Automation with Artificial Intelligence @ Singapore

For more information: https://casugol.com/course/aai

International Acclaimed Certification. 5-Star Reviews

Suitable for everyone. Learn in an Interactive, Supportive, and Encouraging Environment.

• Duration: 8 hours
• Certification: Participants will receive a Certificate of Completion upon completing the course

Course Objective

• Acquire practical knowledge and skills in core concepts of automation and artificial intelligence, and their role in streamlining workflows.
• Learn how to design, implement, and integrate AI-driven automation solutions across various workflows, evaluate the performance of AI models and automation systems, and apply optimization strategies for better outcomes.

Examination

NA

• importance of Data in AI-Driven Automation
• Types of Data: Structured, Unstructured, and Semi-structured
• Data Sources: Public Datasets, Proprietary Data, and APIs
• Key Concept: The Impact of High-quality Data on AI Model Performance
• Data Cleaning and Preprocessing Techniques using Python
• Handling Missing Values, Outliers, and Noise
• Data Normalization and Standardization Techniques
• Feature Selection, Extraction, and Engineering for AI

• Data Validation, Consistency Checks, and Dealing with Incomplete data
• Techniques for Improving Data Accuracy and Reliability
• Importance of Metadata and Proper Documentation
• Data Storage and Management for Automation
• ETL vs. ELT
• Cloud-based Data Storage Solutions (AWS, Google Cloud, Azure)

• Robotic Process Automation (RPA) vs. Intelligent Process Automation (IPA)
• Overview of AI Use Cases in Automation (e.g., Predictive Maintenance, Customer Support Automation)
• Key Components of AI-driven Automation Systems
• Identifying Tasks for Automation
• Creating Flowcharts and Designing AI-driven Workflows
  Best practices for Integrating AI into Existing Business Workflows

• Basics of Machine Learning Models: Supervised vs. Unsupervised Learning
• Common AI Algorithms Used in Automation
• Preparing Datasets for Model Training: Splitting into Training, Validation, and Test Sets
• Building Simple Machine Learning Models for Automation (e.g., regression, classification) using Python
• Training and Testing Models using Python Frameworks

• Key Performance Metrics for AI-driven Automation (Accuracy, Precision, Recall, F1-Score)
• Model Validation Techniques: Cross-Validation, A/B Testing, and Confusion Matrix
• Bias and Fairness in AI Models
• Methods to Measure the Effectiveness of AI Automation on Workflows
• Monitoring AI Models and Workflows in Real Time

• Fine-tuning Hyperparameters for Better Model Performance
• Techniques for Optimizing Large-scale Automation Solutions
• Implementing Feedback Loops for Continuous Improvement

Be Recognized as a CASUGOL Certified Professional

Add Value to your already prolific portfolio with a Certificate of Completion from CASUGOL.

Awarded upon completing and meeting all requirements of the course.

Host or Publisher CASUGOL