Feature Engineering For Machine Learning Training Course: Prepare Ml Data

Introduction

Supercharge your machine learning models with our Feature Engineering for Machine Learning Training Course. This program is designed to equip you with the essential skills to prepare data for machine learning models, enabling you to create high-performing models through effective feature extraction and transformation. In today's data-driven world, mastering feature engineering is crucial for organizations seeking to build accurate and reliable machine learning systems. Our feature engineering training course offers hands-on experience and expert guidance, empowering you to optimize your data for machine learning model performance.

This prepare ML data training delves into the core concepts of feature engineering, covering topics such as data cleaning, feature selection, and transformation techniques. You'll gain expertise in using industry-standard tools and techniques to prepare data for machine learning models, meeting the demands of modern machine learning projects. Whether you're a data scientist, machine learning engineer, or data analyst, this Feature Engineering for Machine Learning course will empower you to design and implement effective data preparation strategies.

Target Audience:

• Data Scientists
• Machine Learning Engineers
• Data Analysts
• AI Engineers
• Software Developers
• Statisticians
• Anyone needing feature engineering skills

Course 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.

Duration

10 Days

Course content

Module 1: Introduction to Feature Engineering

• Fundamentals of feature engineering for machine learning.
• Overview of data cleaning, feature selection, and transformation.
• Setting up a feature engineering development environment.
• Introduction to feature engineering tools and libraries.
• Best practices for feature engineering.

Module 2: Data Cleaning and Preprocessing

• Mastering data cleaning and preprocessing techniques.
• Utilizing methods for handling missing values and outliers.
• Implementing data normalization and standardization.
• Designing and building data cleaning pipelines.
• Best practices for data cleaning.

Module 3: Feature Selection and Dimensionality Reduction

• Utilizing feature selection and dimensionality reduction methods.
• Implementing techniques like correlation analysis and PCA.
• Designing and building feature selection models.
• Optimizing feature sets for model performance.
• Best practices for feature selection.

Module 4: Feature Transformation and Encoding

• Implementing feature transformation and encoding strategies.
• Utilizing techniques like one-hot encoding and binning.
• Designing and building feature transformation pipelines.
• Optimizing encoding for categorical and numerical data.
• Best practices for feature transformation.

Module 5: Feature Pipelines for Models

• Designing and building feature pipelines for machine learning models.
• Utilizing pipeline frameworks (e.g., scikit-learn pipelines).
• Implementing automated feature engineering workflows.
• Optimizing pipelines for model training and deployment.
• Best practices for feature pipelines.

Module 6: Feature Optimization

• Optimizing feature engineering for model performance and interpretability.
• Utilizing feature importance and selection metrics.
• Implementing feature tuning and hyperparameter optimization.
• Designing efficient feature sets.
• Best practices for feature optimization.

Module 7: Troubleshooting Feature Engineering

• Debugging common issues in feature engineering.
• Analyzing feature distributions and correlations.
• Utilizing troubleshooting techniques for problem resolution.
• Resolving common data preparation errors.
• Best practices for troubleshooting.

Module 8: Data Validation and Quality Checks

• Implementing data validation and quality checks for feature sets.
• Utilizing data validation libraries and techniques.
• Designing and building data quality monitoring systems.
• Optimizing validation for feature integrity.
• Best practices for data validation.

Module 9: Integration with ML Frameworks

• Integrating feature engineering with various machine learning frameworks.
• Utilizing libraries like scikit-learn, TensorFlow, and PyTorch.
• Implementing feature transformations for specific model architectures.
• Optimizing integration for model training.
• Best practices for framework integration.

Module 10: Handling Different Data Types

• Understanding how to handle different data types and structures.
• Utilizing techniques for text, image, and time-series data.
• Implementing feature engineering for structured and unstructured data.
• Designing data preparation strategies.
• Best practices for data types.

Module 11: Advanced Feature Engineering

• Exploring advanced feature engineering techniques (automated feature engineering, deep feature synthesis).
• Utilizing automated feature engineering tools.
• Implementing deep feature synthesis for complex data.
• Designing and building advanced feature pipelines.
• Optimizing advanced techniques for specific applications.
• Best practices for advanced techniques.

Module 12: Real-World Use Cases

• Implementing feature engineering for recommendation systems.
• Utilizing feature engineering for natural language processing.
• Implementing feature engineering for computer vision tasks.
• Utilizing feature engineering for financial fraud detection.
• Best practices for real-world applications.

Module 13: Feature Engineering Tools Implementation

• Utilizing feature engineering tools and libraries (Featuretools, TensorFlow Transform).
• Implementing data preparation with specific tools.
• Designing and building automated feature engineering scripts.
• Optimizing tool usage for efficient development.
• Best practices for tool implementation.

Module 14: Feature Performance Monitoring

• Implementing feature performance monitoring.
• Utilizing feature importance and model performance metrics.
• Designing and building performance dashboards.
• Optimizing monitoring for feature effectiveness.
• Best practices for monitoring.

Module 15: Future Trends in Feature Engineering

• Emerging trends in feature engineering.
• Utilizing AI for automated feature generation.
• Implementing feature engineering in cloud-native environments.
• Best practices for future applications.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.