Machine learning has become a core technology in the field of artificial intelligence with its excellent performance and wide range of applications. Traditional machine learning course teaching models often focus on theoretical explanations and lack sufficient practical guidance, making it difficult for students to apply their knowledge to solve practical problems. This article explores feasible ways to reform the teaching of machine learning courses, by strengthening the close integration of basic knowledge and practical skills, as well as optimizing course evaluation methods, significantly improving students' learning effectiveness and practical application abilities.
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Yan Wang (2024) studied this question.
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