LinkedIn Learning - Machine Learning Foundations - Probability

Files Included :

01 - Probability for machine learning.mp4 (3.96 MB)
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02 - What you should know.mp4 (2.91 MB)
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01 - Defining probability.mp4 (7.48 MB)
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02 - Applications of probability in ML.mp4 (8.03 MB)
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03 - Sample space and events.mp4 (12.99 MB)
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04 - Random variables.mp4 (8.49 MB)
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05 - Examples of probability.mp4 (10.95 MB)
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01 - Probability of an event.mp4 (8.6 MB)
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02 - The sum rule.mp4 (4.51 MB)
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03 - The product rule.mp4 (6.02 MB)
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04 - The sum rule extended.mp4 (5.68 MB)
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05 - Conditional probability.mp4 (7.78 MB)
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06 - Total probability.mp4 (10.24 MB)
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01 - Joint and marginal probability.mp4 (5.18 MB)
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02 - Joint probability tables.mp4 (7.82 MB)
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03 - The chain rule for probability.mp4 (9.06 MB)
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01 - Probability distributions.mp4 (7.09 MB)
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02 - Histograms and probability.mp4 (7.95 MB)
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03 - Discrete probability distribution.mp4 (5.44 MB)
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04 - The binomial distribution.mp4 (12.43 MB)
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05 - The Bernoulli distribution.mp4 (6.48 MB)
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06 - The Poisson distribution.mp4 (6.83 MB)
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01 - The continuous probability distribution.mp4 (9.62 MB)
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02 - Central limit theorem.mp4 (7.6 MB)
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03 - The law of large numbers.mp4 (3.36 MB)
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01 - Introduction to Bayes' theorem.mp4 (10.09 MB)
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02 - Example of Bayes' theorem in practice.mp4 (7.76 MB)
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03 - Naive Bayes' clasifier.mp4 (5.3 MB)
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01 - Next steps.mp4 (1.93 MB)
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