Top 10 Python Packages For Machine Learning

1. TensorFlow

  • Supports reinforcement learning and other algorithms.
  • Provides computational graph abstraction.
  • Offers a very large community.
  • Provides TensorBoard, which is a tool for visualizing ML models directly in the browser.
  • Production ready.
  • Can be deployed on multiple CPUs and GPUs.
  • Runs dramatically slower than other frameworks utilizing CPUs/GPUs.
  • Steep learning curve compared to PyTorch.
  • Computational graphs can be slow.
  • Not commercially supported.
  • Not very toolable.

2. Pytorch

  • Contains tools and libraries that support Computer Vision, NLP , Deep Learning, and many other ML programs.
  • Developers can perform computations on Tensors with GPU acceleration.
  • Helps in creating computational graphs.
  • Modeling process is simple and transparent.
  • The default “define-by-run” mode is more like traditional programming.
  • Uses common debugging tools such as pdb, ipdb or PyCharm debugger.
  • Uses a lot of pre-trained models and modular parts that are easy to combine.
  • Because PyTorch is relatively new, there are comparatively fewer online resources to be found. This makes it harder to learn from scratch, although it is intuitive.
  • PyTorch is not widely considered to be production-ready compared to Google’s TensorFlow, which is more scalable.

3 SciPy

  • Great for image manipulation.
  • Provides easy handling of mathematical operations.
  • Offers efficient numerical routines, including numerical integration and optimization.
  • Supports signal processing.
  • There is both a stack and a library named SciPy. The library is part of the stack. Beginners who don’t know the difference may become confused.

4 Keras

  • Great for experimentation and quick prototyping.
  • Portable.
  • Offers easy expression of neural networks.
  • Great for use in modeling and visualization.
  • Slow, since it needs to create a computational graph before it can perform operations.

5 NumPy

  • Intuitive and interactive.
  • Offers Fourier transforms, random number capabilities, and other tools for integrating computing languages like C/C++ and Fortran.
  • Versatility — other ML libraries like scikit-learn and TensorFlow use NumPy arrays as input; data manipulation packages like Pandas use NumPy under the hood.
  • Has terrific open-source community support/contributions.
  • Simplifies complex mathematical implementations.
  • Can be overkill — do not use when you can get away with Python Lists, instead.

6 Seaborn

  • Gives more attractive graphs than matplotlib.
  • Has built-in plots that matplotlib lacks.
  • Uses less code to visualize graphs.
  • Smooth integration with Pandas: data visualization and analysis combined!
  • Because Seaborn is built on matplotlib, you have to know the latter in order to use the former.
  • Seaborn relies on default themes, and as a result is not as customizable as matplotlib.

7 Scikit-Learn

  • Simple, easy to use, and effective.
  • In rapid development, and constantly being improved.
  • Wide range of algorithms, including clustering, factor analysis, principal component analysis, and more.
  • Can extract data from images and text.
  • Can be used for NLP.
  • This library is especially suited for supervised learning, and not very suited to unsupervised learning applications like Deep Learning.

8 Pandas

  • Expressive, fast, and flexible data structures.
  • Supports aggregations, concatenations, iteration, re-indexing, and visualizations operations.
  • Very flexible usage in conjunction with other Python libraries.
  • Intuitive data manipulation using minimal commands.
  • Supports a wide range of commercial and academic domains.
  • Optimized for performance.
  • It is built on matplotlib, meaning a novice programmer has to be familiar with both libraries in order to know which one would be best suited to solve their problem.
  • Less suitable for n-dimensional arrays and statistical modeling. Use NumP, SciPy or SciKit Learn instead.

9 Natural Language Toolkit (NLTK)

  • The Python library contains graphical examples, as well as sample data.
  • Includes a book and cookbook making it easies for beginners to pick up.
  • Provides support for different ML operations like classification, parsing, and tokenization functionalities, etc.
  • Acts as a platform for prototyping and building research systems.
  • Compatible with several languages.
  • Understanding the fundamentals of string processing is a prerequisite to using the NLTK framework. Fortunately, the documentation is adequate enough to assist in this pursuit.
  • NLTK does sentence tokenization by splitting the text into sentences. This has a negative impact on the performance.

10 Matplotlib

  • Flexible usage: supports both Python and IPython shells, Python scripts, Jupyter Notebook, web application servers and many GUI toolkits (GTK+, Tkinter, Qt, and wxPython).
  • Optionally provides a MATLAB-like interface for simple plotting.
  • The object-oriented interface gives complete control of axes properties, font properties, line styles, etc.
  • Compatible with several graphics backends and operating systems.
  • Matplotlib is frequently incorporated in other libraries, such as Pandas.
  • Because Matplotlib has two different interfaces (object-oriented vs MATLAB-like), a novice developer can become confused.
  • Matplotlib is a visualization library, not a data analysis library. For data analysis, you’ll need to combine it with other libraries, like Pandas.

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