Python for Machine Learning

Machine Learning applications are solving many problems and improving traditional processes across industries. It is behind enabling better personalization, smarter recommendations, and improved search functionality and Python has been instrumental in all these developments. In this blog, we will see why Python is crucial for machine learning. 

Some of the characteristics of Python that make it an ideal programming language for machine learning are:

Simplicity and Consistency

The best thing about Python is that it provides a concise and reliable code which is easy to learn and implement. While machine learning models run on complex algorithms and workflows, Python’s simplicity allows developers to write reliable and error-free code. When the effort and time spent on understanding and implementing the code reduces, developers are able to focus their energies on solving the Machine Learning problem instead of the technical nuances of the programming language. 

Also, Python is suitable for collaborative implementation where multiple developers are working on a single project. Since it is a general-purpose language, it performs a set of complex ML tasks and enables you to quickly build prototypes to test your product for ML purposes. 

Wide range of libraries and frameworks

Implementing machine learning algorithms is tricky and requires a lot of time to achieve desired results with the algorithm. Therefore, a well-structured and well-tested environment is vital to enable developers to come up with the best coding solutions.

Reducing development time is crucial for programmers and hence they turn to the number of Python libraries that aid development of ML algorithms. Some of these libraries are:

  • Scikit Learn: It is used for handling basic ML algorithms like linear and logistic regression, clustering, classification, regression among others.
  • Tensorflow: It helps in setting up, training, and utilising artificial neural networks with massive data sets to successfully work with deep learning.
  • Matplotlib: It helps in creating 2D plots, charts, histograms, and other visualisation forms.
  • Keras: It allows fast calculations and prototyping by using GPU in addition to the CPU of the computer.
  • Numpy, Scipy, and Pandas: They enable high-performance scientific computing and data analysis.
  • Seaborn: For data visualisation.
  • NLTK: It helps in working with computational linguistics, natural language recognition, and processing. 
  • Scikit: Image for image processing. 
  • PyBrain: This library is for neural networks, unsupervised and reinforcement learning.
  • Caffe: It is for deep learning that allows processing 60+ million images a day using a single NVIDIA K40 GPU.
  • StatsModels: For statistical algorithms and data exploration. 

Platform independence:

Linux, Windows, and macOS all support Python. Python codes are used to write standalone executable programs for most common operating systems. This means that Python software can be easily distributed and used on those operating systems without a Python interpreter.

For their computing needs, developers usually use Google or Amazon services. However, there are companies that use their own dedicated machines to train their machine learning models. The fact that Python is platform-independent, makes training the models a lot easier and much cheaper.


Python is a very flexible language and hence is a great choice for machine learning. It provides the option to choose between OOPs and scripting. Also, it eliminates the need for recompiling source code and developers can implement the changes they want to see the results immediately. Another plus point of Python is that developers can combine Python with other programming languages to reach their desired goals in the most convenient ways possible.

The other aspect of flexibility in Python is the provision of choosing programming styles and even combining these styles to solve specific problems efficiently. 

  • The imperative style has the commands that describe the sequence of computations which occur as a change of the program state.
  • The functional style is also known as the declarative style as it declares the operations that should be performed. It declares the statements in the form of mathematical equations. Unlike the imperative style, it does not consider the program state. 
  • The object-oriented style – This style is based on two concepts, i.e., class and object. This style can be used only to a finite degree as Python does not support it fully. 
  • The procedural style – It is the preferred style of the beginners in Python. It is used for applications such as iteration, sequencing, selection, and modularisation. 

Python being so flexible, has the least possibility of errors as compared to other programming languages. Therefore, python programmers have a fair chance of taking control of the situation and work in a comfortable environment.

Visualisation Options:

As mentioned earlier, Python has a vast range of libraries which also include the ones that could be used for visualisation. However, for those working with Machine Learning, Deep Learning, and Artificial Intelligence, it is vital that they represent data in human-readable forms.

Libraries such as Matplotlib allows data scientists to create plots, histograms, and charts for better comprehension and effective representation. Such visualisations are in the form that is easily understandable by stakeholders across domains, even the ones who do not have in-depth knowledge of machine learning.

So these are some of the reasons why Python is the perfect choice for machine learning among the programming languages. If you are someone who works with machine learning models, developing them on Python will benefit you in many ways. If you have any questions regarding Python or its importance in machine learning, please feel free to drop them in the comments below. 

Get Python for Machine Learning course completion free certificate from Great learning which you can share in the Certifications section of your LinkedIn profile, on printed resumes, CVs, or other documents.

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Christophe Rude

Christophe Rude

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