Anyone who has been keeping a keen eye on the shifting paradigms of modern day technology does not really need an introduction to the terms Python and machine learning. That being said we shall take a brief look at them for the record. Then we can move on to examine the contribution of Python in making machine learning easier.
Machine learning has been among the major buzz words across the techworld for a considerable amount of time now. And why not? The world had always envisaged a future where machines will do the bidding of man and machine learning makes it possible.
We can define machine learning as a discipline that focuses on training a machine that can act without being explicitly programmed. However, you should not imagine this self functioning machine as a giant robot that can do whatever it wants and can go out of control. These are essentially programs featuring algorithms that are trained to learn from data and make predictions and recognitions. For example you can train a machine learning algorithm to predict certain movements in the stock market by exposing it to a significant amount of data.
Python is one of the most popular programming languages of this era. This open source tool has won a lot of hearts as well as the title of the most popular language in various lists. It has a simple syntactic system, a relatively easy learning curve, a dedicated community of developers and a large number of libraries. Python is brilliantly suited for general purpose coding but we are going to focus on its utility towards the cause of machine learning. Our goal is to find out why professionals are so keen on performing machine learning using Python
Python has multiple libraries that are handcrafted for machine learning
Python is simple yet effective. Hence it is not surprising that it would be used for the not so simple discipline of machine learning. Developers have lined up libraries that are brilliantly suited for the purpose. Let us walk through few of those libraries and try to figure out what makes them apt.
This Python library comes with a feature called array interface. That means it can translate images, sound waves and other binary streams in array of real numbers. So, when it comes to the implementations of image processing or speech recognition, using NumPy is ipso facto inevitable. Google’s Tensorflow uses NumPy internally.
Built on NumPy this collection of mathematical algorithms gives more power to the developer. It enables you to use efficient numerical routines like integration and optimization. The higher statistical ability provided by this module is definitely an aid to the cause of machine learning.
This one can be associated with both NumPy and SciPy. It is designed for enabling standard machine learning and data mining processes like classification, regression and clustering. You can apply the algorithms in this library to execute training procedures like logistic regression and nearest neighbour.
There are more libraries suited for machine learning like PyTorch, Pandas, and Keras. The last one is considered to be exceptionally suitable for the beginners.
Python has been identified as one of the most suitable languages for performing machine learning and as we have seen it is not done without valid reasons. Consequently, your ability to perform machine learning using Python can take you places.