Data Science
Data analysis, machine learning, and statistical modelling with Python.
A comprehensive introduction to machine learning covering supervised and unsupervised learning, key algorithms (linear regression, decision trees, SVMs, neural networks), model evaluation, feature engineering, and the end-to-end ML workflow with Python and Scikit-Learn.
A comprehensive introduction to deep learning covering neural network fundamentals, PyTorch, CNNs, RNNs, transfer learning, generative models, regularisation, deployment, and ethics with hands-on Python code examples throughout.
A comprehensive introduction to data visualisation covering design principles, chart selection, and the Python visualisation toolkit — Matplotlib, Seaborn, Plotly, Pandas, Streamlit, and Folium. Learn to create static, interactive, and geospatial visualisations that tell compelling data stories.
A hands-on introduction to data science covering the Python toolkit — NumPy, Pandas, Matplotlib, and Scikit-Learn. Learn to clean, explore, visualise, and model data through practical examples and real-world datasets.
A comprehensive introduction to Natural Language Processing covering text preprocessing, text representation, classification, sentiment analysis, named entity recognition, language models, sequence-to-sequence architectures, transformers, and the ethical considerations of NLP — with hands-on Python examples using NLTK, spaCy, and Hugging Face Transformers.