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Mastering Automated Machine Learning (AutoML) for Predictive Modeling

Mastering Automated Machine Learning (AutoML) for Predictive Modeling

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Mastering Automated Machine Learning (AutoML) for Predictive Modeling



Course Overview

In this comprehensive and interactive course, you will master the fundamentals of Automated Machine Learning (AutoML) and learn how to apply them to real-world predictive modeling problems. Upon completion, you will receive a certificate issued by The Art of Service.



Course Features

  • Interactive and engaging content
  • Comprehensive and personalized learning experience
  • Up-to-date and practical knowledge
  • Real-world applications and case studies
  • High-quality content and expert instructors
  • Certificate upon completion
  • Flexible learning and user-friendly interface
  • Mobile-accessible and community-driven
  • Actionable insights and hands-on projects
  • Bite-sized lessons and lifetime access
  • Gamification and progress tracking


Course Outline

Module 1: Introduction to AutoML

  • What is AutoML?: Definition, history, and evolution
  • Benefits of AutoML: Increased efficiency, improved accuracy, and reduced costs
  • AutoML applications: Predictive modeling, natural language processing, and computer vision
  • AutoML tools and platforms: Overview of popular AutoML libraries and frameworks

Module 2: Fundamentals of Machine Learning

  • Supervised and unsupervised learning: Concepts, examples, and applications
  • Regression, classification, and clustering: Algorithms and techniques
  • Model evaluation and selection: Metrics, cross-validation, and hyperparameter tuning
  • Data preprocessing and feature engineering: Handling missing values, outliers, and feature scaling

Module 3: AutoML for Predictive Modeling

  • AutoML workflows: Data ingestion, preprocessing, model selection, and deployment
  • Hyperparameter tuning and optimization: Grid search, random search, and Bayesian optimization
  • Model ensemble and stacking: Techniques for combining multiple models
  • AutoML for time series forecasting: ARIMA, SARIMA, and LSTM

Module 4: Advanced AutoML Topics

  • Transfer learning and domain adaptation: Concepts and applications
  • Attention mechanisms and explainability: Techniques for interpreting model outputs
  • AutoML for natural language processing: Text classification, sentiment analysis, and language modeling
  • AutoML for computer vision: Image classification, object detection, and segmentation

Module 5: Case Studies and Real-World Applications

  • Predictive maintenance: Using AutoML for equipment failure prediction
  • Credit risk assessment: Using AutoML for credit scoring and risk evaluation
  • Medical diagnosis: Using AutoML for disease diagnosis and patient outcome prediction
  • Marketing personalization: Using AutoML for customer segmentation and targeting

Module 6: AutoML Deployment and Maintenance

  • Model deployment and serving: Techniques for deploying models in production
  • Model monitoring and maintenance: Strategies for monitoring and updating models
  • AutoML for edge devices: Deploying AutoML models on edge devices and IoT sensors
  • AutoML for cloud and on-premises: Deploying AutoML models on cloud and on-premises infrastructure

Module 7: AutoML Ethics and Fairness

  • Bias and fairness in AutoML: Concepts and techniques for detecting and mitigating bias
  • Explainability and transparency: Techniques for interpreting and explaining AutoML models
  • AutoML for social good: Applications and case studies of AutoML for social impact
  • AutoML regulations and standards: Overview of regulations and standards for AutoML development and deployment

Module 8: Final Project and Certification

  • Final project: Applying AutoML to a real-world predictive modeling problem
  • Project evaluation and feedback: Receiving feedback and guidance on the final project
  • Certificate issuance: Receiving a certificate upon completion of the course
  • Course wrap-up and next steps: Summary of key takeaways and future learning opportunities


Certificate

Upon completion of the course, participants will receive a certificate issued by The Art of Service. The certificate will be awarded based on the completion of all course modules, assignments, and the final project.



Target Audience

This course is designed for professionals and students who want to learn about Automated Machine Learning (AutoML) and its applications in predictive modeling. The target audience includes:

  • Data scientists and machine learning engineers
  • Business analysts and data analysts
  • Software developers and programmers
  • Researchers and academics
  • Anyone interested in learning about AutoML and predictive modeling
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