Statistics for Machine Learning A-Z

Briefly Explained

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Contents
· Introduction
  ∘ Numerical Variable
  ∘ Categorical Variable
  ∘ Continuous Variable
  ∘ Discrete variable
  ∘ Dependent Variable
  ∘ Independent Variable
  ∘ Observational Studies
  ∘ Experimental Studies
  ∘ Simple Random Sample
  ∘ Stratified Sample
  ∘ Placebo Effeect
  ∘ Generalizability
  ∘ Histogram
  ∘ Dotplot
  ∘ Boxplot
  ∘ IQR
  ∘ Q3
  ∘ Q1
  ∘ Left skewed
  ∘ Right Skewed
  ∘ Symmetric
  ∘ Mean
  ∘ Median
  ∘ Average
  ∘ Variance
  ∘ Standard deviation
  ∘ Mode
  ∘ Null Hypothesis
  ∘ Alternative Hypothesis
  ∘ P-Value
  ∘ Law of Large Numbers
  ∘ Mutually Exclusive ( Disjoint)
  ∘ Non-disjoint
  ∘ Probability Trees
  ∘ Normal Distribution
  ∘ Binomial Distribution
  ∘ Bernoulli Distribution
  ∘ PDF (Probability Density Function)
  ∘ Z Score
  ∘ Percentiles
  ∘ Sampling Variability
  ∘ Central Limit Theorem
  ∘ Confidence Interval
  ∘ Significance Level
  ∘ Power
  ∘ Accuracy
  ∘ Precision
  ∘ Statistical Inference
  ∘ Type 1 Error
  ∘ Type 2 Error
  ∘ T Distribution
  ∘ Degrees of Freedom
  ∘ Distribution
· Conclusion

Introduction

Programming, Statistics, Calculus.

These are 3 things that you should be familiar with if you would like to be involved in Machine Learning.

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While there are too many courses that existed in the market, I love creating that kind of article to remind myself of these terms.

That helps me refresh my memories and make repetition.

Repetition is the mother of learning, the father of action, which makes it the architect of accomplishment.” Zig Ziglar

Whether you are at the beginning of your Data Science or Machine Learning career or experienced one, that article will serve you to create a neural path in your mind about Statistics.

Terms

Numerical Variable

The value that contains an integer.

Categorical Variable