Tag: ML (Machine Learning)

Explainable AI with Python: SHAP and LIME for Model Transparency

Elena Daehnhardt · 10 Jul, 2026

Use SHAP and LIME in Python to analyse feature impact, diagnose model behaviour, and communicate decisions clearly. Read more...

LoRA fine-tuning wins

Elena Daehnhardt · 16 Oct, 2025

You no longer need to retrain entire language models. LoRA allows you to teach new capabilities via tiny adapters. Here is the architectural code, deployment cheat sheets, and production pitfalls. Read more...

Cross-Validation Techniques

Elena Daehnhardt · 13 Mar, 2025

Building a machine learning model is easy; proving it actually works on unseen data is the hard part. In this post, we cover cross-validation techniques—from traditional K-Fold to Stratified and Time-Series splits—using hands-on examples in scikit-learn. Read more...

Bias-Variance Challenge

Elena Daehnhardt · 10 Nov, 2023

In machine learning, we usually start from a simple baseline model and progressively adjust its complexity until we reach that spot with the best model performance. How can we do this? Let's detail the most essential machine learning concepts and the bias-variance challenge. Read more...

Decision Tree versus Random Forest, and Hyperparameter Optimisation

Elena Daehnhardt · 6 Nov, 2023

Decision trees, with their elegant simplicity and transparency, stand in stark contrast to the robust predictive power of **Random Forest**, an ensemble of decision trees. In this post, we compare the key distinctions, advantages, and trade-offs between these two approaches. We will use Scikit-Learn for training and testing both models and also perform hyperparameter optimisation to find both model parameters for improved performance. Read more...

Machine-Learning Process

Elena Daehnhardt · 30 Oct, 2023

The machine learning process involves a series of steps and activities designed to develop and deploy machine learning models to solve specific problems or make predictions. To simplify, we create programs that take in data and produce desired results in machine learning. There are several stages in the machine-learning process that we briefly describe in this post. Read more...

Generate Music with AI

Elena Daehnhardt · 24 Aug, 2023

In this post, we will get into music generation with AI. We will briefly explore existing AI applications generating audio. We will explore transformer usage while coding music generation with HuggingFace transformers in Python. Read more...

Audio Signal Processing with Python's Librosa

Elena Daehnhardt · 5 Mar, 2023

In this post, I focus on audio signal processing and working with WAV files. I apply Python's Librosa library for extracting wave features commonly used in research and application tasks such as gender prediction, music genre prediction, and voice identification. To succeed in these complex tasks, we need a clear understanding of how WAV files can be analysed, which I cover in detail with handy Python code snippets. Read more...

Machine Learning Tests using the Titanic dataset

Elena Daehnhardt · 10 Feb, 2023

In this post, we created and evaluated several machine-learning models using the Titanic Dataset. We have compared the performance of the Logistic Regression, Decision Tree and Random Forest from Python's library scikit-learn and a Neural Network created with TensorFlow. The Random Forest Performed the best! Read more...

TensorFlow: Evaluating the Saved Bird Species Prediction Model

Elena Daehnhardt · 2 May, 2022

In this post, I have described the process of in-depth model evaluation. I have reused the previously created EffecientNetB0 model, which is fine-tuned with the 400 Bird Species Kaggle dataset. As a result, I have found out which bird species are not well predicted. Read more...

TensorFlow: Transfer Learning (Fine-Tuning) in Image Classification

Elena Daehnhardt · 6 Apr, 2022

We used a 400 species birds dataset for building bird species predictive models based on EffeicientNetB0 from Keras. The baseline model showed already an excellent Accuracy=0.9845. However, data augmentation did not help in improving accuracy, which slightly lowered to 0.9690. Further, this model with a data augmentation layer was partially unfrozen, retrained with a lower learning rate, and reached an Accuracy=0.9850. Read more...

Mining Microblogs for Culture-awareness in Web Adaptation

Elena Daehnhardt · 3 Jan, 2022

In this post, I am briefly writing up about what I did in my PhD research at Heriot-Watt University and the main idea behind the thesis. Read more...

Tools and Data to Experiment with Machine Learning

Elena Daehnhardt · 19 Oct, 2021

Python open-source library scikit-learn provides a comprehensive selection of machine learning techniques (regression, classification, clustering), feature selection, metrics, preprocessing, and other functionality. Scikit-learn has some basic neural-network support through MLPClassifier, but for real deep learning you are better off reaching for TensorFlow's Keras API rather than the now-defunct Scikit Flow wrapper. Read more...

Deep Learning vs Machine Learning

Elena Daehnhardt · 16 Oct, 2021

Artificial Intelligence (AI) is a field of computer science. AI provides methods and algorithms to mimic human intelligence, reasoning, and decision-making and provide insights, which businesses could use in research or industry to build new exciting and innovative products or services. Machine Learning (ML) is a subset of AI with algorithms that learn from data. In this post, we sort out the differences between AI and ML. Read more...

