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Mastering Automated Machine Learning (AutoML) Models; A Hands-on Approach

Mastering Automated Machine Learning (AutoML) Models; A Hands-on Approach

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Mastering Automated Machine Learning (AutoML) Models: A Hands-on Approach



Course Overview

This comprehensive course is designed to equip participants with the skills and knowledge needed to master Automated Machine Learning (AutoML) models. Through a hands-on approach, participants will learn how to build, deploy, and manage AutoML models that can solve complex real-world problems.



Course Objectives

  • Understand the fundamentals of AutoML and its applications
  • Learn how to build and deploy AutoML models using popular frameworks and tools
  • Master the art of data preprocessing, feature engineering, and hyperparameter tuning
  • Understand how to evaluate and optimize AutoML models for better performance
  • Learn how to integrate AutoML models with other machine learning models and deploy them in real-world applications


Course Outline

Module 1: Introduction to AutoML

  • What is AutoML?
  • History and evolution of AutoML
  • Applications of AutoML
  • Advantages and disadvantages of AutoML
  • Overview of popular AutoML frameworks and tools

Module 2: Data Preprocessing and Feature Engineering

  • Data preprocessing techniques for AutoML
  • Feature engineering techniques for AutoML
  • Data transformation and normalization
  • Handling missing values and outliers
  • Data visualization and exploration

Module 3: Building and Deploying AutoML Models

  • Building AutoML models using popular frameworks and tools
  • Deploying AutoML models in real-world applications
  • Integrating AutoML models with other machine learning models
  • Using AutoML for regression, classification, clustering, and other tasks
  • Hyperparameter tuning and model optimization

Module 4: Evaluating and Optimizing AutoML Models

  • Evaluation metrics for AutoML models
  • Optimization techniques for AutoML models
  • Using cross-validation and walk-forward optimization
  • Handling overfitting and underfitting
  • Using ensemble methods and stacking

Module 5: Advanced AutoML Topics

  • Using transfer learning and domain adaptation
  • Using attention mechanisms and graph neural networks
  • Using generative models and adversarial training
  • Using multi-task learning and meta-learning
  • Using AutoML for natural language processing and computer vision

Module 6: Real-World Applications of AutoML

  • Using AutoML in finance and banking
  • Using AutoML in healthcare and medicine
  • Using AutoML in marketing and advertising
  • Using AutoML in transportation and logistics
  • Using AutoML in energy and environment

Module 7: AutoML Case Studies and Projects

  • Real-world case studies of AutoML applications
  • Hands-on projects using popular AutoML frameworks and tools
  • Building and deploying AutoML models for real-world problems
  • Evaluating and optimizing AutoML models for better performance
  • Presenting and discussing project results


Course Features

  • Interactive and engaging: The course includes hands-on projects, case studies, and interactive exercises to keep participants engaged and motivated.
  • Comprehensive and up-to-date: The course covers the latest advancements and techniques in AutoML, including popular frameworks and tools.
  • Personalized and flexible: The course allows participants to learn at their own pace and provides personalized feedback and support.
  • Practical and real-world applications: The course focuses on real-world applications and case studies to help participants understand how to apply AutoML in practice.
  • High-quality content and expert instructors: The course is taught by expert instructors and includes high-quality content, including video lectures, readings, and assignments.
  • Certification and recognition: Participants receive a certificate upon completion, issued by The Art of Service.
  • Lifetime access and support: Participants have lifetime access to the course materials and support from the instructors and community.
  • Gamification and progress tracking: The course includes gamification elements and progress tracking to help participants stay motivated and engaged.
  • Mobile-accessible and user-friendly: The course is designed to be mobile-accessible and user-friendly, allowing participants to learn on-the-go.
  • Community-driven and actionable insights: The course includes a community-driven approach and provides actionable insights and feedback to help participants improve their skills and knowledge.


Certificate of Completion

Upon completing the course, participants will receive a Certificate of Completion, issued by The Art of Service. This certificate is a recognition of the participant's skills and knowledge in AutoML and can be used to demonstrate their expertise to employers and clients.

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