In my article My personal road map for learning data science in 2018 I wrote about how I try to tackle the data science knowledge sphere. Due to the fact that 2018 is slowly coming to an end I think it is time for a little wrap up.
What are the things I learned about Data Science in 2018? Here we go:
1. The difference between Data Science, Machine Learning, Deep Learning and AI
A picture says more than a thousand words.
2. The difference between supervised and unsupervised 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.
Your data doesn’t have labels. Your algorithm e.g. k-means clustering need to figure out a structure given only the data
3. The areas of applied machine learning
4. Bayes Theorem
5. 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.
6. Visualization with matplotlib
7. Math with numpy
I wrote some articles about the usage of numpy but only scraped the surface of this mighty library
8. Image manipulation with OpenCV
9. JuPyter Notebooks
Sometimes I love them sometimes I hate them. I wrote an Introduction to JuPyter Notebook
In 2018 I’ve listened to a bunch of great podcasts on iTunes: