The Resource Probability and Statistics for Machine Learning, Krohn, Jon

Probability and Statistics for Machine Learning, Krohn, Jon

Label
Probability and Statistics for Machine Learning
Title
Probability and Statistics for Machine Learning
Statement of responsibility
Krohn, Jon
Creator
Contributor
Author
Subject
Genre
Language
  • eng
  • eng
Summary
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..
Characteristic
videorecording
http://library.link/vocab/creatorName
Krohn, Jon
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O'Reilly Media Company
Label
Probability and Statistics for Machine Learning, Krohn, Jon
Link
https://learning.oreilly.com/library/view/-/9780137566273/
Instantiates
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
two-dimensional 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
Link
https://learning.oreilly.com/library/view/-/9780137566273/
Publication
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
two-dimensional 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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