3 Key Reasons Why Python for Machine Learning

Machine learning is a wonderful subject, it has allowed for impressive applications in recent years and lately seems to be all over the news and dripping down to the vocabulary that your friends and colleagues are constantly using. If you want part of the action and don’t know where to begin, here are 3 key reasons why Python for Machine Learning is the way to go.

Python is Easy

"Whether you're new to programming or an experienced developer, it's easy to learn and use Python." This is an excerpt from python’s website, and let me tell you it is true. 

Aimed for a clear syntax and being developer friendly you can do more with less in python. If you are just getting started with machine learning this could be all you need to get you developing machine learning algorithms rapidly and with as little friction as possible.

To see how simple it is, here is a python for loop. Printing numbers from 0 to 9 as we go.

for i in range(10):
    print(i)

 Need to generate a random number with python between 0 and 100? it's as simple as the following code.

import random
random.randint(0,100)

Python syntax is beautifully easy and no matter your programming level, it’ll be the easiest languages to pick up quickly and start getting into machine learning. 

Data Wrangling with Python is a Breeze

In order to build machine learning models, you need data. In the real world, this data is not in an easily digestible format for your machine learning models. This is an important reason why you want to use python for machine learning. 

Python has a variety of packages or modules that provide much-needed flexibility to wrangle data and get it into the format you need. One of them is the famous Pandas library which provides functions and data structures to work with data in a tabular form.

As a quick example, if you need to read a CSV file into memory you would do the following with Pandas.

import pandas as pd
df = pd.read_csv('your_file.csv')

Many argue that one of the reasons why Python became so popular is thanks to the python Pandas package. Pandas allow you to work with your data as you would in an SQL and much more.

Machine learning algorithms also require heavy use of linear algebra, and thus heavy use of matrices. Numpy is another widely used python package for machine learning which provides you with all the array functions you would need.

In short, Python provides you with the tools you need to quickly transform your data into a machine learning algorithm readily input. 

Python’s Machine Learning Packages

What do you do once you prepared your data for a machine learning algorithm? You need to run the algorithm itself! Python once again makes this extremely easy with its wide array of machine learning packages at your disposal.

The Sklearn python package is widely popular for implementing most of the machine learning algorithms you might need. From classifiers such as KNN’s, regression analysis to neural networks. Sklearn gives you much of the needed functionality within Python to get you running rapidly. 

As an example of python’s machine learning package capabilities, let us run the popular k means clustering algorithm.

from sklearn.cluster import KMeans
import numpy as np
X = np.array([[1, 2], [1, 4], [1, 0],[10, 2], [10, 4], [10, 0]])
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)

Ever heard of Google’s Tensorflow? An extremely popular framework for building and training machine learning algorithms used at Google itself. Python was the first client language supported by TensorFlow. 

Look over at Facebook and its machine learning framework called PyTorch. Notice the Py in there? Yep, it’s python’s machine learning package.

I could go on an on. Python has a wide variety of packages to implement state of the art machine learning algorithms quickly and effectively thanks to Python’s beautiful syntax. You can’t go work with Python for Machine Learning.

Bonus Reason - Developers around the world Want it

In StackOverflow's 2018 annual developer’s survey Python reached the #1 spot of the most wanted language. Meaning that it is the language that developers who do not yet use it most often say they want to learn.

They want to learn for a reason, its easy to work with, fast and efficient to develop on and has an ecosystem that continues to grow for data science as well as other tasks.

Conclusion 

To get on the AI bandwagon you need to pick from many tools out there. Python will give you the most bang for the buck as you engage in your machine learning adventure. It’s ease of use, data science enabling package environment and worldwide following and growth makes it an easy choice for the new or experienced professionals alike. Get started today in machine learning with python.