When you finished reading part 1 of the introduction you might have wondered how to draw more than one line or curve into on plot. I will show you now.
To make it a bit more interesting we generate two functions: sine and cosine. We generate our x-values with numpy’s linspace function Continue reading “Introduction to matplotlib – Part 2”
In my article My personal road map for learning data science in 2018 I wrote about how I try to tackle the data science knowledge sphere. Due to the fact that 2018 is slowly coming to an end I think it is time for a little wrap up.
What are the things I learned about Data Science in 2018? Here we go:
1. The difference between Data Science, Machine Learning, Deep Learning and AI
Continue reading “10 things I didn’t know about Data Science a year ago”
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”
Numpy is a package for scientific computing in Python.
import numpy as np
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 Continue reading “Linear Algebra with numpy – Part 1”
I got confused by all the buzzwords: data science, machine learning, deep learning, neural nets, artificial intelligence, big data, and so on and so on.
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: Continue reading “My personal road map for learning data science in 2018”