{"id":12843,"date":"2026-08-10T10:51:03","date_gmt":"2026-08-10T10:51:03","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-introduction-to-data-science-and-machine-learning-2025-4\/"},"modified":"2026-08-10T10:51:03","modified_gmt":"2026-08-10T10:51:03","slug":"udemy-introduction-to-data-science-and-machine-learning-2025-4","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-introduction-to-data-science-and-machine-learning-2025-4\/","title":{"rendered":"Udemy \u2013 Introduction to Data Science and Machine Learning 2025-4"},"content":{"rendered":"
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Description<\/span><\/h2>\n

Introduction to Data Science and Machine Learning. This course provides a comprehensive and balanced introduction to the intersection of data science and machine learning, covering theory, computational methods, and practical applications. The course is designed for beginners to build a strong foundation in fundamental concepts, statistics, and mathematics, and no prior experience is required. Topics include data science fundamentals, data visualization and storytelling, linear and nonlinear regression methods, and an overview of classification techniques such as decision trees, random forests, and neural networks. It also covers unsupervised learning and uncovering hidden patterns with methods such as spectral clustering. Participants will be empowered to solve real-world problems in fields such as engineering and business using Python and its popular libraries. What you will learn includes applying quantitative modeling and data analysis techniques to solve practical problems, effectively communicating findings through data visualization, mastering statistical analysis techniques, applying data science principles to engineering challenges, applying computational tools to analyze big data, gaining a deep understanding of classic machine learning algorithms, implementing these algorithms to build intelligent systems, understanding basic mathematical and statistical principles, and creating effective data visualizations. A review of key algorithms such as regression, neural networks, and clustering methods is also an essential part of this course.<\/span><\/p>\n

What you will learn<\/span><\/h3>\n