Python-based Machine Learning libraries you must be aware of

If you are just starting your ML experience or you want to improve your knowledge of the existing ML libraries, the article is for you. It discusses the main features of the most valuable libraries for Machine Learning development.

Artificial intelligence and Machine Learning have been widely known for increasing your sales services, improving employees’ productivity, analyzing large volumes of data, and improving customer satisfaction. But starting a Machine Learning project is not easy. Choose the right solution, library, and company to be on the top of software development trends.



Written in Python and C++, TensorFlow is an open-source software library aimed at numerical computation and Machine Learning. It uses data flow graphs for different types of perceptual and language understanding tasks.

TensorFlow is fast and robust since it can be run on multiple CPUs and GPUs. Google and other Alphabet companies use it both for production purposes and for conducting Machine Learning and deep neural networks research. Nevertheless, this library is general enough to be applicable in a wide variety of other areas.

  • Deep Flexibility
    If you can construct a data flow graph for your computation, then you can definitely benefit from TensorFlow. It has a flexible architecture and allows developers to add their own low-level data operators, define some useful compositions of operators, or write higher-level libraries on top of TensorFlow.
  • True Portability
    TensorFlow runs on CPUs or GPUs, and on desktop, server, or mobile computing platforms. It can run the model as a service in the cloud using Docker containers.
  • Maximized Performance
    TensorFlow allows using available hardware at its maximum capacity. Compute graph elements can be assigned to different devices and TensorFlow handles everything with the help of threads, queues, and asynchronous computation.
  • Auto-Differentiation
    TensorFlow features automatic differentiation capabilities and handles the derivatives computing. It is especially valuable for gradient-based Machine Learning algorithms.
  • Language Options
    This library has a Python interface, C++ interface, and even interactive TensorFlow iPython notebook - easy-to-use tools to build and execute computational graphs.

Changes in TensorFlow 2.0

In October 2019, the following updates of TensorFlow were released:

  • Support for the Keras framework
  • It is possible to use Keras inside Tensorflow. It ensures that new Machine Learning models can be built with ease.
  • Supports debugging your graphs and networks - TensorFlow 2.0 runs with eager execution by default for ease of use and smooth debugging.
  • Robust model deployment in production on any platform.
  • Powerful experimentation for research.
  • Simplifying the API by cleaning up deprecated APIs and reducing duplication.



Being one of the most used Python libraries, NumPy supports multi-dimensional arrays and matrices for mathematical and logical operations. But that’s not its only advantage. NumPy is also known for its speed and vectorization capabilities. Its highly efficient data-structures and capability to vectorize operation boost performance and parallelization capabilities.

We use certain NumPy operations as the foundation for the Machine Learning stack to:

  • create vectors and matrices;
  • create a sparse matrix;
  • select one or more element in vector or matrix;
  • describing the size and dimensions of a matrix;
  • apply functions to multiple elements;
  • find the minimum and maximum values;
  • calculate average, variance and standard deviation;
  • transport a vector or a matrix;
  • find the determinant and rank of a matrix;
  • extract the diagonal of a matrix;
  • find eigenvectors and eigenvalues;
  • calculate dot products;
  • add, multiply, and subtract matrices;
  • generate random values.



pandas is an open source package that provides flexible and high-performance data structure manipulation, modeling, and analysis tools for Python. Data analysis and modeling were never the strong sides of Python programming language and its functionality in this sphere, except data wrangling, leaves much to be desired. pandas library changed this situation. Now work with data in Python becomes intuitive. In collaboration with the powerful IPython toolkit and other libraries, pandas improves performance and productivity of Python data analysis.

Main pandas features:

  • size mutability and integrated handling of missing data;
  • automatic and explicit data alignment;
  • powerful performance of split/apply/combine operations on data sets, for both aggregating and transforming data;
  • flexible reshaping and pivoting of data sets;
  • intuitive merging and joining of data sets;
  • intelligent label-based slicing, indexing, and subsetting of large data sets;
  • robust IO tools for loading, reading, and writing data between in-memory data structures and different formats: CSV, Excel files, SQL databases, and ultrafast HDF5 format;
  • hierarchical axis indexing that provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure.



Python-based computing package PyTorch is considered to be a replacement for NumPy library that is aimed at Machine Learning. It provides maximum speed and flexibility by making use of multiple GPUs. The end-to-end Machine learning framework PyTorch is a child of the Facebook Artificial Intelligence research group. It helps the social network to apply face recognition and auto-tagging.

The key features of PyTorch include:

  • easy-to-use API for integration with other data science and Machine learning frameworks;
  • multi-dimensional arrays that can be used on a GPU.
  • dynamic computation graphs build on each step of code execution.

The latest version PyTorch 1.3 contains the long-awaited features:

  • Support for named tensors.
  • Mobile support. An end-to-end API for both Android and iOS devices.
  • Quantization of tensors. Post-training dynamic quantization, post-training static quantization and quantization aware training are the new kinds of quantization methods offered by PyTorch;
  • Increased support for TensorBoard including 3d Mesh and Hyperparameter;
  • Enhance performance in torch.nn, torch.nn.functional, Autograd engine etc.



Scikit-learn is a free and open-source Machine Learning library for Python. This library offers efficient easy-to-use tools for data mining and data analysis. Basically, Scikit-learn is a Python module that provides a big number of advanced Machine Learning algorithms for supervised and unsupervised problems.

Scikit-learn is mostly written in Python, with some additions in Cython to improve performance. The required dependencies include numerical library NumPy, scientific library SciPy, and a working C/C++ compiler. Scikit-learn is not concerned with loading, manipulating, and summarizing data as SciPy or Pandas, but focuses on modeling data instead.

Scikit-learn offers a large range of models that are grouped to perform different objectives:

  • data preprocessing - normalization, changing raw data into suitable representations.
  • clustering - automatic grouping of similar objects into sets (algorithms: KMeans, mean-shift, spectral clustering).
  • classification - identifying to which category an object belongs to (algorithms: SVM, random forest, nearest neighbors, etc.)
  • regression - predicting a continuous-valued variable with existing values and related attributes (algorithms: ridge regression, SVR, Lasso).
  • cross Validation - estimating the performance of estimator (supervised models on unseen data).
  • dimensionality Reduction - reducing the number of random variables in data for summarization, visualization and feature selection (algorithms: feature selection, PCA, non-negative matrix factorization).
  • ensemble methods - combining the predictions of multiple estimators (supervised models).
  • feature extraction - defining attributes in image and text data in a format supported by Machine Learning algorithms.
  • feature selection -identifying meaningful attributes to improve accuracy scores or performance.
  • manifold Learning (approach to nonlinear dimensionality reduction) - summarizing and represents complex multi-dimensional data.
  • model selection - comparing models, parameter tuning (modules: grid search, metrics, cross validation).

Why choose Quintagroup for your ML project?

Machine learning and AI were successfully applied in the Predictions subsystem of OCDS Analytics platform developed by Quintagroup. The subsystem was designed to get the most probable unit of measure based on the inputs. More information about our use case you can find in the recommended topics or just by approaching us clicking the link.

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