Level 5 Diploma in Artificial Intelligence and Machine Learning

1. Advanced Principles of AI & Machine Learning Core frameworks, methodologies, and applications in modern AI systems.

2. Python Programming for AI & ML Advanced syntax, libraries (TensorFlow, PyTorch, Scikit‑Learn), and modular programming.

3. Data Engineering & Big Data Analytics Data pipelines, preprocessing, distributed systems, and cloud‑based analytics.

4. Supervised & Unsupervised Learning Models Regression, classification, clustering, and reinforcement learning.

5. Neural Networks & Deep Learning Architectures CNNs, RNNs, LSTMs, and advanced neural network design.

6. Natural Language Processing (NLP) Text mining, sentiment analysis, and language models.

7. Computer Vision & Image Recognition Object detection, image classification, and applied vision systems.

8. Model Training, Optimization & Evaluation Hyperparameter tuning, cross‑validation, accuracy metrics, and performance optimization.

9. Audit‑Ready Documentation & Compliance Standards Technical documentation, GDPR compliance, and ISO/IEC AI frameworks.

10. Case Studies & Practical AI/ML Projects Real‑world AI applications such as predictive analytics, chatbots, and autonomous systems.

• Builds competence in AI engineering and machine learning systems

• Enhances compliance with RQF, BCS, IEEE, ISO/IEC AI, and W3C standards

• Strengthens skills in deep learning, NLP, and computer vision

• Provides tools for audit‑ready AI projects and compliance frameworks

• Offers recognized certification to support careers in AI engineering, data science, and IT leadership

• Learners progressing from Level 4 Diploma in Artificial Intelligence and Data Analytics (AIDA‑414)

• Students seeking advanced careers in AI, machine learning, and data science

• IT professionals aiming to formalize their expertise with advanced certification

• Assessment Type: Written exam + AI/ML project + viva

• Format: MCQs, applied coding tasks, project submission, and oral defense

• Total Questions: 90 theory + 1 project + 1 viva

• Passing Score: 70%

• Duration: 8–10 weeks (150–180 hours total)

• Certification: Level 5 Diploma in Artificial Intelligence and Machine Learning (AIML)

To deliver this Qualification, HiQual UK Approved ATPs must demonstrate the capability to deliver, assess, and internally quality assure qualifications in line with recognised regulatory principles and the expectations of the Regulated Qualifications Framework (RQF).

Approved centres must operate effective systems to ensure the validity, reliability, fairness, consistency, and security of assessment.

1. Centre Recognition and Legal Compliance

Centres must be formally recognised by HiQual UK prior to the delivery or assessment of any qualification. To maintain recognition, centres must: Be a legally constituted organisation operating in compliance with applicable legislation and regulatory requirements. Demonstrate effective governance, management oversight, and clear lines of accountability. Comply with all HiQual UK policies, procedures, and conditions of centre recognition. Notify HiQual UK promptly of any material changes that may affect delivery, assessment, or internal quality assurance arrangements.

2. Resources, Facilities, and Learning Environment

Centres must ensure that sufficient and appropriate resources are in place to support learning and assessment. This includes: Learning environments appropriate to the mode of delivery, including classrooms and, where applicable, specialist or practical facilities. Access to learning and assessment resources that enable learners to meet qualification outcomes. Secure systems for managing learner data, assessment records, and certification claims. Arrangements that support equality of access and reasonable adjustments for learners where required.

3. Staff Competence and Occupational Expertise

Centres must ensure that all staff involved in delivery, assessment, and internal quality assurance are competent and suitably qualified. Centres must: Appoint tutors with appropriate subject knowledge, teaching competence, and relevant occupational or professional experience. Ensure assessors are trained and competent in applying HiQual UK assessment requirements and standards. Appoint a qualified Internal Quality Assurer (IQA) responsible for monitoring assessment practice and decisions. Maintain records of staff qualifications, experience, training, and continuing professional development (CPD).

4. Assessment Practice and Internal Quality Assurance (IQA)

Centres must operate robust internal quality assurance systems to ensure assessment integrity. Centres must: Ensure assessment is valid, fit for purpose, and conducted in line with HiQual UK requirements. Implement effective IQA procedures to monitor assessor performance and confirm the consistency of assessment decisions. Maintain accurate, complete, and auditable records of learner registration, assessment evidence, and outcomes. Carry out regular internal reviews and standardisation activities to support continuous improvement.

5. Integrity, Risk Management, and Malpractice

Centres must take appropriate measures to protect the integrity of assessment. Centres must: Maintain policies and procedures for the prevention, identification, and management of malpractice and maladministration. Ensure secure handling, storage, and retention of assessment materials and learner evidence. Report any suspected or confirmed malpractice to HiQual UK in accordance with published procedures.

6. Health, Safety, Safeguarding, and Learner Protection

Centres must provide a safe, inclusive, and supportive learning environment. Centres must: Comply with applicable health and safety and safeguarding legislation. Conduct risk assessments for learning activities, particularly where practical or technical work is involved. Maintain procedures to safeguard learner welfare and wellbeing.

7. Learner Information, Support, and Fair Treatment

Centres must ensure learners are informed, supported, and treated fairly. Centres must: Provide clear and accurate information on programme requirements, assessment methods, and certification. Ensure learners receive timely and constructive feedback on assessment outcomes. Operate transparent complaints and appeals procedures aligned with HiQual UK requirements. Manage learner information securely in compliance with data protection legislation.