Mathematics, Probability & Statistics for Machine Learning

Mathematics, Probability & Statistics for Machine Learning

For Data Science, Artificial Intelligence (AI), Machine Learning, and Deep Learning learn math, probability, and statistics.

What you’ll learn

  • You’ll learn about several types of distributions, such as normal, binomial, and Poisson.
  • Learn everything there is to know about set theory, permutation, and combination.
  • Learn how to connect probability and statistics.
  • You’ll discover how to use Bayes’ theorem.
  • From the ground up, learn probability theory.
  • You’ll discover mutually exclusive and non-mutually exclusive probability laws.
  • You’ll study probability’s dependent and independent events.
  • There’s much more…


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
  • Probability Addition Rules
  • 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
You’ll also have access to the Q&A section, where you may ask questions and get answers. You can also message me directly.
You will receive a certificate of accomplishment upon completion of this course, which you can publish on your LinkedIn profile for our colleagues and potential employers to see!

For whom is this course intended:

  • Those who are likely to be starting from the ground up.
  • Probability is now being studied by students.
  • In the field of data science, I’m a professional.
  • Bankers are those who work in the banking industry.
  • Individuals who work in the insurance industry.

Who this course is for:

  • Professionals and students.
  • Those who need to know how to solve challenges using probability

Last updated: 1/2022 | Size: 2.2 GB
Click to get:

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