The top 20 Machine Learning tools that were widely used and highly regarded in the industry include:
1. Python: A versatile programming language with popular libraries like NumPy, Pandas, and scikit-learn for Machine Learning tasks.
2. R: A statistical programming language with extensive libraries for data manipulation and modeling.
3. scikit-learn: A Python library offering various algorithms and tools for data preprocessing, model selection, and evaluation.
4.TensorFlow: An open-source deep learning framework developed by Google for building neural networks.
5. Keras: An easy-to-use high-level neural networks API that runs on top of TensorFlow.
6. PyTorch: Another deep learning framework that offers dynamic computation and is widely used for research and production.
7. XGBoost: A gradient boosting library known for its high performance in structured tabular data problems.
8.LightGBM: A fast and efficient gradient boosting framework designed for large datasets.
9. CatBoost: A gradient boosting library optimized for categorical features.
10. WEKA: A collection of Machine Learning algorithms for data mining tasks, implemented in Java.
11. KNIME: An open-source platform for data analytics, reporting, and integration.
12.Orange: A visual programming tool for data visualization, analysis, and Machine Learning.
13.H2O.ai: An open-source platform for Machine Learning with in-built autoML capabilities.
14. Microsoft Azure ML: A cloud-based platform that provides tools for building, deploying, and managing Machine Learning models.
15. Google Cloud AI Platform: Google’s cloud-based Machine Learning platform for data scientists and developers.
16. Amazon SageMaker: Amazon’s fully managed service for building, training, and deploying Machine Learning models.
17. IBM Watson Studio: IBM’s integrated development environment for AI and data science projects.
18. RapidMiner: An integrated data science platform for data preparation, Machine Learning, and model deployment.
19. DataRobot: An automated Machine Learning platform that helps build and deploy accurate models quickly.
20. AutoML: Not a specific tool, but the term refers to Automated Machine Learning platforms like AutoML from Google and Auto-Sklearn that automate the model selection and hyperparameter tuning process.
