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 Christian’s t-shirt size from David’s and Julia’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 “Feature Scaling”
As we already introduced Precision and Recall the ROC curve is another way of looking at the quality of classification algorithms.
ROC stands for Receiver Operating Characteristic
The ROC curve is created by plotting the true positive rate (TPR) on the y-axis against the false positive rate (FPR) on the x-axis at various threshold settings.
You already know the TPR as recall or sensitivity.
Continue reading “Receiver Operating Characteristic”
Pandas is a data analyzing tool. Together with numpy and matplotlib it is part of the data science stack
You can install it via
pip install pandas
Working with real data
The data set we are using is the astronauts data set from kaggle:
Continue reading “Introduction to Pandas”
Applying for a data scientist job offer? Tired of writing the same old curriculum vitae?
Why not showing your data visualization skills directly in your application?
Continue reading “Curriculum Vitae for Data Scientists”
To work with OpenCV from python, you need to install it first:
pip install opencv-python
After we import cv2 we can directly work with images like so:
img = cv2.imread("doc_brown.png")
Continue reading “Intro to OpenCV with Python”
Too confused of the confusion matrix?
Let me bring some clarity into this topic!
Continue reading “Confusion Matrix”
With numpy you can easily create test data with random_integers and randint.
numpy.random.randint(low, high=None, size=None, dtype='l')
numpy.random.random_integers(low, high=None, size=None)
random_integers includes the high boundary while randint does not. Continue reading “numpy random choice”
In the realms of Data Science you’ll encounter sooner or the later the terms “Precision” and “Recall”. But what do they mean?
Living together with little kids You very often run into classification issues:
My daughter really likes dogs, so seeing a dog is something positive. When she sees a normal dog e.g. a Labrador and proclaims: “Look, there is a dog!”
That’s a True Positive (TP) Continue reading “Classification: Precision and Recall”
One part of my bucket list for 2018 is finishing the Udacity Course UD120: Intro to Machine Learning.
The host of this course are Sebastian Thrun, ex-google-X and founder of Udacity and Katie Malone, creator of the Linear digressions podcast.
The course consists of 17 lessons. Every lesson has a couple of hours of video and lots and lots of quizzes in it. Continue reading “UD120 – Intro to Machine Learning”
Lesson 2 of the Udacity Course UD120 – Intro to Machine Learning deals with Naive Bayes classification. Continue reading “Lesson 2: Naive Bayes”