Complete Math, Statistics & Probability for Machine Learning
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 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!
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