Chapter 10 Uncategorized topics

This chapter contains a list of topics which I consider interesting but I am unable to currently categorize them anywhere.

Spark Bayesian methods Text processing Topic modelling Shell and Bash?

Be Curious You are already at the level where curiosity will be what keeps you growing. With this chapter we are starting with a new practice that I will be leaving recommendations for additional sources that you can read

Is Machine Learning Unethical or Racist?

Are data scientists evil? Is there a reason why to be scared of these stalkers?

Propensity models Can two models and their respective probabilities be really compared?

Customer and user You need to distinguish between the two

The falacy of absolute numbers For many years what matters a lot is a conversion rate or a success rate. I recommend though to be careful with it.

Should I focus on how my code looks? I have a saying: “People have feelings. They feel when you code like sh_t.”

Long and wide format of data

A journalist asks a programmer: What makes a code bad? Programmer: No comment

Parallel programming

Self-organizing maps

How does my PC view features? Numerical and categorical features. This needs to come before Supervised Learning as I am explaning classification and regression. No it doesn’t, it is explained only conceptually.

Posted by u/victor_stefan to r/ProgrammerHumor, Reddit.

(#fig:machine learning)Posted by u/victor_stefan to r/ProgrammerHumor, Reddit.

What is information leakage? One of the steps we need to ensure is to prevent information leakage. This happens in cases, where our independent features contain some leaked (or spoiler) information about our target feature. Let me give you an example.

What is correlation and causality?

Make an example with babies and birds.