Probability for Data Science & Machine Learning

Overview

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Dive into a comprehensive 47-minute video tutorial on probability for data science and machine learning. Learn key concepts including union, intersection, conditional probability, Bayes' theorem, combinatorics, distributions, and more. Explore real-world applications of probability formulas and gain insights into choosing appropriate methods. Follow along with a detailed table of contents to easily navigate specific topics. Master fundamental mathematical principles essential for understanding machine learning and data analytics.

Syllabus

Intro.
Probability Definitions.
Union.
Intersection.
Complement.
Conditional Probability.
Contingency Table.
Addition Rule.
Joint Probability.
Dependent vs. Independent.
Independent Events.
Mutually Exclusive Events.
Venn Diagrams.
Tree Diagrams.
Total Probability.
Bayes' Theorem.
Combinatorics.
Permutations.
Combinations.
Poker Probabilities.
Which to use?.
Variations.
Types of Variables.
Discrete Uniform Distribution.
Probability Mass.
Variance.
Relative Frequency Histogram.
Cumulative Distribution.
Expected Value.
Standard Deviation.
Normal Distribution.
Z Score.
Negative Z Score.
Reverse Z Score.
Confidence Intervals.
Binomial Probability.
Poisson Distribution.
Geometric Probability.
Central Limit Theorem.
Negative Binomial Probability.
Which to use?.
Negative Binomial Formula.
Hypergeometric Distribution.
Continuous Probability.
Continuous Probability Formula.
Exponential Distribution.
Exponential Formulas.