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

The post 10 things I didn’t know about Data Science a year ago appeared first on Creatronix.

To work with OpenCV from python, you need to install it first. We additionally install numpy and matplotlib as well

pip install opencv-python numpy matplotlib

Reading Images from file

After we import cv2 we can directly work with images like so:

import cv2 
img = cv2.imread("doc_brown.png")

For showing the image, it is recommended to use matplotlib

import matplotlib.pyplot as plt 
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) 
plt.imshow(img) 
plt.show()

OpenCV stores images internally in the BGR format – blue – green – red so we have to convert to RGB before displaying them.

Image Shape

We have now an image object which can already tell us more about the image itself:

print(img.shape) 
(205, 236, 3)

The tuple shows us the number of (rows, columns, channels)

Manipulate brightness

import numpy as np 
brightness = np.zeros(img.shape, dtype="uint8") + 30 
img = cv2.add(img, brightness)

Manipulating brightness works like this: you need a numpy array with the size of the image, add your brightness and add the brightness array to the original image.

Adding noise

Adding noise works the same way, but instead of adding a fixed value to every pixel you add normal distributed values.

noise = np.random.randint(0, 100, size=img.shape, dtype="uint8") 
img = cv2.add(img, noise)

Smoothing

Smoothing is reducing the noise of an image by adding a Gaussian blur:

img = cv2.GaussianBlur(img, ksize=(31, 31), sigmaX=5)

Gray-scale conversion

Last but not least we can convert an image to a gray scale. Beware that for showing / storing the image you need to add the flag cmap=”gray”

img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
plt.imshow(img, cmap='gray')

Have fun fiddling around with OpenCV!

Github Repo

https://github.com/jboegeholz/introduction_to_opencv

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