What does the Automated Machine Learning (AutoML) Models course cover?
Automated Machine Learning (AutoML) Models is covered here in 7 modules: Introduction to AutoML: Applications of AutoML, History and evolution of AutoML, Data Preprocessing and Feature Engineering: Data visualization and exploration, Building and Deploying AutoML Models: Hyperparameter tuning and model optimization and 4 more. The outline lists 35 specific topics, opening with What is AutoML?
How do you approach Automated Machine Learning (AutoML) Models step by step?
The work is sequenced in 7 stages. It starts with Introduction to AutoML: Applications of AutoML, History and evolution of AutoML, moves through Data Preprocessing and Feature Engineering: Data visualization and exploration and Building and Deploying AutoML Models: Hyperparameter tuning and model optimization, and ends at AutoML Case Studies and Projects: Presenting and discussing project results.
What is in Module 1 of the Automated Machine Learning (AutoML) Models course?
Module 1 is Introduction to AutoML: Applications of AutoML, History and evolution of AutoML. It works through What is AutoML?, History and evolution of AutoML, Applications of AutoML and 2 more. It sets the vocabulary the remaining 6 modules build on.
How is the Automated Machine Learning (AutoML) Models course delivered?
The Automated Machine Learning (AutoML) Models course is fully self-paced with immediate online access after enrolment. Access does not expire and future updates are included at no cost. It can be taken on any device, and a certificate of completion is issued by The Art of Service when you finish.
How much does the Automated Machine Learning (AutoML) Models course cost?
The Automated Machine Learning (AutoML) Models course is $199 as a one time payment. There is no subscription, no per seat licence and no hidden fee. Enrolment carries a 30 day satisfied or refunded guarantee, so it can be assessed in full before you commit.
Closely related courses: AutoML Models Toolkit, Enterprise Architecture Tools, Application Security Engineering, Software Regression Analysis.
More answers: what you get with every course, refund policy, all help answers.
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: Applications of AutoML, History and evolution of 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 visualization and exploration
- 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: Hyperparameter tuning and model optimization
- 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: Using ensemble methods and stacking
- 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 multi-task learning and meta-learning
- 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 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: Presenting and discussing project results
- 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.