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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.
Decision Tree versus Random Forest, and Hyperparameter Optimisation
Decision trees, with their elegant simplicity and transparency, stand in stark contrast to the robust predictive power of Random Forest, an ensemble of 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.
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.
Generate Music with AI
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.
Audio Signal Processing with Python's Librosa
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.
Machine Learning Tests using the Titanic dataset
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!
TensorFlow: Evaluating the Saved Bird Species Prediction Model
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.
TensorFlow: Transfer Learning (Fine-Tuning) in Image Classification
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.
Mining Microblogs for Culture-awareness in Web Adaptation
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.
Tools and Data to Experiment with Machine Learning
Python open-source library scikit-learn provides a comprehensive selection of machine learning techniques (regression, classification, clustering), feature selection, metrics, preprocessing, and other functionality. At this moment, Scikit-learn, is lacking deep learning functionality; however, we can use TensorFlow with the Scikit Flow wrapper for creating neural networks using the Scikit-learn approach.
Deep Learning vs Machine Learning
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.