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Crash Course
Randomness - Crash Course Statistics
There are a lot of events in life that we just can’t predict, but just because something is random doesn’t mean we don’t know or can’t learn anything about it. Today, we’re going to talk about how we can extract information from...
Curated Video
Fundamentals of Neural Networks - Cross-Entropy Loss Function
This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary.
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This clip is from the chapter "Artificial Neural Networks" of the series...
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This clip is from the chapter "Artificial Neural Networks" of the series...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Probability Model: Probability Models towards Random Variables
In this video, we will cover probability models towards random variables.
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine...
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Curse of Dimensionality
In this video, we will cover Curse of Dimensionality.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Classification
In this video, we will cover classification.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Continuous Random Variables: Uniform Distribution Python
In this video, we will cover uniform distribution Python.
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Random Variables: Binomial Python Practice
In this video, we will cover binomial python practice.
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Regression
In this video, we will cover regression.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Introduction - Basics for Data Science: Mastering Probability and Statistics in Python
In this video, we will cover a quick introduction to this section.
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning...
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning...
Packt
Fundamentals of Neural Networks - Cross-Entropy Loss Function
This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary. This clip is from the chapter "Artificial Neural Networks" of the series "Fundamentals in...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Probability Model: Probability Models BayesRule
In this video, we will cover probability models BayesRule.
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory and...
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Multivariate Gaussian
In this video, we will cover multivariate Gaussian.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Joint Distributions Solution 03
In this video, we will cover joint distributions solution 03.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Joint Distributions Solution 02
In this video, we will cover joint distributions solution 02.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Joint Distributions Exercise 03
In this video, we will cover joint distributions exercise 03.
Curated Video
Data Science and Machine Learning (Theory and Projects) A to Z - Multiple Random Variables: Joint Distributions Exercise 01
In this video, we will cover joint distributions exercise 01.
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory...
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This clip is from the chapter "Basics for Data Science: Mastering Probability and Statistics in Python" of the series "Data Science and Machine Learning (Theory...
Khan Academy
Probability Density Functions, Probability and Statistics
Beat the weatherman at his own game with this probability video! Sal shows students how to use probability density functions to predict random occurrences (such as rain). Probability problems - and inclement weather - are no match for...
Khan Academy
Khan Academy: Term Life Insurance and Death Probability
Video lesson describing how term life insurance relates to probability.
Khan Academy
Khan Academy: Discrete and Continuous Random Variables
Defining discrete and continuous random variables. Working through examples of both discrete and continuous random variables.
Khan Academy
Khan Academy: Probability Density Functions
A video abour probability density functions for continuous random variables.