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
Continue reading “10 things I didn’t know about Data Science a year ago”
What is Feature Scaling?
Feature Scaling is an important pre-processing step for some machine learning algorithms.
Imagine you have three friends of whom you know the individual weight and height.
You would like to deduce Chris’ T-shirt size from Cameron’s and Sarah’s by looking at the height and weight.
||Height in m
||Weight in kg
One way You could determine the shirt size is to just add up the weight and the height of each friend. You would get: Continue reading “Lesson 10: Feature Scaling”
Lesson 2 of the Udacity Course UD120 – Intro to Machine Learning deals with Naive Bayes classification. Continue reading “Lesson 2: Naive Bayes”
Data Science tries to answer one of the following questions:
- Classification -> “Is it A or B?”
- Clustering -> “Are there groups which belong together?”
- Regression -> “How will it develop in the future?”
- Association -> “What is happening very often together?”
There are two ways to tackle these problem domains with machine learning:
- Supervised Learning
- 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
I got confused by all the buzzwords: data science, machine learning, deep learning, neural nets, artificial intelligence, big data, and so on and so on.
As an engineer I like to put some structure to the chaos. Inspired by Roadmap: How to Learn Machine Learning in 6 Months and Tetiana Ivanova – How to become a Data Scientist in 6 months a hacker’s approach to career planning I build my own learning road map for this year: Continue reading “My personal road map for learning data science in 2018”