## 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.

Name Height in m Weight in kg T-Shirt size
Julia 1.58 52 Small
David 1.79 79 Large
Christian 1.86 64 ?

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”

## ROC Curve

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.

## Introduction to Pandas

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:

## Curriculum Vitae for Data Scientists

Applying for a data scientist job offer? Tired of writing the same old curriculum vitae?

## Intro to OpenCV with Python

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:

```import cv2
```

## Too confused of the confusion matrix?

Let me bring some clarity into this topic!

## numpy random choice

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”

## Classification: Precision and Recall

In the realms of Data Science you’ll encounter sooner or the later the terms “Precision” and “Recall”. But what do they mean?

## Clarification

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”

## UD120 – Intro to Machine Learning

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”