Training agenda
  1. Practical Introduction
    • PyCharm environment
    • The basics of NumPy
    • Data loading
    • Pandas basics
    • "Hello world" of machine learning – iris flower classification
  1. Data preparation
    • The basics of data exploration
    • Fundamentals of data visualisation
    • Feature encoding
    • Dealing with missing data
    • Feature standardarisation
    • Feature selection/dimensionality reduction
  1. The basics of machine learning
    • Spliting data into training, validation and test sets
    • Linear regression
    • Logistic regression
    • Model evaluation
    • Randomness and  reproducibility
    • Cross-validation
    • Hyperparameter optimization (grid search, random search)
  1. Classical algorithms of machine learning
    • k-nearest neighbors algorithm
    • Decision trees
    • Random forest
    • Support Vector Machine (SVM)
    • Clustering: k-means
  1. Artificial neural networks
    • Implementation of neural networks using scikit-learn package
    • Batch and online learning
    • Introduction to deep learning
    • Implementation of deep neural network with  Keras package