Is mathematics essential for machine learning? One can ask the same question in the following way.
What if I am not good at mathematics? Can I learn machine learning algorithms?
Assuming that a beginner asks this question, let’s have a frank discussion.
Although at some point mathematics is essential for machine learning and data science, at the very beginning, you don’t need it.
We have already discussed what type of mathematical knowledge is necessary for data science and machine learning.
However, it is not mandatory that you have to be good at mathematics.
Let’s see the image again.
As we can see, we need a part of mathematics and a part of python to learn machine learning algorithms.
But even before that part of mathematics, you can write your first algorithm in python.
Or you can read a python code and try to understand it.
For example, let’s consider the following code.
import numpy as np
distances_in_miles = np.array([14.5, 80.2, 6.4, 0.6, 25.56])
distances_in_kilometers = distances_in_miles * 1.6
print(distances_in_kilometers)
# [ 23.2 128.32 10.24 0.96 40.896]Firstly, you don’t need to learn higher level mathematics to convert miles to kilometers.
Everybody knows that. Just multiply the value in miles by 1.6. You’ll get the value in kilometers.
But can you think of a list of values in miles that we can convert to kilometers in one operation?
The above algorithm shows that.
It’s that easy.
At one go, we have converted a list of values to kilometers.
However, we have not done that.
The NumPy library has done that.
If we had to do the same thing manually, we would have to write a loop and convert each value sequentially.
I hope you get the point.
How to start Machine Learning
There are tons of machine learning libraries that will help you to perform such heavy tasks.
As a result, the python libraries will take the burden.
Therefore our learning path will be empirical. Not theory based. We will do, we will learn.
Certainly, it may take some time. But learning never ends.
Therefore, stay tuned. If you’re a beginner, watch the beginner’s category. In addition, also learn about libraries.
We’ll concentrate on NumPy, Pandas, Matplotlib and scikit-learn.
Besides, if you are a beginner, please watch our Python, Mathematics, Data Science and Machine learning categories.
We also have some GitHub repositories to start with.
If you are a beginner in Python, please clone this GitHub repository.
For Discrete Mathematics and Python algorithm, please clone this GitHub repository.
And finally, for NumPy please clone this GitHub repository.
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