In 2019, Python was the leader in the analytics, Data Science, and Machine Learning software market with a share of 65.8%. The language continues to grow in 2021 as it is a highly useful tool for building Artificial Intelligence apps and working with data. Python application development enables developers to play around with both structured and unstructured data in the most seamless way possible.
But Python isn't enough - there needs to be a Machine Learning framework where engineers can work with data. The frameworks include the tools, libraries, and interfaces that help in Python app development for ML-based projects and Deep learning solutions.
Both Machine Learning (ML) and Deep learning (SL) utilize a few similar functions except for the fact that Deep Learning is based more on neural networks and connections. The best Python frameworks and libraries offer functionality to build both ML and DL based solutions.
But what are those Machine Learning frameworks and libraries? This article will highlight the top 11 Python Machine Learning libraries and Deep Learning frameworks that developers use for building advanced AI-based solutions.
11 Best Python Frameworks and Libraries for ML and DL
Machine Learning and Deep Learning require advanced frameworks that help with functions like image detection, language processing, pattern detection, and more. Python frameworks for Machine Learning engineers provide these capabilities through unique tools.
Here are the top frameworks and Python libraries for Machine Learning -
- Another of the highly known Python Machine Learning libraries in Python. Engineers use the library for data manipulation and analysis. It works amazingly well with structured data for Machine Learning algorithms. It offers great features to deploy ML and DL-based applications.
- Pandas assists with data reshaping, dataset joining, data filtration, alignment and easily handles missing data as well. It also provides a 2-D representation of data to make things convenient for python developers.
- It is a high-level library designed to perform operations using neural network structures in TensorFlow. It assists with the creation of primary Python objects that relate to a specific part of the neural network. It simplifies the design of the neural network, ensuring developers get the best results out of the process.
- Sonnet divides the modules into submodules or can pass to other modules during the internal process. Sonnet Machine Learning models can be easily integrated with TensorFlow code and other high-level libraries.
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Which ML Framework is Best?
Now that we have learned the top 11 frameworks for Machine Learning development, we know that all of them have a few similar functions but can perform different operations. It depends upon the complexity of the ML project before deciding which framework or library is the best suited for an application.
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