Data Science & Machine Learning
Complete data science learning path from foundations through visualisation, machine learning, deep learning, and natural language processing.
Introduction to Data Science with Python
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.
Introduction to Data Visualisation
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.
Introduction to Machine Learning
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.
Introduction to Deep Learning
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.
Introduction to Natural Language Processing
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.
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