hands_on_python_for_ds

This repository contains the didactic material illustrated during the lesson of:

• PhD @ IMT School for advanced studies Lucca
• Master II Livello - Data Science and Statistical Learning (MD2SL)

The series of lessons (more or less)

Program

1. Introduction to Python for data science

• Introduction to pandas and jupyter
• Dataframes, manipulation, and typical operations
• Reading from files
• Plotting libraries

2. Unsupervised learning (some example)

• Introduction to unsupervised learning
• Examples with k-means
• Examples with hierarchical clustering
• Examples with density based clustering

3. Dimensionality reduction

• Introduction
• Principal component analysis (PCA)
• t-distributed stochastic neighbor embedding (t-SNE)

4. Supervised learning

• Scikit-learn for classification/regression
• Pipeline and data handling
• Random Forest / XGBoost / SVM / etc.
• Classification evaluation

5. Neural networks and deep learning

• Introduction and recap
• Implementation in Python
• Deep learning:
  • Introduction to Pytorch
  • RNN
  • CNN

6. Advanced on Deep Learning

• Attention Mechanism
• VAE
• Transformer