Jupyter Notebooks for teaching, presentations and other simple purposes. Lecture notebooks are regularly improved.
Introduction to python in an episodic structure, ideal for teaching beginner non-IT people. Each chapter consists multiple concepts with several examples and exercises. The first 5 notebook is introduction to programming ending in object oriented paradigm. The remaining notebooks covers webscraping with minimal HTML introduction and data transformations with pandas.
Introduction to Data Science in Python. Building on the Python101 material, this set of notebooks introduces the most important basic DS concepts with several examples and exercises. The lecture's main promise is that every chapter not only introduces you a a new concept and the relevant tools to work with your data but also gives you a model to play around with. The pipeline concept is introduced in the very beginning, and you build and extend your pipeline throughout the class.
A really short introduction to webscraping and classic (tokenization, lemmatization, vectorization) and modern (word embeddings) NLP tools.
Python 101 material splitted by topics.
Python 101 reworked and advanced topics are changed from webscraping / pandas introduction to system libraries, regular expressions, parsing, and REST API topics.
High level looak at Lensa recommender system.
A talk about whether people can be predicted.
Less detailed and slightly modified computable individualites talks.
A really short presentation about neural networks.
Dev story and pitfalls of Lensa recommender system.
Scraping different sites, transforming, joining scraped data and exporting them to excel.