To make it a bit more interesting we generate two functions: sine and cosine. We generate our x-values with numpy’s linspace function

import numpy as np 
import matplotlib.pyplot as plt 

x = np.linspace(0, 2*np.pi) 
sin = np.sin(x) 
cos = np.cos(x) 
plt.plot(x, sin, color='b') 
plt.plot(x, cos, color='r') 
plt.show()

You can plot two or more curves by repeatedly calling the plot method.

That’s fine as long as the individual plots share the same axis-description and values.

Subplots

fig = plt.figure() 
p1 = fig.add_subplot(2, 1, 1) 
p2 = fig.add_subplot(2, 1, 2) 
p1.plot(x, sin, c='b') 
p2.plot(x, cos, c='r'

The add_subplot method allows us to put many plots into one “parent” plot aka figure. The arguments are (number_of_rows, number_of_columns, place in the matrix) So in this example we have 2 rows in 1 column, sine is in first, cosine in second position:

when you have a 2 by 2 matrix it is counted from columns to row

fig = plt.figure() 
p1 = fig.add_subplot(221) 
p2 = fig.add_subplot(222) 
p3 = fig.add_subplot(223) 
p4 = fig.add_subplot(224) 
p1.plot(x, sin, c='b') 
p2.plot(x, cos, c='r') 
p3.plot(x, -sin, c='g') 
p4.plot(x, -cos, c='y')

The code is available as a Jupyter Notebook on my github

More about matplotlib in Part 3

The post Introduction to matplotlib – Part 2 appeared first on Creatronix.

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.