{"id":9506,"date":"2026-08-10T10:08:11","date_gmt":"2026-08-10T10:08:11","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/oreilly-the-essential-machine-learning-foundations-math-probability-statistics-and-computer-science-video-collection-2022-3\/"},"modified":"2026-08-10T10:08:11","modified_gmt":"2026-08-10T10:08:11","slug":"oreilly-the-essential-machine-learning-foundations-math-probability-statistics-and-computer-science-video-collection-2022-3","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/oreilly-the-essential-machine-learning-foundations-math-probability-statistics-and-computer-science-video-collection-2022-3\/","title":{"rendered":"Oreilly \u2013 The Essential Machine Learning Foundations: Math, Probability, Statistics, and Computer Science (Video Collection) 2022-3"},"content":{"rendered":"
\n

Descriptions<\/h2>\n

The Essential Machine Learning Foundations: Math, Probability, Statistics, and Computer Science (Video Collection), An outstanding data scientist or machine learning engineer must master more than the basics of using ML algorithms with the most popular libraries, such as scikit-learn and Keras. To train innovative models or deploy them to run performantly in production, an in-depth appreciation of machine learning theory is essential, which includes a working understanding of the foundational subjects of linear algebra, calculus, probability, statistics, data structures, and algorithms. When the foundations of machine learning are firm, it becomes easier to make the jump from general ML principles to specialized ML domains, such as deep learning, natural language processing, machine vision, and reinforcement learning.<\/p>\n

The more specialized the application, the more likely its implementation details are available only in academic papers or graduate-level textbooks, either of which assume an understanding of the foundational subjects. Linear Algebra for Machine Learning LiveLessons provides you with an understanding of the theory and practice of linear algebra, with a focus on machine learning applications. Calculus for Machine Learning LiveLessons introduces the mathematical field of calculus\u201a\u00c4\u00eethe study of rates of change\u201a\u00c4\u00eefrom the ground up. It is essential because computing derivatives via differentiation is the basis of optimizing most machine learning algorithms, including those used in deep learning, such as backpropagation and stochastic gradient descent. Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons provides you with a functional, hands-on understanding of probability theory and statistical modeling, with a focus on machine learning applications.<\/p>\n

What you\u2019ll learn<\/h3>\n