Machine learning in Python

The training is intended for people who want to start using machine algorithms in practice.

The participants are acquainted with the basics of using high-level machine learning algorithms, environment and the packages being used in machine learning, as well as methods of data preprocessing.

1. Practical Introduction
   • PyCharm environment
   • The basics of NumPy
   • Data loading
   • Pandas basics
   • "Hello world" of machine learning – iris flower classification

2. Data preparation
   • The basics of data exploration
   • Fundamentals of data visualisation
   • Feature encoding
   • Dealing with missing data
   • Feature standardarisation
   • Feature selection/dimensionality reduction

3. 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)

4. Classical algorithms of machine learning
   • k-nearest neighbors algorithm
   • Decision trees
   • Random forest
   • Support Vector Machine (SVM)
   • Clustering: k-means

5. 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

Knowledge of Python at the basic level and theoretical aspects of machine learning.

• Language: English