Complete L2D Course

Basic Python:

The Basic Python component of the L2D course offers learners a comprehensive introduction to programming in Python. Some of the key study areas covered in this module are:

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

 

Data Handling:

An introduction to importing, handling and analysing a variety of different data types, in Python. Key study areas include:

• 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

Note: This module (or its equivalent) is a compulsory prerequisite to taking the Machine Learning component of L2D.

 

Supervised Machine Learning:

Key areas taught in L2D's exploration of Supervised Machine Learning include:

• 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

 

Unsupervised Machine Learning:

Key areas taught in L2D's exploration of Unsupervised Machine Learning include:

• 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)

Note: L2D's Basic Python and Data Handling modules (or their equivalent) are compulsory requirements to taking our Machine Learning course.