Explainable AI with Python: SHAP and LIME for Model Transparency Elena Daehnhardt · 10 Jul, 2026 Use SHAP and LIME in Python to analyse feature impact, diagnose model behaviour, and communicate decisions clearly. Read more...
LoRA fine-tuning wins Elena Daehnhardt · 16 Oct, 2025 You no longer need to retrain entire language models. LoRA allows you to teach new capabilities via tiny adapters. Here is the architectural code, deployment cheat sheets, and production pitfalls. Read more...
Cross-Validation Techniques Elena Daehnhardt · 13 Mar, 2025 Building a machine learning model is easy; proving it actually works on unseen data is the hard part. In this post, we cover cross-validation techniques—from traditional K-Fold to Stratified and Time-Series splits—using hands-on examples in scikit-learn. Read more...
Bias-Variance Challenge Elena Daehnhardt · 10 Nov, 2023 In machine learning, we usually start from a simple baseline model and progressively adjust its complexity until we reach that spot with the best model performance. How can we do this? Let's detail the most essential machine learning concepts and the bias-variance challenge. Read more...
Decision Tree versus Random Forest, and Hyperparameter Optimisation Elena Daehnhardt · 6 Nov, 2023 Decision trees, with their elegant simplicity and transparency, stand in stark contrast to the robust predictive power of Random Forest, an ensemble of decision trees. In this post, we compare the key distinctions, advantages, and trade-offs between these two approaches. We will use Scikit-Learn for training and testing both models and also perform hyperparameter optimisation to find both model parameters for improved performance. Read more...
Machine-Learning Process Elena Daehnhardt · 30 Oct, 2023 The machine learning process involves a series of steps and activities designed to develop and deploy machine learning models to solve specific problems or make predictions. To simplify, we create programs that take in data and produce desired results in machine learning. There are several stages in the machine-learning process that we briefly describe in this post. Read more...
Generate Music with AI Elena Daehnhardt · 24 Aug, 2023 In this post, we will get into music generation with AI. We will briefly explore existing AI applications generating audio. We will explore transformer usage while coding music generation with HuggingFace transformers in Python. Read more...
Audio Signal Processing with Python's Librosa Elena Daehnhardt · 5 Mar, 2023 In this post, I focus on audio signal processing and working with WAV files. I apply Python's Librosa library for extracting wave features commonly used in research and application tasks such as gender prediction, music genre prediction, and voice identification. To succeed in these complex tasks, we need a clear understanding of how WAV files can be analysed, which I cover in detail with handy Python code snippets. Read more...
Machine Learning Tests using the Titanic dataset Elena Daehnhardt · 10 Feb, 2023 In this post, we created and evaluated several machine-learning models using the Titanic Dataset. We have compared the performance of the Logistic Regression, Decision Tree and Random Forest from Python's library scikit-learn and a Neural Network created with TensorFlow. The Random Forest Performed the best! Read more...
TensorFlow: Evaluating the Saved Bird Species Prediction Model Elena Daehnhardt · 2 May, 2022 In this post, I have described the process of in-depth model evaluation. I have reused the previously created EffecientNetB0 model, which is fine-tuned with the 400 Bird Species Kaggle dataset. As a result, I have found out which bird species are not well predicted. Read more...
TensorFlow: Transfer Learning (Fine-Tuning) in Image Classification Elena Daehnhardt · 6 Apr, 2022 We used a 400 species birds dataset for building bird species predictive models based on EffeicientNetB0 from Keras. The baseline model showed already an excellent Accuracy=0.9845. However, data augmentation did not help in improving accuracy, which slightly lowered to 0.9690. Further, this model with a data augmentation layer was partially unfrozen, retrained with a lower learning rate, and reached an Accuracy=0.9850. Read more...
Mining Microblogs for Culture-awareness in Web Adaptation Elena Daehnhardt · 3 Jan, 2022 In this post, I am briefly writing up about what I did in my PhD research at Heriot-Watt University and the main idea behind the thesis. Read more...
Tools and Data to Experiment with Machine Learning Elena Daehnhardt · 19 Oct, 2021 Python open-source library scikit-learn provides a comprehensive selection of machine learning techniques (regression, classification, clustering), feature selection, metrics, preprocessing, and other functionality. Scikit-learn has some basic neural-network support through MLPClassifier, but for real deep learning you are better off reaching for TensorFlow's Keras API rather than the now-defunct Scikit Flow wrapper. Read more...
Deep Learning vs Machine Learning Elena Daehnhardt · 16 Oct, 2021 Artificial Intelligence (AI) is a field of computer science. AI provides methods and algorithms to mimic human intelligence, reasoning, and decision-making and provide insights, which businesses could use in research or industry to build new exciting and innovative products or services. Machine Learning (ML) is a subset of AI with algorithms that learn from data. In this post, we sort out the differences between AI and ML. Read more...