CURRICULUM

• Take your AI and Machine Learning skills to a new level!

• 5x days of intense, quality and fun learning.

• Aesthetically pleasing in print, digital and audio formats.

• Horizontal learning experience for more immersive study.

• 80+ Hours of learning rich, targeted and in-depth content without the fluff.

• Content, demos, labs and exercises prepared by our professionals, at every stage and topic of the course for beginners and advanced levels.

• Official and only learning courseware, for the C|AAINE™ online and on-site course.

Course Formats

• Digital Courseware Book: for instructor-led courses (online or on-site) and for eLearning platform.

• Print Courseware Book: for instructor-led courses (online or on-site) only and on-demand.

• Course delivery:

  • Online self-paced self-study (via our Obi.Academy eLearning audio/visual platform)

  • Online course delivery (via our Obi.Academy eLearning audio/visual platform)

  • Onsite course delivery (via our partners Startel B.V. in the Netherlands and South Africa)

Course Delivery:

The Certified Applied Artificial Intelligence and Natural Language Processing Expert (C|AAINE™) course is available online as a self-paced self-study course or as an instructor-led online and onsite, with our approved training partners (ATPs) in the UK and Europe. Obipixel Ltd is actively in discussions with potential partners in the USA, Africa and Asia. To enquire about becoming an approved training provider and affiliate or a learner/attendee on our courses with our partners with Obi.Academy please contact us for further details.

Course Breakdown

Day 1 – Foundations of NLP and Generative AI

Module 1: Introduction to Generative AI and NLP Techniques

• 1.1 What is Generative AI?

• 1.2 Core NLP Concepts: Tokens, Embeddings, Transformers, Attention

• 1.3 Setting Up Tools: LLaMA 3, Mistral, APIs, Platforms

• 1.4 Preprocessing and Normalisation Techniques

Module 2: Foundational NLP with Pre-trained Models

• 2.1 Tokenisation, Embeddings

• 2.2 RNNs, Word2Vec, GloVe

• 2.3 Handling Missing/Noisy Data

• 2.4 Feature Engineering (TF-IDF)

Labs:

• Lab 0: Load and Interact with a Pre-trained LLaMA 3 Model

• Lab 1: Tokenisation and Embeddings

• Lab 2: Text Preprocessing

• Lab 7: RNNs

• Lab 8: Word2Vec

• Lab 9: GloVe

Day 2 – Fine-Tuning and Customisation of AI Models

Module 3: Fine-Tuning LLMs

• 3.1 What is Advanced Fine-Tuning?

• 3.2 Instruction-Tuning

• 3.3 Domain Adaptation

• 3.4 Parameter-Efficient Fine-Tuning

Module 4: Applied Prompt Engineering

• 4.1 Prompt Engineering for Custom Tasks

• 4.2 Chain-of-Thought Reasoning

• 4.3 Zero-shot, Few-shot, Multi-shot Prompts

• 4.4 Evaluation and Deployment

Labs:

• Lab 3: Sentiment Analysis Using LLMs

• Lab 4: Summarisation with LLaMA 3

• Lab 5: Advanced Fine-Tuning Techniques

• Lab 10: Attention Mechanisms in Transformer Models

Day 3 – Advanced Applications: NER, Multilingual, Multimodal

Module 5: Named Entity Recognition (NER)

• 5.1 What is Entity Recognition?

• 5.2 NER Models

• 5.3 Applications of Entity Recognition

• 5.4 Steps, Challenges & Ethics

Module 6: Translation & Multilingual Summarisation

• 6.1 What is Machine Translation?

• 6.2 Challenges in Translation

• 6.3 Multilingual Summarisation

• 6.4 Translation Pipelines & Real-World Applications

Module 7: Multimodal Content Generation

• 7.1 What is Multimodal Generation?

• 7.2 Multimodal Models (Gemini, BLIP, Grok)

• 7.3 Applications in Industry

• 7.4 Pipelines and Ethical Challenges

Labs:

• Lab 6: Entity Recognition in Real Applications

• Lab 11: Translation Tasks

• Lab 12: Multilingual Summarisation

• Lab 13: Summarisation with LLaMA 3

• Lab 14: Translation with LLaMA 3

• Lab 15: Fine-tuning LLaMA 3 for Specific Domains

Day 4 – Deployment and Dashboarding

Module 8: Model Deployment (Web, Python, C#)

• 8.1 What is Model Deployment?

• 8.2 Deployment Workflow

• 8.3 Web, Python, and C# Integrations

• 8.4 Case Studies & Ethical Considerations

Module 9: Building Multimodal Dashboards

• 9.1 What are Multimodal Dashboards?

• 9.2 Components and Frameworks (Streamlit, Dash, Blazor)

• 9.3 Connecting LLMs to Dashboards

• 9.4 Use Cases and Best Practices

Labs:

• Lab 16: Text Generation with Mistral

• Lab 17: Multilingual Analysis with Mistral

• Lab 18: Attention Mechanisms in Mistral

• Lab 19: OpenAI API Integration in C#

• Lab 20: Summarisation with OpenAI API in C#

Day 5 – Pipelines, Real-Time Systems, and Final Integration

Module 10: Comprehensive Pipeline Implementation

• 10.1 What is a Comprehensive Pipeline?

• 10.2 Building NLP Pipelines

• 10.3 Multimodal and Real-Time Pipelines

• 10.4 Deployment and Monitoring

Module 11: Deploying Models to Applications (Web, Python, C#)

• 11.1 Deployment Workflow

• 11.2 Web App Integration

• 11.3 Python & C# API Integration

• 11.4 Case Studies

Module 12: Building Multimodal Dashboards

• 12.1 Components of a Multimodal Dashboard

• 12.2 Frameworks for Development

• 12.3 Connecting Models to Dashboards

• 12.4 Sector-specific Use Cases

Module 13: Full Pipeline Case Studies

• 13.1 Real-Time Threat Detection (Cybersecurity)

• 13.2 Financial Forecasting Pipeline

• 13.3 Monitoring, Scaling and Evaluation

Labs:

• Lab 21: Real-Time Pipeline with LLMs

• Lab 22: Build a Multimodal Dashboard with LLaMA 3 + Streamlit

• Lab 23: Deploy and Monitor a Full Pipeline

Examination

The examination has been strategically developed in-house and is only offered by Obipixel Ltd at Obi.Academy. This ensures the best quality possible and the most intensive and rigorous testing of our students, without compromise. Many examination providers do not adhere to this focus, and thus examination questioning is diluted and often not even relevant. Once our students have learnt their subject matter using the Certified Applied Artificial Intelligence and Natural Language Processing Expert (C|AAINE™) Academy Guide and they have chosen to attend our online or onsite courses, they will be tested correctly and fairly without worrying about content that is not relevant and not meant to be in their examination, which is such a systemic problem in today’s examinations across so many vendor products.

Duration: 4 hours
Pass Mark: 75%
Proctoring: Exam is proctored online with webcam and audio monitoring.
Questions: 150 (from a pool of 1000 randomly rotated questions in a blockchain for absolute control).
Exam Voucher: Available for order and provided by Obi.Academy and any Approved Training Provider. Vouchers are available at £200/voucher/student.
Achievement: Certification (for life), Certificate and Digital Badge.