Data Analysis of College Practice Teaching Based on Machine Learning Algorithm

With the rapid development of information technology, especially the wide application of big data and artificial intelligence technology, the field of education is undergoing unprecedented changes. Among them, practical teaching in colleges and universities, as an important part of modern education system, is facing severe challenges. However, the traditional practice teaching mode often has some problems, such as uneven distribution of resources, disjointed teaching content and market demand, and single teaching method, which limit the improvement of practice teaching effect. In this regard, based on the actual needs, this paper will deeply analyze the application feasibility of artificial intelligence and machine learning in the reform of practical teaching in colleges and universities, and put forward a set of data analysis and processing scheme of practical teaching in colleges and universities, aiming at improving the effectiveness and personalized level of practical teaching through the integration, mining and analysis of multi-source data. Practice has proved that machine learning algorithms such as fuzzy C-means (FCM) clustering analysis and frequent pattern growth (FP-growth) correlation analysis are adopted in the scheme, and the data involved in college practice teaching are deeply analyzed, and the feasibility and effectiveness of the model are verified by practical tests, which provides scientific basis and decision support for college practice teaching.