The Resource Probability and Statistics for Machine Learning, Krohn, Jon
Probability and Statistics for Machine Learning, Krohn, Jon
Resource Information
The item Probability and Statistics for Machine Learning, Krohn, Jon represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium.This item is available to borrow from 1 library branch.
Resource Information
The item Probability and Statistics for Machine Learning, Krohn, Jon represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Merrimack Valley Library Consortium.
This item is available to borrow from 1 library branch.
 Summary
 9 Hours of Video Instruction Handson 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, handson 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 Rsquared 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 highlevel software libraries (e.g., scikitlearn, 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..
 Language

 eng
 eng
 Edition
 1st edition
 Extent
 1 online resource (1 video file, approximately 8 hr., 58 min.)
 Label
 Probability and Statistics for Machine Learning
 Title
 Probability and Statistics for Machine Learning
 Statement of responsibility
 Krohn, Jon
 Language

 eng
 eng
 Summary
 9 Hours of Video Instruction Handson 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, handson 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 Rsquared 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 highlevel software libraries (e.g., scikitlearn, 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..
 Characteristic
 videorecording
 http://library.link/vocab/creatorName
 Krohn, Jon
 http://library.link/vocab/relatedWorkOrContributorName
 O'Reilly Media Company
 Label
 Probability and Statistics for Machine Learning, Krohn, Jon
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 twodimensional moving image
 Content type code

 tdi
 Content type MARC source
 rdacontent
 Dimensions
 unknown
 Edition
 1st edition
 Extent
 1 online resource (1 video file, approximately 8 hr., 58 min.)
 Issuing body
 Made available through: O'Reilly Media Company.
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Reproduction note
 Electronic reproduction.
 Specific material designation
 remote
 System control number
 (CaSebORM)9780137566273
 System details
 Mode of access: World Wide Web
 Label
 Probability and Statistics for Machine Learning, Krohn, Jon
 Carrier category
 online resource
 Carrier category code

 cr
 Carrier MARC source
 rdacarrier
 Color
 multicolored
 Content category
 twodimensional moving image
 Content type code

 tdi
 Content type MARC source
 rdacontent
 Dimensions
 unknown
 Edition
 1st edition
 Extent
 1 online resource (1 video file, approximately 8 hr., 58 min.)
 Issuing body
 Made available through: O'Reilly Media Company.
 Media category
 computer
 Media MARC source
 rdamedia
 Media type code

 c
 Reproduction note
 Electronic reproduction.
 Specific material designation
 remote
 System control number
 (CaSebORM)9780137566273
 System details
 Mode of access: World Wide Web
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<div class="citation" vocab="http://schema.org/"><i class="fa faexternallinksquare fafw"></i> Data from <span resource="http://link.mvlc.org/portal/ProbabilityandStatisticsforMachineLearning/ABsC3VPy0aQ/" typeof="Book http://bibfra.me/vocab/lite/Item"><span property="name http://bibfra.me/vocab/lite/label"><a href="http://link.mvlc.org/portal/ProbabilityandStatisticsforMachineLearning/ABsC3VPy0aQ/">Probability and Statistics for Machine Learning, Krohn, Jon</a></span>  <span property="potentialAction" typeOf="OrganizeAction"><span property="agent" typeof="LibrarySystem http://library.link/vocab/LibrarySystem" resource="http://link.mvlc.org/"><span property="name http://bibfra.me/vocab/lite/label"><a property="url" href="http://link.mvlc.org/">Merrimack Valley Library Consortium</a></span></span></span></span></div>