Top 9 Machine Learning Frameworks for AI & ML Experts

Machine learning frameworks play a crucial role in developing and deploying artificial intelligence and machine learning models.

Here are the top 9 Machine Learning frameworks for AI & ML experts in the year 2023. these frameworks empower professionals to tackle complex challenges and drive innovation in AI and ML.

PyTorch is a deep learning framework that puts Python first. PyTorch Lightning is the deep learning framework for professional AI researchers and machine learning engineers.

1. PyTorch

Scikit-learn is the most useful and robust library for machine learning in Python. It provides a selection of efficient tools for machine learning.

2. Scikit-learn

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research

3. Caffe

XGBoost is an open-source software library which provides a regularizing gradient boosting framework for C++, Java, Python, R, Julia, Perl, and Scala.

4. XGBoost

Microsoft Cognitive Toolkit (CNTK) is an open source library that contains all the basic building blocks to build a neural network.

5. Microsoft Cognitive Toolkit

Keras is an open-source library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.

6. Keras

TensorFlow is one of the most popular and widely adopted machine learning frameworks. it Developed by Google Brain.

7. TensorFlow

Theano is a Python library that allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

8. Theano

Amazon Machine Learning is a cloud-based service that consists of visualization tools for developers at all skill levels.

9. Amazon Machine Learning

The top 8 machine learning frameworks discussed in this web story are invaluable tools for AI and ML experts. These frameworks provide powerful capabilities and a wide range of features.