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Learn how to design, implement, and integrate AI-driven automation solutions across various workflows and evaluate the performance of AIAbout this Event
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
Module 1 – Introduction to Data Preparation and Management
- 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
Module 2 - Advanced Data Management Techniques
- 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)
Module 3 - Introduction to AI-Driven Automation Solutions
- 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
Module 4 - Building and Training AI Models for Automation
- 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
Module 5 - Evaluating AI-Driven Automation Solutions
- 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
Module 6 - AI Model Optimization Techniques
- 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.
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Event Venue & Nearby Stays
The Horizon, Bangsar South, Jalan Pantai Permai, Kuala Lumpur, Malaysia
Tickets
SGD 309.25
