Mathematics, Probability & Statistics for Machine Learning

You will master everything from Set theory to Combinatorics to Probability in this comprehensive probability course, which includes several challenges and solutions. Probability is a fundamental concept in many fields of modern research, including machine learning, risk management, inferential statistics, and business decisions.

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You’ll be able to address a variety of day-to-day commercial and scientific prediction challenges if you understand the depth of probability. This course covers, but is not limited to, the following topics:

• Sets
• Set of Universal Use
• Subsets that are both proper and improper
• Singleton Set and Super Set
• Set that is null or empty
• Set the bar high.
• Sets that are equal and equivalent
• Notes for Builders
• The Set’s Cardinality
• Operational Procedures
• The Sets Laws
• Sets, both finite and infinite
• Sets of Numbers
• Diagram of a Venn Diagram
• Set’s Union, Intersection, and Complement
• Factorial
• Permutations
• Combinations
• Theoretical Probability is a branch of probability theory that deals with the possibility of
• Probability based on empirical evidence
• Mutual and non-mutual agreements Exclusive
• Probability Multiplication Rules
• Events that are dependent and independent
• Variable at Random
• Continuous and Discrete Variables
• Z-Score
• Probability with Conditions
• Theorem of Bayes
• Binomial Distribution is a type of probability distribution.
• Poisson Distribution is a statistical model that describes the distribution of
• Distribution of the Normal
• Kurtisos and Skewedness
• T stands for distribution.
• Probability Decision Tree