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

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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.

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.

>>> import numpy as np 
>>> np.random.random_integers(5) 
4

>>> np.random.random_integers(5, size=(5)) 
array([5, 3, 4, 1, 4]) 

>>> np.random.random_integers(5, size=(5, 4)) 
array([[2, 3, 3, 5], [1, 3, 1, 3], [5, 3, 3, 4], [1, 5, 2, 5], [2, 5, 4, 5]])

If You want a random selection of choices from an array you can use

>>> import numpy as np 
>>> animals = ['dog', 'cat', 'rabbit'] 
>>> np.random.choice(animals, 9)) 
array(['dog', 'rabbit', 'dog', 'rabbit', 'dog', 'dog', 'cat', 'dog', 'cat'], dtype='|S6')

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It is often used together with pandas, matplotlib and Jupyter notebooks. Often these packages are referred to as the datascience stack.

Installation

You can install numpy via pip

pip install numpy

Basic Usage

In the datascience world numpy is often imported like this:

import numpy as np

The “as” keyword defines a so called alias. Now you can use structures from numpy by referencing them with “np” instaed of the whole name.

Think “abbreviation”.

n-dimensional array

The most important data structure is ndarray, which is short for n-dimensional array.

You can convert a list to an numpy array with the array-method

my_list = [1, 2, 3, 4] 
my_array = np.array(my_list)

You can also convert an array back to a list with

my_new_list = my_array.tolist()

You can retrieve the dimensionality of an array with the ndim property:

my_array.ndim

and get the number of data points with the shape property

my_array.shape

Vector arithmetic

Addition / Subtraction

a = np.array([1, 2, 3, 4]) 
b = np.array([4, 3, 2, 1]) 
a + b 
array([5, 5, 5, 5]) 

a - b 
array([-3, -1, 1, 3])

Scalar Multiplication

a = np.array([1, 2, 3, 4]) 
a * 3 

array([3, 6, 9, 12])

To see why it is charming to use numpy’s array for this operation You have to consider the alternative:

c = [1,2,3,4] 
d = [x * 3 for x in c]

Dot Product

a = np.array([1,2,3,4]) 
b = np.array([4,3,2,1]) 
a.dot(b) 

20 # 1*3 + 2*3 + 3*2 + 4*1

Learn more about numpy:

numpy random choice

Numpy linspace function

Project on github

The post Linear Algebra with numpy appeared first on Creatronix.

You give it a range (e.g. 0 to 23) and the number of divisions and it will distribute the values evenly across that range. The stop values is included in the resulting value array by default.

Example:

import numpy as np 
np.linspace(0, 23, num=9) 

0: 0.000
1: 2.875
2: 5.750
3: 8.625
4: 11.500
5: 14.375
6: 17.250
7: 20.125
8: 23.000

Read more about numpy:

Linear Algebra with numpy

numpy random choice

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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:
So 2018 will be all about Data Science. Hearing about the Personal Knowledge Mastery concept at SWEC17 I am going to tackle the learning process on different levels.

Watch the Pros

Thanks to open course ware there are a ton of awesome university courses online e.g.:

MIT 6.0002 Introduction to Computational Thinking and Data Science

Learn the tools

There is already a whole bunch of tools we can consider belonging to a standard data science stack. Because my main language is Python the focus is of course on mostly python modules.

• JuPyter Notebook
• numpy
• pandas
• seaborn
• bokeh
• holoviews
• scikit-learn
• keras / TensorFlow
• Tableau

Finishing Udacity / Udemy courses

To brush up my python skills and my knowledge of basic computer science I will finish some already started online courses:

• • [  ] Introduction to Machine Learning
  • [  ] Python Bootcamp
  • [  ] Algorithms and Data Structures
  • [  ] Introduction to Artificial Intelligence
  • [  ] Introduction to computer vision
  • [  ] Artificial Intelligence for Robotics

Reading data science books

To get a broad overview I bought two books on DS / ML

• [  ] Data Science from Scratch
• [  ] Hands on Machine Learning

Do Exercises on Kaggle

• [x] Create Account at Kaggle
• [  ] Do first exercise
• [  ] Participate in a contest

Visit Meetups about Data Science

[  ] Visit Big Data Meetup Events

Add some Peer Pressure

My brother in law and I teemed up and build a Whatsapp learn & exchange group. We are currently four members.

Write Blog Articles

I will try to incorporate some of the stuff I’ve learned into blog articles.

I already did

• Bayes’ Theorem Part 1
• Data Science Overview
• Classification: Precision and Recall
• Confusion Matrix
• UD120 Intro to Machine Learning
• Lesson 2: Naive Bayes
• Lesson 3: Support Vector Machines

So stay tuned!

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