Why to do Machine Learning with Python?

19-Feb-2021

What is Python?

Python is one of the large numbers of high-level programming languages prevalent today. It has many extremely convenient and ingenious features such as built-in data structures, dynamic typing, dynamic binding, and functions. These features of Python make it far more advanced than other low-level languages such as assembly language or machine language. Even among high-level languages, Python is among the topmost languages in terms of richness of features and language design (syntax, semantics, efficiency, etc.) 

One aspect of this language design is the syntax of Python. Python’s syntax is incredibly simple and easy to both read and write. It is very sparse and minimal. It doesn’t have many intricate nooks and crannies which a beginner will have to learn. For instance, Python makes use of whitespace for formatting and indentation, instead of curly brackets or braces. This makes the code look very organized, neat, and clean. This is a very major reason why people carry out machine learning with Python.

The Python interpreter uses dynamic binding and dynamic typing and does not have static binding or static typing. So one will find that because of features like these it is very easy to write programs in Python and that even writing large programs becomes effortless. The Python interpreter has also been designed very well and makes it possible to scan Python code and step through it line by line. This makes testing and debugging a very smooth and pleasant experience instead of the error-prone and difficult experience it usually is. 

The excellent interpreter that Python has makes it very suitable for Rapid Application Development or RAD. This includes creating simulations, testing concepts, and programming prototypes very quickly. Python can also be used as a glue language to connect various components of a software application together. Or, it can be used as a scripting language to write little scripts which run quickly to find and solve small or large problems.

Why is Python so common in Machine Learning?

Machine learning is a very advanced and complex field of Artificial Intelligence. So the code written for machine learning algorithms, tools, and techniques can become extremely verbose, intricate, and obscure. Machine learning applications’ code usually tends to be tens of thousands of lines long. So it is very important that the programming language used to write the machine learning code is full of powerful and clever features which will make it possible to accomplish more with lesser code. Also, the machine learning code should be readable and easy to understand because it is already very complex and involved, filled with mathematical formulae and computer science algorithms. So a programming language that is not verbose and has a sparse syntax is required. The Python programming language fulfills all the above requirements and so programmers carry out machine learning using Python.

Reasons for Python’s popularity in Machine Learning -

Some other reasons for Python’s popularity in Machine learning are -

1. Python has a great library ecosystem -

The community of software developers which has grown around Python is very active and very dedicated. It has written libraries and frameworks for Python that are incredibly comprehensive and well-designed. Most problems which Machine Learning developers face in their field have already been solved and packaged in the form of Python libraries and frameworks. So Machine learning developers will find that they will be able to call upon many premade functions to perform the common tasks of Machine learning. Some examples of Python libraries are Scikit-learn, Pandas,  Keras, TensorFlow, Matplotlib, NLTK, Scikit-image, PyBrain, Caffe, StatsModels, etc. Thus using Python for machine learning is a very viable option.

2. Python has a very low entry barrier -

Python syntax is very readable, sparse, and minimal. Python does not use any complicated symbols for formatting and indentation but achieves those with the help of simple whitespace. Also, Python syntax resembles natural human languages to a large degree, so it is very easy even for beginners to comprehend Python code. Python also has a very high-quality interpreter. This makes it very easy for a beginner to Python to step through Python code line by line and find and fix bugs. So it testing and debugging is a very seamless and painless experience in Python. All these things make it very easy for beginners to Python to learn it and become proficient in it. Our machine learning Python course takes advantage of this characteristic of Python to teach learners effectively.

3. Python is a very flexible programming language -

Python allows programmers to program in a variety of styles, such as -

• The imperative style – In the imperative style the programmer gives a sequence of commands to the computer. The computer has to then execute those commands in the sequence they have been given and change the program state according to them.

• The functional style – The functional style doesn’t consider the state of the program but instead allows the programmer to program in a declarative style. It involves giving commands to the computer as a set of mathematical equations which are then solved by the computer and executed efficiently.

• The object-oriented style – The object-oriented style makes writing large programs very easy and convenient. Two components form the basis of object-oriented style – the class and the object. Similar objects are grouped together into classes. While Python doesn’t fully support the object-oriented style (it is unable to perform encapsulation properly) it still allows programming in it to a certain degree.

• The procedural style – The procedural style is the most commonly used style among beginners to Python. It involves executing simple commands in a step-by-step, sequential manner. It is used for sequencing, iteration, modularization, and selection.

This makes it very easy to learn machine learning with Python.

4. Python is very platform-independent -

Python code runs exactly the same on any platform, as long as it doesn’t contain any features specific to one platform. So the Python interpreter can be installed and can run on Linux, Windows, MacOS, and a variety of other platforms. So our machine learning with Python course is not limited to any one platform.

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