Python is a huge collection of libraries that help serve various purposes. To help you understand more about this, here is an article that brings you the top 7 best Python frameworks for machine learning.
Table of Contents:
Top Python Machine Learning Libraries
Machine learning is considered the most algorithm-rich field in computer science. We must be thankful to Python and its libraries, frameworks, and modules for making us overcome those days when people had to code all big algorithms for machine learning. Therefore, new applications can now be developed by writing only fewer lines of code.
Python machine learning libraries have become the preferred language for executing machine learning algorithms and have replaced many programming languages in the industry. Defined below are the reasons why Python is popular among developers:
- It has a huge collection of libraries.
- Python is considered a beginner’s level programming language because of its easiness and simplicity.
- Python’s programming syntax is easy to learn and is of a high level if compared to Java, C, and C++.
Best Python Frameworks For Machine Learning
Now, let us look at some of the best python frameworks for machine learning:
PyTorch is the largest machine learning library that has a wide range of tools and libraries that support machine learning, computer vision, and language processing. Python machine library was introduced in 2017 and since then it has gained popularity and until today is attracting a growing number of machine learning developers.
PyTorch produces a robust framework that helps you to create calculation diagrams on the go and likewise modify them during runtime. Other than this it also has various benefits like support for multiple GPUs, custom data loaders, and simplified pre-processors.
Key Features of PyTorch:
Defined below are some of the different features that place Python among the best python frameworks for Machine Learning:
- Its extensive libraries provide solutions for every existing problem.
- Easily adaptable for people with varying skills because of its smooth implementation and integration.
- Improved productivity by decreasing the time to code and debug.
- Works seamlessly with C and C++ code modules and can also be used for Soft Computing.
NumPy can be defined as a general-purpose array-processing package. It has a large collection of different mathematical functions with the help of which NumPy can process large multi-dimensional matrices and arrays.
NumPy is very useful for handling Fourier transforms, linear algebra, and random numbers. It also serves as an effective multi-dimensional container for any generic data that is in any datatype. NumPy allows speedy computation and execution of complicated functions and has various key features like broadcasting functions, out-of-box tools for integrating C/C++, and Fortran code, etc.
Key Features of NumPy:
- Linear algebra and statistical operations.
- Random simulations and Shape manipulation.
- Support for several mathematical and logical operations.
- Selecting and Sorting capabilities.
- It comes with support for n-dimensional arrays.
Scikit-learn is another best python frameworks for machine learning built on top of two Python libraries – NumPy and SciPy. Scikit-learn was created with the idea of being easy to use but still be flexible, it was focused more on data modeling and not on other tasks like handling, loading, and visualization of data.
Scikit-learn can be used for data mining and data analysis and includes easy integration with different programming libraries like Pandas and NumPy. Scikit-learn library can manage the following main machine learning functions, classification, clustering, regression, model selection, pre-processing, and dimensionality reduction.
Key Features of Scikit-Learn:
- Built on NumPy and SciPy.
- Easily accessible to everybody, and reusable in many contexts.
- Easy and efficient tools for predictive data analysis.
- Open source and commercially usable.
With growing machine learning, many Python developers today are creating deep machine learning python libraries for analytical and scientific computing. The data structure used by SciPy is a multi-dimensional array produced by the NumPy module. SciPy library was made to work with NumPy arrays thereby providing user-friendly and efficient numerical functions.
SciPy library offers various modules for image optimization, linear algebra, special functions, integration interpolation, Ordinary Differential Equation (ODE) solving, signal and image processing, and additional computational tasks in science and analytics.
Key Features of SciPy:
- SciPy provides users with high-level commands and classes for data-visualization and data-manipulation.
- SciPy makes easier and faster development of sophisticated and specific applications.
- SciPy is open-source, which enables developers across the world to contribute to the development of additional modules.
TensorFlow is another successful framework for creating machine learning models. TensorFlow is a fast, scalable, and flexible open-source machine learning python framework used for research and production. It supports various toolkits used for creating models at varying levels of abstraction.
TensorFlow presents very stable Python and C++ APIs and has a flexible architecture which makes it run on different computational platforms CPUs, GPUs, etc. TensorFlow enables you to create and ML models not only on computers but also on mobile devices and servers with the help of TensorFlow Lite.
Key Features of TensorFlow:
- Managing deep neural networks
- Abstraction capabilities and Natural Language Processing
- Image, Text, and Speech recognition
- Partial Differential Equation
Pandas are the most popular Python library that is used for data analysis with support for quick, adaptable, and expressive data structures designed to work on both “labeled” or “relational” data. Pandas is a highly stable library for solving practical, real-world data analysis in Python, it provides extremely optimized performance.
Pandas can be used in a wide range of fields for various academic and commercial domains including finance, statistics, economics, analytics, etc. It also helps you accomplish five standard steps in the processing and interpretation of data, despite the origin of data — load, manipulate, prepare, model, and analyze.
Key Features of Pandas:
- Merging and joining and reshaping of datasets.
- Handling of missing data and data arrangement.
- Multiple indexing options such as Fancy indexing and Hierarchical axis indexing.
- Options for Data filtration.
Theano is a python machine learning library built on NumPy and has a very similar interface. It can also serve as an optimizing compiler for manipulating and evaluating various mathematical expressions. Theano can work on the GPU and CPU, although working on GPU architecture delivers faster results, therefore it can perform heavy-duty computations up to 140 times faster on GPU as compared to CPU.
Theano is a powerful Python frameworks for machine learning that allows easy defining, evaluation, and optimizing of powerful mathematical expressions. It also has built-in tools for unit-testing and validation, thereby avoiding bugs and problems.
Key Features of Theano:
- Effective integration support with NumPy
- Faster and stable evaluations of even the most complicated variables
- Capability to create custom C code for mathematical operations
So, explained above were the top best python frameworks for machine learning for you to get started with. All these libraries are exceptionally helpful when you are working on machine learning projects. This helps save your time and further provides specific functions that are needed. Using these Python machine learning libraries, you can include several analytical functions, even with the least knowledge of the algorithms that you are working with.
Every Python Library and Framework has its own advantages and limitations. With some of the latest machine learning Python libraries discussed above, you can easily get an idea about what each of these libraries has to offer and you make your choice accordingly.