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LAPT
London Academy of Professional Training
Certificate
ISO 23053 — Framework for AI Systems Using Machine Learning
ISO Certification Programme
REF: IIT-AII-23053
• ISO IT & Related Technologies
• Artificial Intelligence
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| 6Subjects | 20Chapters | 114Classes | 430Total Marks | 60%Pass Mark | LifetimeValidity |
At a Glance
| Reference | IIT-AII-23053 |
| Award | Certificate |
| Assessment | 430 marks — 258 to pass (60%) |
| Prerequisites | None |
| Validity | Lifetime |
| Awarding Body | LAPT — London Academy of Professional Training |
Curriculum
| 1 | AI Systems Evaluation and Optimisation | 0 chapters 85 marks |
| 2 | Artificial Intelligence Fundamentals | 5 chapters · 30 classes 85 marks |
• 1 Understanding Machine Learning Concepts and Terminology — 6 classes
› 1.1 Define Key Machine Learning Terminology
› 1.2 Explore Types of Machine Learning Techniques
› 1.3 Examine Supervised vs. Unsupervised Learning
› 1.4 Identify Applications of Machine Learning in Real-world Scenarios
› 1.5 Analyze the Role of Data in Machine Learning Models
› 1.6 Evaluate Best Practices for Implementing Machine Learning
• 2 Data Preparation and Preprocessing Techniques for Machine Learning — 6 classes
› 2.1 Understand the Importance of Data Preparation in Machine Learning
› 2.2 Identify Common Data Quality Issues Affecting Machine Learning
› 2.3 Explore Techniques for Handling Missing Data in Datasets
› 2.4 Apply Data Transformation Methods for Feature Scaling
› 2.5 Demonstrate Data Encoding Techniques for Categorical Variables
› 2.6 Create a Comprehensive Data Preprocessing Pipeline for ML Models
• 3 Model Selection and Evaluation Metrics in Machine Learning — 6 classes
› 3.1 Understand the Importance of Model Selection in Machine Learning
› 3.2 Identify Key Factors Influencing Model Selection
› 3.3 Explore Common Machine Learning Models and Their Use Cases
› 3.4 Analyze Evaluation Metrics for Model Performance
› 3.5 Compare Different Models Using Standard Evaluation Techniques
› 3.6 Apply Best Practices for Model Selection and Evaluation in a Case Study
• 4 Advanced Machine Learning Algorithms and Techniques — 6 classes
› 4.1 Explore Decision Trees and Their Applications
› 4.2 Analyze Support Vector Machines for Classification Tasks
› 4.3 Implement Neural Networks for Complex Data Problems
› 4.4 Investigate Ensemble Learning Techniques for Improved Accuracy
› 4.5 Evaluate Deep Learning Methods for Image Recognition
› 4.6 Apply Reinforcement Learning to Dynamic Decision Making
• 5 Ethical Considerations and Governance in AI Systems — 6 classes
› 5.1 Identify Ethical Principles in AI Development
› 5.2 Analyze Real-World AI Ethical Dilemmas
› 5.3 Evaluate the Role of Governance in AI Systems
› 5.4 Discuss the Impact of Bias in AI Algorithms
› 5.5 Create a Framework for Responsible AI Use
› 5.6 Propose Strategies for Ethical AI Implementation
| 3 | Ethics in AI and Machine Learning | 5 chapters · 24 classes 45 marks |
• 1 Foundations of Ethics in AI: Key Concepts and Theories — 6 classes
› 1.1 Define Key Ethical Concepts in AI and Machine Learning
› 1.2 Explore Historical Theories of Ethics Relevant to AI
› 1.3 Analyze the Role of Bias in AI Ethics
› 1.4 Discuss Ethical Implications of Machine Learning Algorithms
› 1.5 Evaluate Case Studies on Ethical Dilemmas in AI
› 1.6 Propose Ethical Guidelines for AI Development
• 2 Understanding Bias and Fairness in Machine Learning — 6 classes
› 2.1 Define and Identify Bias in Machine Learning Systems
› 2.2 Explore Types of Bias: Data, Algorithmic, and User
› 2.3 Examine Real-World Examples of Bias in AI Applications
› 2.4 Assess the Impact of Bias on Fairness and Decision-Making
› 2.5 Implement Techniques for Mitigating Bias in Machine Learning
› 2.6 Develop an Action Plan for Ensuring Fairness in AI Projects
• 3 Transparency and Explainability in AI Systems — 6 classes
› 3.1 Define Transparency and Explainability in AI Systems
› 3.2 Explore the Importance of Transparency in AI Decision-Making
› 3.3 Analyze Case Studies of AI Systems Lacking Explainability
› 3.4 Identify Techniques for Enhancing Explainability in Machine Learning
› 3.5 Evaluate Ethical Implications of Transparent AI Systems
› 3.6 Develop a Framework for Implementing Explainable AI in Practice
• 4 Privacy and Data Protection in AI Applications — 6 classes
› 4.1 Understand Key Principles of Privacy in AI
› 4.2 Identify Data Protection Regulations Impacting AI
› 4.3 Analyze Ethical Implications of Data Usage in AI
› 4.4 Evaluate Risk Management Strategies for Personal Data
› 4.5 Develop Best Practices for Ensuring Data Privacy in AI
