Python Tools for Machine Learning

Python Tools for Machine Learning


Machine learning is among the top and most prospective directions in the software development niche. The concept helps conveniently automate various work processes (including Big Data processing), enhance the precision of business predicting results, optimize the supply chain, etc. Also, ML is a foundation for applications that feature the recognition of voice signals (sounds, speech), facial features, and other objects which cannot be identified with the help of single-line mathematical formulas and simple Boolean expressions.

There are many tools to aid in the creation of solutions based on machine learning in the Python programming language. That’s what this feature is all about – we highlight most renowned, efficient ML tools as well as some other important aspects of ML.



Types of machine learning tasks

The ML technology can be of great use in various spheres of business and industry. It can be used in banking and finance, commerce and ecommerce, or health and entertainment. Nevertheless, all the tasks machine learning software are meant to handle can be subdivided into three major categories (there are more categories, but the following three cover the vast majority of case studies):

  • Supervised learning. The input data in supervised learning is the data and the result of its processing. Such pairs are called ‘examples’ and the further activity of the software algorithm is indicated by way of analyzing such examples. The more examples learned, the more precise the software, which is reasonable to expect. Supervised learning is a basis for the classification tasks (indication of a single correct solution among the number, N, of possibilities based on previous experience) and regression tasks (indication of the precise answer which IS NOT a discrete value based on previous experience).
  • Unsupervised learning. In this category the externally collected data isn’t systematized in any way. Software based on this type of learning sets up connections and defines templates autonomously. The unsupervised learning solves the clusterization tasks.
  • Reinforcement learning. Here, the input data is used in order to acquire a supervisor’s reaction. If the chosen answer is incorrect, the supervisor reacts positively in response; in the case of a negative reaction, the software looks for other solutions for the set task.


Why Python is one of the most popular ML-based software languages

At 20 years of age, the Python language is among the most frequently employed for the creation of software based on ML. Many programmers even consider it the best in the given development niche. It has a steep learning curve, provides extensively painless interaction with various database management systems, and integrates easily with different software tools specializing solely in composing machine learning algorithms.

And so, as we’ve approached the subject of Python tools for machine learning, now you can find out more about the most popular and useful solutions that can ultimately boost the effectiveness of your project development below.


Software tools for the ML-based solutions creation


An offspring of the Google team, Tensorflow is one of the most advanced Python frameworks for machine learning that implements deep machine learning algorithms. It is a second-generation, open-source system, the predecessor of which was the less integral recognition solution DistBelief. Despite its high learning curve, the product can nevertheless provide developers with a number of capabilities (alternatively, you can choose from other popular machine learning frameworks with steeper learning curves, like Theano). In particular, Tensorflow features tools that allow executing the input data analysis both with the help of encyclopedic data and the data previously analyzed during the interaction with certain users (supervisors). Although Tensorflow’s final results are characterized by a high level of precision, developers usually prefer not to use it in scientific software development.




Shogun is an open-source solution available in many programming languages due to the SWIG (Simplified Wrapper and Interface Generator). It is based on the Support Vector Machines (SVM). This tool can be launched with minimum effort through the cloud and provides efficient and simple realization of all general ML scripts.


A higher-level API, Keras is perfect for beginners. It is used for the creation of artificial neural networks which imitate the memory process, similar to that taking place within human neurons. This product is easily integrated with Theano, TensorFlow, and CNTK and allows building nodular solutions open to scaling.


This API provides accessible and efficient means for intellectual data analysis. Based on such specialized tools as NumPy, SciPy, and matplotlib (which we’ll also discuss), this API is, basically, a universal assistant in solving the classification, regression, and clusterization tasks.


This free module for constructing web solutions is a very practical and effective piece of software that features hundreds of detailed examples. Its capabilities include data processing via Google/Twitter/Wikipedia APIs, human voice recognition, and machine learning with the use of SVM and VSM methods and clusterization.


Theano is one of the most renowned machine learning frameworks for Python. It was created for the processing of multidimensional arrays. It is closely integrated with the older computing solution NumPy. Developers love Theano for its fast performance provided by the employment of an additional GPU during computations as well as for the handy unit-testing feature.




The free platform Natural Language Toolkit is a universal solution for human speech processing. It can be used even for the creation of narrowly-specialized software requiring identification of difficult terminology or dialect expressions. The NLTK is compatible with Linux, Windows, and Mac OS X operating systems.


An open-source product, Gensim is used by developers to model vector spaces in Python and is based on the NumPy and SciPy libraries. Given software is adapted to work with large volumes of digital data while demonstrating great performance and rational memory consumption rates.


SciPy is a free library built to implement complex mathematical and engineering computations. It includes NumPy, IPython, and Pandas packages, which provide an all-around approach to solving multi-staged scientific tasks when combined. Particularly, it provides all the standard math analysis functions (e.g. calculation of extremes, differential equations solvents, integrals solvents), plus quite specific capacities such as gesture and image recognition. SciPy will be a great choice for those used to work with MATLAB.


This product allows the implementation of multidimensional data analysis processes. That’s why it’s frequently employed in the creation of predicting apps. Also, it is painlessly integrated with NumPy, Pandas, and Scikit-Learn. Dask executes the paralleling of computations, allowing app scaling based on the mentioned packages beyond the confines of a single computer (e.g. it can spread through the distributed clusters).


Numba helps to accelerate Python-based apps. It is a pretty relevant product for those using capacitive machine learning algorithms. Basically, it employs the LLVM compiler capacities in order to translate Python code into binary in an accelerated mode.




Another compiler by nature, the HPAT tool boosts the performance of software that operates large data volumes. It automatically parallels Python code distributing bulks where possible.


NumPy is one of the basic packages for mathematical calculations in Python. It effortlessly operates with multidimensional bulks of data. It covers even the most specific parts of linear algebra and mathematical analysis, allowing for the most voluminous calculations. And the overall performance of apps isn’t affected by that at all, so paralleling the code is required only in the case of working on large-scale software.


An open-source Pandas package which thoroughly processes and analyzes data. As a matter of fact, it is a high-level add-on for the previous package, which is even more optimized for high-performance applications.


Machine learning tools: conclusion

The ML apps are able to solve the widest range of tasks, which could only be handled by the narrowly-focused experts before. And, by the way, we can help you implement your unique idea! Our team develops high-profile and utterly accessible scalable software.

Alex Morgunov

Alex Morgunov

Project Lead

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