How many tools does one have to master in order to call herself a data scientist?
I’m about to start blogging about data science again, and I wanted to preface it with a little story about my journey towards Data Science.
If you haven’t lived under a rock you’ve heard about neural networks.
In my previous post I’ve shared my Jupyter notebook with an attempt to predict the survival of Titanic passengers based on the Kaggle dataset for beginners.
I’ve been hesitant to write this blog post. On one hand, I managed to build my first (i.
In arithmetic operations involving matrices and vectors (arrays) their shapes have to be compatible.
Following my linear regression cheat sheet, here’s a logistic regression cheat sheet.
I’ll just keep it here. My linear regression cheat sheet made while following a great ML course from Stanford University.
When learning something new these days one is presented with an overwhelming amount of sources to choose from: books, courses, articles, documentation, stackoverflow, etc.
I find that Jupiter Notebook is a really nice tool for dealing with data, and for learning and exploring the capabilities of various libraries.