20 Machine Learning Tools for 2024 Elevate Your AI Skill

TensorFlow: 

 An open-source machine learning framework developed by Google 

PyTorch: 

 An open-source machine learning library developed by Facebook's AI Research lab 

Scikit-learn: 

 A simple and efficient tool for data mining and data analysis 

Keras:  

A high-level neural networks API, capable of running on top of TensorFlow, CNTK, or Theano 

Apache Spark MLlib:  

A scalable machine learning library built on top of Apache Spark 

XGBoost: 

 An optimized distributed gradient boosting library 

LightGBM: 

 A gradient boosting framework that uses tree-based learning algorithms.

CatBoost:  

A gradient boosting library developed by Yandex.

H2O.ai:  

An open-source platform for machine learning and AI.

Microsoft Azure Machine Learning: 

 A cloud-based service for building, training, and deploying machine learning models 

IBM Watson Studio:  

A platform that provides tools for data scientists, application developers, and subject matter experts 

Databricks:  

A unified analytics platform that provides a collaborative environment for data science and engineering 

Amazon SageMaker:  

A fully managed service for building, training, and deploying machine learning models at scale 

Google Cloud AI Platform: 

 A suite of machine learning tools provided by Google Cloud.

AutoML:  

Automated machine learning tools such as Google's AutoML, H2O.ai's Driverless AI, and others 

MLflow: 

 An open-source platform for managing the machine learning lifecycle 

Jupyter Notebooks: 

 An open-source web application that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

Docker:  

A platform for developing, shipping, and running applications in containers.

Kubeflow:  

An open-source platform for deploying, monitoring, and managing machine learning models on Kubernetes 

Weka:  

A collection of machine learning algorithms for data mining task