Drexel University 3 credits CS
Machine learning (ML) learns concepts from data to perform complex tasks to solve a variety of challenging problems. With the growth and abundance of data sources and types, ML methods become more sophisticated and give rise to applications in new areas accomplishing tasks perceived as impractical or not feasible before. This course educates students to recognize the relevant factors in applying ML methods and architectures to different application problems in various application domains. The focus on specific application domains, tasks, and areas may vary depending on students' interest but will cover the essential problem areas of artificial intelligence such as vision, natural language, recommender systems, and applications in the biomedical field.
Our subject experts help with essays, research papers, case studies, discussion posts, and capstone projects for these programs β written from scratch to your course requirements.
Program and course information is reproduced from each university's published academic catalog for informational purposes and may change. Always confirm current requirements with your university. We are an independent academic support service and are not affiliated with or endorsed by the universities listed.
Expert guides on essay writing, citation styles, study skills, and academic success.
View All Articles
Learn how to write a good research question for a paper. Narrow your topic, identify a research problem, and create a focused question with practical examples.
Learn how to choose a research topic you can actually research. Discover how to narrow broad ideas, find reliable sources, and develop a focused research question.
Learn how to cite sources in a research paper using APA, MLA, and Chicago styles, with in-text citation examples and reference-list tips.