## 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

Mathematics, Probability & Statistics for Machine Learning

- 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…

#### Description

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

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