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Learn Classification, 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.
Comprehensive program covering data preprocessing, visualization, SQL/NoSQL databases, and machine learning techniques. Offers practical skills in Excel, Python, and MS Azure for both beginners and experienced coders.
Comprehensive exploration of deep learning techniques for visual computing, covering neural networks, autoencoders, and CNNs with hands-on implementations using Python and popular datasets.
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…
Comprehensive review of life science fundamentals: cells, genetics, evolution, body systems, ecology, and classification. Covers key concepts for a solid understanding of biology.
Guidance on histopathological reporting of appendiceal carcinomas and mucinous neoplasms, covering classification, staging, and challenging cases for pathologists.
Explore brachiopod anatomy, morphology, and classification. Learn to distinguish these marine invertebrates from mollusks and understand their evolutionary history and diverse shell shapes.
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 the fundamentals and advanced concepts of Gradient Boost for regression and classification in machine learning.
Learn to implement XGBoost for regression and classification, explore its mathematical foundations, and master advanced optimizations in Python.
Learn key technologies and techniques, including R and Apache Spark, to analyse large-scale data sets to uncover valuable business information.
Further develop your knowledge of big data by applying the skills you have learned to a real-world data science project.
Learn the basics of machine learning and how you can create a machine learning model with Python.
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.
Dive into machine learning fundamentals using R. Explore supervised and unsupervised learning, statistical modeling, and various algorithms. Gain practical insights through real-life examples and societal impacts.
Explore cataloging techniques, tools, and technologies for library support staff, focusing on description, access, classification, and modern discovery systems.
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