Fine Tuning Generative AI Models for Real World Applications
About This Course
This course equips learners with the practical and theoretical expertise needed to develop and fine-tune generative AI models using state-of-the-art deep learning architectures. Participants will explore foundational concepts in probability, statistics, and deep learning algorithms such as GANs, VAEs, and Transformers. Through hands-on implementation using leading machine learning libraries like TensorFlow, PyTorch, and Keras, learners will gain the skills to select and build suitable generative models tailored to specific problem requirements.
The course emphasizes robust data preparation practices, including dataset cleaning, de-duplication, embeddings, and tokenization, ensuring models are trained on high-quality, structured inputs. Learners will apply neural optimization techniques, train models on benchmark datasets, and use appropriate evaluation metrics to measure performance. A strong focus is placed on identifying weaknesses in generative models and applying targeted fine-tuning strategies such as supervised and parameter-efficient fine-tuning to enhance model outputs.
With the growing integration of generative AI across sectors like healthcare, finance, content creation, and software development, this course delivers in-demand competencies that are critical in today’s AI-driven economy. It prepares learners for technical roles in AI and machine learning teams, enhancing employability and supporting career advancement into more specialized or senior positions. Professionals can leverage this training to contribute to R&D projects, innovate new AI solutions, or transition into leadership roles focused on AI implementation.
This course is designed for intermediate learners with foundational knowledge in machine learning and programming, looking to deepen their skills in generative AI.
What You'll Learn
LO1: Implement generative models using deep learning algorithms and analyse their suitability for different problem requirements.
LO2: Preprocess and transform generative datasets using embeddings and tokenisation for model readiness.
LO3: Train generative AI models with neural optimisation techniques using benchmark datasets and performance metrics.
LO4: Identify model weaknesses through evaluation and propose targeted fine-tuning strategies for improvement.
Course Outline:
LU 1: Generative AI Theory
T1: Probability theory and statistics (e.g., latent variables, probabilistic modelling)
T2: Deep learning theory and algorithms (e.g., GANs, VAEs, Transformers)
T3: Machine learning libraries (e.g., TensorFlow, PyTorch, Keras)
T4: Implement generative models based on existing architectures
T5: Analyse problem statements and requirements to select and implement appropriate generative models
LU 2: Generative AI Data Preparation
T1: Common dataset formats and evaluation methodologies for generative tasks
T2: Data pre-processing, de-duplication and cleaning techniques (including understanding of training data requirements for AI models, common data quality issues)
T3: Embeddings and tokenisation
T4: Preprocess and prepare data for generative training (e.g., clean and format datasets, use libraries (e.g., Pandas, NumPy) for data manipulation, split data into training, validation and test sets)
LU 3: Generative AI Model Training
T1: Optimisation techniques for training neural networks
T2: Parallel cluster training and inference
T3: Loss functions and evaluation metrics for generative tasks
T4: Train generative models on benchmark datasets
LU 4: Generative AI Model Fine Tuning
T1: Fine-tuning techniques (e.g., supervised fine-tuning, parameter-efficient fine-tuning, perform inference)
T2: Identify limitations and propose initial improvements to models"
Entry Requirements
Knowledge and Skills
• Able to operate using computer functions
• Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)
Attitude
• Positive Learning Attitude
• Enthusiastic Learner
Experience
• Minimum of 1 year of working experience
Target age group: 21-65 years old