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Learn Clustering, earn certificates with free online courses from Harvard, Stanford, MIT, University of Pennsylvania and other top universities around the world. Read reviews to decide if a class is right for you.
Leverage generative AI across BI lifecycle: data handling, analysis, visualization, and reporting. Learn ethical considerations and future trends through hands-on labs and real-world case studies.
Master distributed machine learning using PySpark MLlib to build scalable models, process large datasets, and implement advanced algorithms for classification, regression, and clustering in a big data environment.
Unraveling the mysteries of Data Mining and Big Data, this course is a must-have for any budding Data Scientist. 最有趣的理论+最有用的算法=不得不学的数据科学。
Learn how to discover knowledge in data via data mining.
Learn about the elements of AI, machine learning and computer vision. Discover the open-source AI tools, and hidden AI gems. Learn how to play with architectural design data using freely available AI model.
Learn about the key design strategies required to develop adequate housing and inclusive dwelling environments for sustainable urban development.
Comprehensive guide to Proxmox virtualization platform: installation, management, networking, storage, clustering, and high availability for efficient server virtualization and containerization.
Get a simplified overview of the top tools in artificial intelligence.
Explore machine learning basics, algorithms, and models using Python. Learn supervised and unsupervised techniques, statistical modeling, and real-world applications. Gain practical skills in classification, regression, clustering, and dimensionality red…
This course covers feature extraction techniques and the representation of patterns in feature space. Measure of similarity between two patterns. Statistical, nonparametric and neural network techniques for pattern recognition have been discussed in this…
Learn to build, evaluate, and implement Random Forests, including handling missing data and clustering. Gain practical skills using R for this powerful machine learning technique.
Explore fairness in social networks, examining information access, disadvantage, and clustering. Learn about interventions for equal access and the implications of network-based information flow.
Comprehensive introduction to data science fundamentals, covering analysis techniques, tools, and ethical considerations. Includes hands-on Python exercises for practical application of concepts.
Explore key machine learning concepts and techniques, from linear regression to deep learning, clustering, and dimensionality reduction, with hands-on applications.
Explore multivariate statistical methods, including t-tests, ANOVA extensions, dimension reduction, and clustering techniques. Learn to select, implement, and interpret analyses using SAS and Minitab.
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