Все публикации

Confidence Interval Calculation:0:10:03

Confidence Interval Calculation: Utilizing the Underlying Statistical Distribution | 11_25

Confidence Intervals (CI)0:07:43

Confidence Intervals (CI) for Accurate Data Analysis in Machine Learning | 11_24

Understanding and Applying0:13:01

Understanding and Applying Correlations in Data Science and Machine Learning | 11_23

Correlation vs Causation0:04:18

Correlation vs Causation Explained: Key Concepts for DS and Avoiding Pitfalls in ML | 11_22

Spearman Rank Correlation0:06:48

Spearman Rank Correlation Coefficient: Measuring Non-Linear Relationships in Machine Learning| 11_20

Pearson Correlation Coefficient0:12:32

Pearson Correlation Coefficient Explained: A Guide for Machine Learning Enthusiasts | 11_20

Covariance in Data0:14:00

Covariance in Data Science: What It Is and Why It Matters | 11_19

Non-Gaussian Distributions: Real-World0:25:35

Non-Gaussian Distributions: Real-World Applications for Data Analysis and ML | 11_18

Box-Cox Transformation Explained:0:11:17

Box-Cox Transformation Explained: A Tool for Data Normalization in ML | 11_17

The Power of0:11:39

The Power of Power Law: Distribution Patterns in Machine Learning and Statistics | 11_16

Log-Normal Distribution Explained:0:11:41

Log-Normal Distribution Explained: Applications in Data Science and ML | 11_15

Bernoulli vs Binomial0:10:44

Bernoulli vs Binomial Distribution: Key Concepts for Data Science and Machine Learning | 11_14

Random Sampling with0:09:44

Random Sampling with Uniform Distribution: Key Techniques for Data Science | 11_13

Mastering Discrete and0:12:02

Mastering Discrete and Continuous Uniform Distributions for Data Science | 11_12

Chebyshev’s Inequality Explained:0:19:41

Chebyshev’s Inequality Explained: How to Measure Data Spread in Probability | 11_11

Mastering Data Distributions:0:16:16

Mastering Data Distributions: Essential for Machine Learning and Analytics | 11_10

Q-Q Plot Explained:0:22:24

Q-Q Plot Explained: Testing Normality of Random Variables | 11_9

Sampling Distribution and0:18:07

Sampling Distribution and Central Limit Theorem Explained: Essential Concepts for M/L | 11_8

Kernel Density Estimation0:06:28

Kernel Density Estimation Explained: A Deep Dive into Data Smoothing Techniques | 11_7

Z-Scores Explained: Standard0:05:20

Z-Scores Explained: Standard Normal Variate and Why Standardization Matters | 11_6

Mastering Data Shape:0:04:25

Mastering Data Shape: Symmetric Distribution, Skewness, and Kurtosis in Statistics | 11_5

Cumulative Distribution Function0:10:30

Cumulative Distribution Function (CDF) of Gaussian Normal Distribution: A Comprehensive Guide | 11_4

Probability Density Function0:26:17

Probability Density Function (PDF): Exploring the Gaussian Normal Distribution | 11_3

From Data to0:06:48

From Data to Insights: Population and Sample in Probability and Statistics | 11_2