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Probability and Statistics for Machine Learning /
9 Hours of Video Instruction Hands-on approach to learning the probability and statistics underlying machine learning Overview Probability and Statistics for Machine Learning (Machine Learning Foundations) LiveLessons provides you with a functional, hands-on understanding of probability theory and s...
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
Addison-Wesley Professional,
2021.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
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| 520 | |a 9 Hours of Video Instruction Hands-on approach to learning the probability and statistics underlying machine learning Overview 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. About the Instructor Jon Krohn is Chief Data Scientist at the machine learning company untapt. He authored the book Deep Learning Illustrated, an instant #1 bestseller that has been translated into six languages. Jon is renowned for his compelling lectures, which he offers in person at Columbia University and New York University, as well as online via O'Reilly, YouTube, and the SuperDataScience podcast. Jon holds a PhD from Oxford and has been publishing on machine learning in leading academic journals since 2010; his papers have been cited over a thousand times. Skill Level Intermediate Learn How To Understand the appropriate variable type and probability distribution for representing a given class of data Calculate all of the standard summary metrics for describing probability distributions, as well as the standard techniques for assessing the relationships between distributions Apply information theory to quantify the proportion of valuable signal that's present among the noise of a given probability distribution Hypothesize about and critically evaluate the inputs and outputs of machine learning algorithms using essential statistical tools such as the t -test, ANOVA, and R-squared Understand the fundamentals of both frequentist and Bayesian statistics, as well as appreciate when one of these approaches is appropriate for the problem you're solving Use historical data to predict the future using regression models that take advantage of frequentist statistical theory (for smaller data sets) and modern machine learning theory (for larger data sets), including why we may want to consider applying deep learning to a given problem Develop a deep understanding of what's going on beneath the hood of predictive statistical models and machine learning algorithms Who Should Take This Course You use high-level software libraries (e.g., scikit-learn, Keras, TensorFlow) to train or deploy machine learning algorithms and would now like to understand the fundamentals underlying the abstractions, enabling you to expand your capabilities You're a software developer who would lik ... | ||
| 542 | |f Pearson Education, Inc. |g 2021 | ||
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