Machine Learning is a form of Artificial intelligence (AI) that allows software programs to improve their prediction accuracy without being expressly designed to do so. In order to anticipate new output values, machine learning algorithms use past data as inp

1. Knime – Knime is a graphical user interface-based open-source machine learning tool. Knime’s best feature is that it doesn’t require any programming knowledge. Knime’s services are still available to be used. It’s commonly used for data-related purposes. For instance, data manipulation, data mining, and so forth.
2. Accord.net – Accord.net is a framework for computational machine learning. It includes both image and audio packages. Such software aids in the training of models as well as the creation of interactive applications. For instance, audition, computer vision, and so on.
3. Scikit-Learn – Scikit-Learn is a free machine learning framework. Because it is used for multiple purposes, it is a unified platform. Regression, clustering, classification, dimensionality reduction, and preprocessing are all aided by it. NumPy, Matplotlib, and SciPy are the three main Python libraries that Scikit-Learn is built on top of. Additionally, it will assist you in both testing and training your models.
4. TensorFlow – TensorFlow is an open-source framework that can be used for both large-scale and numerical machine learning. It’s a combination of machine learning and neural network models. Furthermore, it is a good Python friend. TensorFlow’s most notable feature is that it runs on both CPU and GPU.
5. Weka – It’s free and open-source. A graphical user interface is used to access it. The software is very easy to use. This tool is used in both research and education. Weka also gives you access to a variety of other machine learning tools. For instance, R, Scikit-learn, and others.