Complete L2D Course

• Algorithmic thinking
• Variables, types and operations
• Conditional statements
• Arrays, tuples, lists and indexing
• Iterations: for and while loops
• Dictionaries: associative arrays
• Functions: uses, applications and defining your own customised Python functions

Basic Python:

• • Algorithmic thinking
  • Variables, types and operations
  • Conditional statements
  • Arrays, tuples, lists and indexing
  • Iterations: for and while loops
  • Dictionaries: associative arrays
  • Functions: uses, applications and defining your own customised Python functions

Data Handling:

• • • Import and characterisation of data as Pandas dataframes
    • Basic statistics
    • Data visualisation with Matplotlib
    • Bivariate and multivariate analyses
    • The Pearson correlation coefficient and correlation matrix
    • Image handling: import and characterisation of image data (greyscale and colour)
    • Image masking and segmentation
    • Time series: visualisation, filtering and Fourier transform
    • Relationships between time series data

• Classification: preparing data for classification, training classifier models.
• State space plot of model predictions
• Prediction probabilities and feature importance
• Complex training and testing of data
• Comparison of different model classes
• Stratified shuffle split
• Evaluation of classification using AUC and ROC curves
• Metrics for model evaluation
• Permutation scoring and confusion matrices
• Normalisation and hyperparameter tuning
• Refinement and progressive adjustment

• Gaussian Mixture Models (GMMs) and sci-kit learn
• Clustering and automated data labelling
• Quantitative scoring using ground truth
• Introductions to the concept and pitfalls of clustering techniques
• GMMs in medical image segmentation and object detection
• Dimensionality reduction (reducing computational workloads of large high-dimensionality datasets)
• PCA (Principal Component Analysis)