What are the things I learned about Data Science in 2018? Here we go:

The difference between Data Science, Machine Learning, Deep Learning and AI

A picture says more than a thousand words.

The difference between supervised and unsupervised learning

Supervised Learning

You have training and test data with labels. Labels tell You to which e.g. class a certain data item belongs. Image you have images of pets and the labels are the name of the pets.

Unsupervised Learning

Your data doesn’t have labels. Your algorithm e.g. k-means clustering need to figure out a structure given only the data

The areas of applied machine learning

are described here: The Essence of Machine Learning and Data Science Overview

Bayes Theorem

In my article Bayes theorem I elaborated about the base rate fallacy and in naive bayes I recapped the second lesson from udacity’s UD120 Intro to Machine Learning

Precision and Recall and ROC

In my article classification: precision and recall I wrote about different useful measures to evaluate the quality of a supervised learning algorithm.

In Receiver Operating Characteristic I wrote about another useful measures the ROC.

Visualization with matplotlib

Matplotlib is a really good starting point for visualization. I wrote about it in Introduction to matplotlib, Matplotlib – Part 2, Scatterplot with matplotlib

Math with numpy

I wrote some articles about the usage of numpy but only scraped the surface of this mighty library

• Linear Algebra with numpy – Part 1
• numpy random choice
• Numpy linspace function

Image manipulation with OpenCV

Intro to OpenCV with Python

JuPyter Notebooks

Sometimes I love them sometimes I hate them. I wrote an Introduction to JuPyter Notebook

Podcasts

In 2018 I’ve listened to a bunch of great podcasts on iTunes:

• Linear digressions
• MIT Lex Fridman
• Dr. Lance Eliot

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