› 4.6 Create a Privacy Impact Assessment for an AI Application
• 5 Governance and Accountability in AI Systems
| 4 | ISO 23053 Framework Overview | 5 chapters · 30 classes 65 marks |
• 1 Introduction to ISO 23053: Understanding the Framework — 6 classes
› 1.1 Define the Key Concepts of ISO 23053
› 1.2 Explore the Structure of the ISO 23053 Framework
› 1.3 Identify the Objectives of Implementing the Framework
› 1.4 Examine the Relevance of AI Systems in Machine Learning
› 1.5 Analyze the Benefits of ISO 23053 for Organizations
› 1.6 Apply Framework Principles to Real-World AI Scenarios
• 2 Key Components of ISO 23053: Structure and Elements — 6 classes
› 2.1 Define the Structure of ISO 23053 Framework for AI Systems
› 2.2 Identify the Key Elements of Machine Learning within ISO 23053
› 2.3 Explore the Roles and Responsibilities Outlined in ISO 23053
› 2.4 Analyze the Integration of Data Management in AI Systems
› 2.5 Evaluate Compliance Requirements for ISO 23053 Implementation
› 2.6 Apply ISO 23053 Framework to Real-World Machine Learning Scenarios
• 3 Risk Management in AI: Aligning with ISO 23053 — 6 classes
› 3.1 Define Key Concepts in Risk Management for AI
› 3.2 Identify ISO 23053 Risk Management Principles
› 3.3 Analyze Risk Assessment Techniques in AI Systems
› 3.4 Evaluate Risk Mitigation Strategies in Compliance with ISO 23053
› 3.5 Implement a Risk Management Plan for AI Projects
› 3.6 Review Case Studies of Risk Management in AI Implementations
• 4 Implementation Guidelines for ISO 23053 in Practice — 6 classes
› 4.1 Explore the Key Principles of ISO 23053 for AI Systems
› 4.2 Identify Stakeholders and Their Roles in ISO 23053 Implementation
› 4.3 Develop a Step-by-Step Plan for Implementing ISO 23053
› 4.4 Assess Current AI Systems Against ISO 23053 Guidelines
› 4.5 Create a Monitoring and Evaluation Framework for ISO 23053
› 4.6 Communicate Implementation Strategies to Stakeholders Effectively
• 5 Evaluation and Continuous Improvement under ISO 23053 — 6 classes
› 5.1 Identify Key Evaluation Metrics for AI Systems
› 5.2 Analyze Data Collection Methods for Continuous Improvement
› 5.3 Implement Feedback Loops in AI Development
› 5.4 Explore Case Studies of Successful AI Evaluations
› 5.5 Develop a Continuous Improvement Action Plan
› 5.6 Present Findings and Recommendations for Future AI Projects
| 5 | Machine Learning Techniques | 5 chapters · 30 classes 85 marks |
• 1 Understanding Machine Learning Fundamentals — 6 classes
› 1.1 Define and Differentiate Key Machine Learning Concepts
› 1.2 Explore the Types of Machine Learning Approaches
› 1.3 Identify Core Components of Machine Learning Models
› 1.4 Analyze the Machine Learning Lifecycle Stages
› 1.5 Evaluate Common Algorithms and Their Applications
› 1.6 Implement a Basic Machine Learning Problem Using a Framework
• 2 Data Preprocessing and Feature Engineering — 6 classes
› 2.1 Understand the Importance of Data Preprocessing
› 2.2 Explore Common Data Cleaning Techniques
› 2.3 Implement Data Normalization and Scaling Methods
› 2.4 Identify and Handle Missing Data Effectively
› 2.5 Perform Feature Selection to Improve Model Performance
› 2.6 Apply Feature Engineering Techniques to Enhance Dataset Quality
• 3 Model Selection and Training Techniques — 6 classes
› 3.1 Identify Key Factors in Model Selection
› 3.2 Compare Different Machine Learning Algorithms
› 3.3 Implement Data Preprocessing Techniques
› 3.4 Evaluate Model Performance Metrics
› 3.5 Apply Cross-Validation for Robustness
› 3.6 Tune Hyperparameters for Optimal Results
• 4 Evaluating Model Performance and Tuning — 6 classes
› 4.1 Define Key Metrics for Model Performance Evaluation
› 4.2 Implement Classification Metrics: Precision, Recall, and F1 Score
› 4.3 Analyze Regression Metrics: RMSE, MAE, and R²
› 4.4 Conduct Cross-Validation for Robust Model Testing
› 4.5 Explore Hyperparameter Tuning Techniques for Model Optimization
› 4.6 Apply Model Performance Evaluation in Real-world Scenarios
• 5 Advanced Machine Learning Techniques and Applications — 6 classes
› 5.1 Explore Neural Network Architectures for Complex Data
› 5.2 Apply Ensemble Learning Techniques to Improve Model Performance
› 5.3 Implement Transfer Learning for Tailored AI Solutions
› 5.4 Analyze Unsupervised Learning Methods for Pattern Discovery
› 5.5 Evaluate Reinforcement Learning Strategies for Dynamic Environments
› 5.6 Integrate Advanced Machine Learning Techniques in Real-World Applications
| 6 | Strategic AI Leadership | 0 chapters 65 marks |
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UKPRN 10067351 • Company 4984798 Curriculum current at the date of issue. |