Python continues to crush other languages in the race to become the most popular tool used by people worldwide!
As per the TIOBE Index 2018, Python squashed all other competitors to earn the “Language of the Year” title and remains in the Top 3 as of July 2019.
Ever wondered why all the gushing behind Python?
A lot of the rage is rooted in Python’s compatibility with machine learning. Python has evolved to become the most favored tool by machine learning buffs all around the world.
Let’s explore why that is the case, shall we?
Python is for the masses
Python is simple to read and write. Even for non-coders, the language looks much like simple English.
Python was developed to improve on many shortfalls of it’s competition. Python does not need long lines of codes to run instructions. It is easy to develop, interpret and improve upon.
Python has a simple syntax which is easy to pick up and learn.
Python is a delight to use!
Python is open-source which means it essentially a free tool. You can access, learn, copy and replicate the language without any obstacles and absolutely free of cost!
In fact, a lot of the features of the tool were developed and contributed towards by their users themselves. Python does not have hard and fast rules when it comes to building features, which means there are different ways to build what you want.
Python has a strong online presence with an active and vocal community. All queries and problems are fixed instantaneously by responses and threads from the strong user base of the tool.
It’s not just about the response time of the community, there are so many previously addressed issues that you can think of a problem and that would have already been tackled by some group in some corner of the internet.
These are some of the features that make Python a stand-out but why exactly is Python the apple of the eyes of Machine Learning devotees?
Most machine learning engineers or data scientists do not have the time to tackle a time-consuming and arduous language. Python is a preferred tool as it deals with complexities on the back-end without prompting the users to get involved in the nitty-gritty of a programming tool.
Another feature that earns great points in Python’s favour is it’s extensive library base. In the spirit of making life easy, Python offers a number of packages and libraries to choose from.
What these libraries ensure is that any user irrespective of his technical soundness can perform a number of different functions by simply installing a pre-formatted library and package designed for that purpose. Consider them as templates or keyboard shortcuts! Imagine, how helpful!
Let’s take a look at some libraries that have gained popular praise in the past few years!
Panda is short for Python Data Analysis Library.
Pandas perform fabulously in data analysis. You can load, prepare, join, merge, manipulate and analyze data using pandas. Pandas have a powerful set of features that let you explore your data more thoroughly.
Numpy stands for Numerical Python. There is no better library to start with as Numpy is used internally by many other Python packages themselves.
Scipy is used for scientific computations. It builds on Numpy’s abilities and uses its arrays to perform advanced mathematical operations.
Data makes more sense once visualized. It makes reading, comparing and drawing inferences easier.
Matplotlib is the ultimate Python library to help visualize your data. There are many other popular packages and libraries that you must be aware of. Check out our exhaustive list here!
Python is Everywhere!
Python is the most regarded and followed tool across communities. Here are some facts that can help you gauge its popularity:
- Python has more than 1,208,167 queries posted on Stack Overflow. It is the most discussed programming language on the platform.
- The Meetup community has about 2,135,213 members and 2415 groups. Python’s community here is the third largest among programming languages.
4 out of 5 developers use Python as their primary choice.
How does Python fare in the employment sector?
Python has accomplished a lot since it’s evolution but let’s see what the job market has to say about!
Python has squashed other programming tools as per the IEEE rankings of last year.
As Python is standing on its own so far, what the world is witnessing is the rapid rise in popularity thanks to the sharp focus on machine learning and artificial intelligence.
Why Python is the best fit for machine learning and AI?
Machine learning is time-taking and involves a lot of mental faculties. Add to it, the burden of a high maintenance programming tool and the user gets discouraged and distracted.
Python, like we discussed, requires lesser attention. It is easy to code, short and brief, comprehensive and offers a number of compatible libraries to put the user to ease. Tensorflow is the best tool for building neural networks. Top companies including Google, Microsoft and Amazon have become pioneers of Python for machine learning.
Other than Numpy and Pandas, here is a list of the top Python libraries that are best suited for machine learning:
- Scikit-Learn: Scikit learn is used to build algorithms and models. It is built on Numpy, Scipy, Matplotlib and Panda.
- Tensorflow: Think of TensorFlow as a flowchart of operations. Input fed to it is in the form of multi-dimensional arrays. This input goes through a series of operations or functions to yield the output. The input in the form of multidimensional arrays or Tensors gives it the name Tensorflow.
- Keras: Keras is another deep learning library of Python. Keras is a bit more advanced than Tensorflow but is much simpler to learn. Keras saves time by not performing its own low-level operations.
- Eli5: Eli5 helps explain what different machine learning algorithms have predicted. It achieves this by debugging machine learning classifiers.
- PyTorch: PyTorch is being used by teams at Uber and Facebook AI. In fact, PyTorch was built on Torch by the team at Facebook. It is majorly used for deep learning and natural language processing. PyTorch is an extremely flexible library which is used far and wide by machine learning buffs everywhere.
- LightGBM: Light gradient boosting machine uses tree-based algorithms for ranking and classification. It adapts well to large data-sets. It is fast and has better accuracy than other gradient boosting tools out there.
- Theano: Theano is another popular Python library aimed at manipulating, optimizing and evaluating mathematical expressions.
What started out as an open-source project developed by the Montreal Institute for Learning Algorithms has now transformed into a go-to library for machine learning enthusiasts over the world!
A good machine learning buff always evaluates and tests the algorithms he puts to use.
Efficiency of models, algorithms and your general approach should be tested to improve your results. Python makes evaluating models and algorithms oh-so-easy! When you’re not worried about the nuances of a programming tool and you have literally an overflowing platter of choices to choose from, it allows you enough time to think and rethink the various ways in which you can get better results from your data.
This ends our feature on why Python should be your chief tool when it comes to machine learning problems! You can always share your experiences with python and any questions you have with us in our comments section.