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 nonmutually exclusive laws of probability

You will learn dependent and independent events of probaility

A lot more…
Requirements

Basic maths
Description
Start learning Mathematics, Probability & Statistics for Machine Learning TODAY!
Hi,
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 daytoday 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 Nonmutual Exclusive
 Multiplication Rules of Probability
 Dependent and Independent Events
 Random Variable
 Discrete and Continuous Variable
 ZScore
 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
 RSquared or Coefficient of Determination
 Adjusted RSquared
 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 30day moneyback guarantee. so you can try out the course riskfree!
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
English [Auto]
Size: 23.40 GB
Google Drive Links
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Torrent Links
https://www.udemy.com/course/probabilitystatisticsmathematics/.