Complete Math, Statistics & Probability for Machine Learning


(Updated 2023) Complete Mathematics, Probability & Statistics for Data Science, Data Analytics, Machine & Deep Learning

What you’ll learn

  • Learn Linear Algebra, Calculus for Machine and Deep Learning
  • Learn to use Python to Solve Maths Problems
  • Learn Discrete Maths for Machine and Deep Learning
  • Learn Probability theory for Machine and Deep Learning
  • Different types of distributions: Normal, Binomial, Poisson…
  • Learn set theory, permutation and combination in details
  • Understand how to link probability with statistics
  • You will learn how to apply Bayes’ theorem
  • You will learn mutually and non-mutually exclusive laws of probability
  • You will learn dependent and independent events of probaility
  • A lot more…


  • Basic maths


Start learning Mathematics, Probability & Statistics for Machine Learning TODAY!


You are welcome to this course: Complete Math, Probability & Statistics for Machine learning.

This is a highly comprehensive Mathematics, Statistics, and Probability course, you learn everything from Set theory, Combinatorics, Probability, statistics, and linear algebra to Calculus with tons of challenges and solutions for Business Analytics, Data Science, Data Analytics, and Machine Learning. Mathematics, Probability & Statistics are the bedrock of modern science such as machine learning, predictive risk management, inferential statistics, and business decisions. Understanding the depth of these will empower you to solve numerous day-to-day business and scientific prediction problems and analytical problems. This course includes but is not limited to:”

  • Sets
  • Universal Set
  • Proper and Improper Subset
  • Super Set and Singleton Set
  • Null or Empty Set
  • Power Set
  • Equal and Equivalent Set
  • Set Builder Notations
  • Cardinality of Set
  • Set Operations
  • Laws of Sets
  • Finite and Infinite Set
  • Number Sets
  • Venn Diagram
  • Union, Intersection, and Complement of Set
  • Factorial
  • Permutations
  • Combinations
  • Theoretical Probability
  • Empirical Probability
  • Addition Rules of Probability
  • Mutual and Non-mutual Exclusive
  • Multiplication Rules of Probability
  • Dependent and Independent Events
  • Random Variable
  • Discrete and Continuous Variable
  • Z-Score
  • Frequency and Tally
  • Population and Sample
  • Raw Data and Array
  • Mean
  • Introduction
  • Weighted Mean
  • Properties of Mean
  • Basic Properties of Mean
  • Mean Frequency Distribution
  • Median
  • Median Frequency Distribution
  • Mode
  • Measurement of Spread
  • Measures of Spread (Variation / Dispersion)
  • Range
  • Mean Deviation
  • Mean Deviation for Frequency Distribution
  • Variance & Standard Deviation
  • Understanding Variance and Standard Deviation
  • Basic Properties of Variance and Standard Deviation
  • Variable | Dependent- Independent – Moderating – Ordinal…
  • Variable
  • Types of Variable
  • Dependent, Independent, Control Moderating and Mediating Variables
  • Correlation
  • Regression & Collinearity
  • Collinearity
  • Pearson and Spearman Correlation Methods
  • Understanding Pearson and Spearman correlation
  • Spearman Formula
  • Pearson Formula
  • Regression Error Metrics
  • Understanding Regression Error Metrics
  • Mean Squared Error
  • Mean Absolute Error
  • Root Mean Squared Error
  • R-Squared or Coefficient of Determination
  • Adjusted R-Squared
  • Summary on Regression Error Metrics
  • Conditional Probability
  • Bayes Theorem
  • Binomial Distribution
  • Poisson Distribution
  • Normal Distribution
  • Skewness and Kurtisos
  • T – Distribution
  • Decision Tree of Probability
  • Linear Algebra – Matrices
  • Indices and Logarithms
  • Introduction to Matrix
  • Addition and Subtraction – Matrices
  • Multiplication – Matrice
  • Square of Matrix
  • Transpose of Matrix
  • Special Matrix
  • Determinant of Matrix
  • Determinant of Singular Matrix – Example
  • Cofactor
  • Minor
  • Place Sign
  • Adjoint of a Square Matrix
  • Inverse of Matrix
  • The inverse of Matrix – Example
  • Matrix for Simultaneous Equation – Exercise & Solution 10
  • Cramer’s Rule
  • Cramer’s Rule Example
  • Eigenvalues and Eigenvectors
  • Euclidean Distance and Manhattan Distance
  • Differentiation
  • Importance of Calculus for Machine Learning
  • The gradient of a Straight Line
  • The gradient of a Curve to Understanding Differentiation
  • Derivatives By First Principle
  • Derived Definition Form of First Principle
  • General Formula
  • Second Derivatives
  • Understanding Second Derivatives
  • Special Derivatives
  • Understanding Special Derivatives
  • Differentiation Using Chain Rule
  • Understanding Chain Rule
  • Differentiation Using Product Rule
  • Understanding Product Rule
  • Differentiation Using Chain and Product Rules
  • Calculus – Indefinite Integrals I
  • Calculus – Indefinite Integrals II
  • Calculus – Definite Integrals I
  • Calculus – Definite Integrals II
  • Calculus – Area Under Curve – Using Integration

You will also have access to the Q&A section where you contact post questions. You can also send me a direct message.

Upon the completion of this course, you’ll receive a certificate of completion which you can post on your LinkedIn account for our colleagues and potential employers to view! All these come with a 30-day money-back guarantee. so you can try out the course risk-free!

Who is this course for:

  • Those starting from scratch in Machine  Learning
  • Those who wish to take their career to the next level
  • Professional in the field of Data Science
  • Professionals in the banking industry
  • Professionals in the insurance industry

Master the core Mathematics, Probability & Statistics for Business Analytics, Data Science, AI, Machine & Deep Learning!

Who this course is for:

  • Students and professionals
  • Those who need to understand how to apply probability to solve problems

Created by Donatus Obomighie, PhD, MSc, PMP
Last updated 7/2023
English [Auto]

Size: 23.40 GB

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