Machine learning methods and resources: An overview
S. Goyal, A. Kaur, Neera Batra · Computational Methods in Science and Technology · 2024
Machine learning tasks are made simpler by a variety of tools that provide an extensive selection of features and ease of use. One renowned framework that excels at deep learning is TensorFlow, which makes model creation and execution quick and easy. With its extensive library of conventional methods, Scikit-learn make experimentation and implementation easier. PyTorch is a popular tool for dynamic graph analysis that may be used for both production and research purposes. A high-level API called Keras facilitates quick prototyping by streamlining deep learning workflows. Scalability is a key feature of Apache Spark, enabling networked machine learning on big datasets. Together, these technologies improve accessibility and quicken machine learning innovation across domains and sectors. This paper will expose a variety of methods within this paper that may be applied at various points of creating a robust machine learning model. It will also show you through the tasks the program completes, give case studies of a few applications, and describe which methods from machine learning are used to achieve the main objectives of the application.