- Understand the fundamentals of feature engineering for machine learning.
- Master data cleaning and preprocessing techniques.
- Utilize feature selection and dimensionality reduction methods.
- Implement feature transformation and encoding strategies.
- Design and build feature pipelines for machine learning models.
- Optimize feature engineering for model performance and interpretability.
- Troubleshoot and address common issues in feature engineering.
- Implement data validation and quality checks for feature sets.
- Integrate feature engineering with various machine learning frameworks.
- Understand how to handle different data types and structures.
- Explore advanced feature engineering techniques (e.g., automated feature engineering, deep feature synthesis).
- Apply real world use cases for feature engineering in machine learning.
- Leverage feature engineering tools and libraries for efficient data preparation.
About the Course
This comprehensive training course equips participants with the practical skills required to transform raw, complex, and unstructured data into high-quality features that improve machine learning model performance. Feature engineering is a critical stage of the machine learning lifecycle, directly influencing model accuracy, robustness, interpretability, and generalization.
Participants will learn how to explore datasets, identify informative variables, handle missing and inconsistent data, encode categorical variables, scale numerical features, engineer time-based and domain-specific variables, and select the most valuable features for predictive modeling. The course also covers dimensionality reduction, feature interactions, automated feature engineering, leakage prevention, and techniques for creating features that remain reliable when models move into production.Through practical exercises and real-world datasets, participants will develop end-to-end feature engineering workflows and learn how to evaluate whether engineered features genuinely improve model performance.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Fundamentals of feature engineering for machine learning.
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Workshop 1.2:
Overview of data cleaning, feature selection, and transformation.
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Workshop 1.3:
Setting up a feature engineering development environment.
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Workshop 1.4:
Introduction to feature engineering tools and libraries.
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Workshop 1.5:
Best practices for feature engineering.
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Workshop 2.1:
Mastering data cleaning and preprocessing techniques.
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Workshop 2.2:
Utilizing methods for handling missing values and outliers.
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Workshop 2.3:
Implementing data normalization and standardization.
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Workshop 2.4:
Designing and building data cleaning pipelines.
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Workshop 2.5:
Best practices for data cleaning.
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Workshop 3.1:
Utilizing feature selection and dimensionality reduction methods.
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Workshop 3.2:
Implementing techniques like correlation analysis and PCA.
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Workshop 3.3:
Designing and building feature selection models.
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Workshop 3.4:
Optimizing feature sets for model performance.
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Workshop 3.5:
Best practices for feature selection.
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Workshop 4.1:
Implementing feature transformation and encoding strategies.
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Workshop 4.2:
Utilizing techniques like one-hot encoding and binning.
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Workshop 4.3:
Designing and building feature transformation pipelines.
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Workshop 4.4:
Optimizing encoding for categorical and numerical data.
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Workshop 4.5:
Best practices for feature transformation.
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Workshop 5.1:
Designing and building feature pipelines for machine learning models.
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Workshop 5.2:
Utilizing pipeline frameworks (e.g., scikit-learn pipelines).
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Workshop 5.3:
Implementing automated feature engineering workflows.
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Workshop 5.4:
Optimizing pipelines for model training and deployment.
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Workshop 5.5:
Best practices for feature pipelines.
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Workshop 6.1:
Optimizing feature engineering for model performance and interpretability.
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Workshop 6.2:
Utilizing feature importance and selection metrics.
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Workshop 6.3:
Implementing feature tuning and hyperparameter optimization.
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Workshop 6.4:
Designing efficient feature sets.
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Workshop 6.5:
Best practices for feature optimization.
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Workshop 7.1:
Debugging common issues in feature engineering.
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Workshop 7.2:
Analyzing feature distributions and correlations.
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Workshop 7.3:
Utilizing troubleshooting techniques for problem resolution.
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Workshop 7.4:
Resolving common data preparation errors.
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Workshop 7.5:
Best practices for troubleshooting.
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Workshop 8.1:
Implementing data validation and quality checks for feature sets.
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Workshop 8.2:
Utilizing data validation libraries and techniques.
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Workshop 8.3:
Designing and building data quality monitoring systems.
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Workshop 8.4:
Optimizing validation for feature integrity.
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Workshop 8.5:
Best practices for data validation.
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Workshop 9.1:
Integrating feature engineering with various machine learning frameworks.
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Workshop 9.2:
Utilizing libraries like scikit-learn, TensorFlow, and PyTorch.
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Workshop 9.3:
Implementing feature transformations for specific model architectures.
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Workshop 9.4:
Optimizing integration for model training.
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Workshop 9.5:
Best practices for framework integration.
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Workshop 10.1:
Understanding how to handle different data types and structures.
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Workshop 10.2:
Utilizing techniques for text, image, and time-series data.
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Workshop 10.3:
Implementing feature engineering for structured and unstructured data.
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Workshop 10.4:
Designing data preparation strategies.
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Workshop 10.5:
Best practices for data types.
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Workshop 11.1:
Exploring advanced feature engineering techniques (automated feature engineering, deep feature synthesis).
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Workshop 11.2:
Utilizing automated feature engineering tools.
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Workshop 11.3:
Implementing deep feature synthesis for complex data.
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Workshop 11.4:
Designing and building advanced feature pipelines.
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Workshop 11.5:
Optimizing advanced techniques for specific applications.
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Workshop 11.6:
Best practices for advanced techniques.
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Workshop 12.1:
Implementing feature engineering for recommendation systems.
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Workshop 12.2:
Utilizing feature engineering for natural language processing.
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Workshop 12.3:
Implementing feature engineering for computer vision tasks.
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Workshop 12.4:
Utilizing feature engineering for financial fraud detection.
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Workshop 12.5:
Best practices for real-world applications.
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Workshop 13.1:
Utilizing feature engineering tools and libraries (Featuretools, TensorFlow Transform).
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Workshop 13.2:
Implementing data preparation with specific tools.
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Workshop 13.3:
Designing and building automated feature engineering scripts.
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Workshop 13.4:
Optimizing tool usage for efficient development.
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Workshop 13.5:
Best practices for tool implementation.
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Workshop 14.1:
Implementing feature performance monitoring.
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Workshop 14.2:
Utilizing feature importance and model performance metrics.
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Workshop 14.3:
Designing and building performance dashboards.
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Workshop 14.4:
Optimizing monitoring for feature effectiveness.
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Workshop 14.5:
Best practices for monitoring.
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Workshop 15.1:
Emerging trends in feature engineering.
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Workshop 15.2:
Utilizing AI for automated feature generation.
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Workshop 15.3:
Implementing feature engineering in cloud-native environments.
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Workshop 15.4:
Best practices for future applications.
