Wuraola Oyewusi

Machine learning skills you'll gain:

• ML fundamentals: supervised vs. unsupervised learning, features, labels, and model evaluation
• Classification: heart failure outcome prediction with real clinical data
• Regression: predicting heart ejection fraction from patient features
• Feature importance analysis and feature scaling techniques
• Unsupervised learning: clustering (k-means) and dimensionality reduction
• Deep learning overview, transfer learning, and pretrained models for healthcare
• Data privacy, ethics, and career pathways in healthcare ML

Tools covered: Google Colab, scikit-learn, TensorFlow, Python for healthcare ML, model evaluation techniques

Perfect for: Healthcare professionals and data scientists entering the healthcare AI field.

Real-world healthcare projects you'll complete:

• Outpatient clinic analysis: wait times, demographics, cost patterns, and correlations
• Medication demand forecasting with time-series analysis using Prophet
• Patient experience analytics with sentiment analysis (TextBlob)
• Geospatial health facility mapping and buffer analysis with GeoPandas and folium
• Interactive healthcare dashboards and visualizations with Plotly
• Advanced data wrangling and cleaning for healthcare datasets

Tools covered: pandas, GeoPandas, Plotly, TextBlob, Prophet, folium, statistical analysis, forecasting

Perfect for: Healthcare analysts and data professionals working with clinical datasets.

Productivity tools you'll master:

• Google Gemini: writing, data exploration, image description, and text extraction from images
• Microsoft Copilot: image generation, design creation, and brand kit development with Designer
• Prompt engineering fundamentals and best practices for GenAI tools

Academic research tools you'll master:

• Elicit AI: scientific literature search and research automation
• Litmaps: seed map generation, literature discovery, and visual citation networks
• SciSpace: literature review, PDF parsing with Copilot, citation generation, and scholarly paraphrasing

Perfect for: Academic researchers, graduate students, professionals, healthcare workers, educators, and knowledge workers across all sectors.

AI administration projects you will explore:

• Draft a clinic grant proposal with ChatGPT
• Create seasonal-flu patient education materials with Copilot
• Explore staff scheduling with Gemini and patient feedback surveys with Copilot
• Analyze insurance claims denial data with ChatGPT
• Consider ethical and secure use of AI in healthcare settings

Healthcare Excel projects you'll complete:

• Patient data management: biodata forms, care coordination, demographic analysis with PivotTables
• Healthcare operations: nursing staff scheduling, lab sample tracking, Gantt chart project timelines
• Clinical applications: medication dosage calculators, symptom trackers, discharge planning checklists
• Analytics dashboards: wait time analysis, patient satisfaction surveys, disease outbreak monitoring
• Supply chain management: inventory tracking, stock forecasting for vaccination centers, dynamic pricing

Excel skills covered: PivotTables, data validation, conditional formatting, formulas (AVERAGE, MEDIAN), charts, dashboards

Perfect for: Nurses, administrators, clinical staff, and healthcare managers.

Python fundamentals you'll master:

• Core programming concepts: data types, structures (lists, dictionaries, tuples, sets)
• Python operations: arithmetic, comparisons, logical operators, and string manipulation
• Functions, conditional statements, loops, and control flow for healthcare tasks
• File handling: reading, writing, and processing healthcare data files
• Built-in functions and Python libraries for data analysis workflows
• Code documentation, error handling, and debugging techniques

Healthcare examples: Grouping medications by pharmacology class, managing dosage strengths, patient data organization

Perfect for: Nurses, physicians, pharmacists, and healthcare administrators with zero prior coding experience.

Healthcare deep learning exercises:

• Prepare X-ray image datasets for computer vision tasks
• Classify normal and pneumonia X-rays with CNNs and pretrained models
• Try zero-shot classification and object detection on X-ray images
• Examine data limitations, ethics, and diagnosis support

Portfolio projects you'll complete:

• Disease prediction: Diabetes ML model with SHAP explainability
• Sentiment analysis: Online medication reviews using transformer models
• Medical image classification: Shoulder implant X-ray detection with CNNs and transfer learning
• Text visualization: Word clouds and Scattertext for disease and medication data

Tools: Machine learning models, SHAP, transformer models, CNNs, transfer learning, word clouds, Scattertext

Perfect for: Learners building a healthcare AI portfolio.

Annotation tools and techniques you'll master:

• Computer vision: Image classification, object detection, semantic segmentation with Pigeon, CVAT, Roboflow
• NLP annotation: Named entity recognition, text classification, sentiment analysis with Universal Data Tool and Prodigy
• Advanced techniques: Polygon masking, Segment Anything Model (SAM), video annotation
• Cloud platforms: AWS SageMaker Ground Truth, Azure ML Studio, Google Vertex AI

Tools: CVAT, Roboflow Annotate, Prodigy, Universal Data Tool, AWS/Azure/GCP annotation platforms

Perfect for: Building ML skills or starting a remote annotation side hustle.

Essential Colab skills you'll master:

• AI-powered coding: Autocomplete, code generation, and Gemini AI support in notebooks
• File management: Upload/download, Google Drive mounting, URL imports, Google Cloud Storage integration
• System interactivity: GPU/TPU access, magic commands, bash commands, Python os/sys modules
• Development workflow: Create Python scripts, markdown documentation, GitHub cloning and commits

Tools: Google Colab, Gemini AI, Google Drive, Google Cloud Storage, GitHub, GPU/TPU computing

Perfect for: Data scientists, ML practitioners, and anyone needing a free cloud-based Python environment.

What you will practise:

• Preprocess and represent text with NLTK, scikit-learn, Gensim, spaCy, and Transformers
• Work with word and sentence embeddings, including Word2Vec and pretrained models
• Extract speech features such as MFCCs and spectrograms with librosa
• Apply methods including POS tagging, text classification, clustering, speech-to-text, and text-to-speech

Text-analysis projects covered:

• Identify named entities in text
• Discover themes with topic modelling
• Summarise documents and explore sentiment analysis

Advanced NLP techniques you'll master:

• Clinical Named Entity Recognition (CNER) with pretrained medical models
• Clinical Entity Resolution: abbreviation expansion and biomedical knowledge base linkage
• Medical text representation using word embeddings (fastText) and sentence encoding (Universal Sentence Encoder)
• Transformer models for clinical diagnosis prediction and masked word prediction
• Named entity recognition using transformer architectures for medical text

Tools covered: scispaCy, fastText, Universal Sentence Encoder (USE), transformer models for healthcare NLP

Practical examples covered:

• Explore a Hugging Face model for product sentiment analysis
• Generate images with the OpenAI API
• Build a conversational chatbot with the Gemini API
• Compare GitHub Models and run examples in Codespaces
• Find further AI resources and developer communities

What the course covers:

• Compare diffusion and autoregressive approaches to text generation
• Explore model architecture, types, limitations, and trade-offs
• Build and train a basic text diffusion model
• Explore pretrained language diffusion models on Hugging Face and Google Gemini

What you will practise:

• Launch JupyterLab and Jupyter AI in an Anaconda environment
• Choose a model provider and set up the Jupyternaut chat interface
• Generate and explain code, and build contextual knowledge in chat
• Generate notebooks from prompts and use AI magic commands
• Integrate Jupyter AI into a notebook workflow in the final project