Complete Math, Probability & Statistics For Machine Learning

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

What you'll learn

Learn Linear Algebra for Machine and Deep Learning

Learn Calculus for Machine and Deep Learning

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…

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 day-to-day business and scientific prediction problems and analytical problems. This course includes but is not limited to:"SetsUniversal SetProper and Improper SubsetSuper Set and Singleton SetNull or Empty SetPower SetEqual and Equivalent SetSet Builder NotationsCardinality of SetSet OperationsLaws of SetsFinite and Infinite SetNumber SetsVenn DiagramUnion, Intersection, and Complement of SetFactorialPermutationsCombinationsTheoretical ProbabilityEmpirical ProbabilityAddition Rules of ProbabilityMutual and Non-mutual ExclusiveMultiplication Rules of ProbabilityDependent and Independent EventsRandom VariableDiscrete and Continuous VariableZ-ScoreFrequency and TallyPopulation and SampleRaw Data and ArrayMeanIntroductionWeighted MeanProperties of MeanBasic Properties of MeanMean Frequency DistributionMedianMedian Frequency DistributionModeMeasurement of SpreadMeasures of Spread (Variation / Dispersion)RangeMean DeviationMean Deviation for Frequency DistributionVariance & Standard DeviationUnderstanding Variance and Standard DeviationBasic Properties of Variance and Standard DeviationVariable | Dependent- Independent - Moderating - Ordinal…VariableTypes of VariableDependent, Independent, Control Moderating and Mediating VariablesCorrelationRegression & CollinearityCollinearityPearson and Spearman Correlation MethodsUnderstanding Pearson and Spearman correlationSpearman FormulaPearson FormulaRegression Error MetricsUnderstanding Regression Error MetricsMean Squared ErrorMean Absolute ErrorRoot Mean Squared ErrorR-Squared or Coefficient of DeterminationAdjusted R-SquaredSummary on Regression Error MetricsConditional ProbabilityBayes TheoremBinomial DistributionPoisson DistributionNormal DistributionSkewness and KurtisosT - DistributionDecision Tree of ProbabilityLinear Algebra - MatricesIndices and LogarithmsIntroduction to MatrixAddition and Subtraction - MatricesMultiplication - MatriceSquare of MatrixTranspose of MatrixSpecial MatrixDeterminant of MatrixDeterminant of Singular Matrix - ExampleCofactorMinorPlace SignAdjoint of a Square MatrixInverse of MatrixThe inverse of Matrix - ExampleMatrix for Simultaneous Equation - Exercise & Solution 10Cramer's RuleCramer's Rule ExampleEigenvalues and EigenvectorsEuclidean Distance and Manhattan DistanceDifferentiationImportance of Calculus for Machine LearningThe gradient of a Straight LineThe gradient of a Curve to Understanding DifferentiationDerivatives By First PrincipleDerived Definition Form of First PrincipleGeneral FormulaSecond DerivativesUnderstanding Second DerivativesSpecial DerivativesUnderstanding Special DerivativesDifferentiation Using Chain RuleUnderstanding Chain RuleDifferentiation Using Product RuleUnderstanding Product RuleDifferentiation Using Chain and Product RulesCalculus - Indefinite Integrals ICalculus - Indefinite Integrals IICalculus - Definite Integrals ICalculus - Definite Integrals IICalculus - Area Under Curve - Using IntegrationYou 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  LearningThose who wish to take their career to the next levelProfessional in the field of Data ScienceProfessionals in the banking industryProfessionals in the insurance industry

Overview

Section 1: Set Theory

Lecture 1 Importance of Set Theory for Machine Learning

Lecture 2 What is Set?

Lecture 3 Universal vs Subset

Lecture 4 Proper vs Improper Subset

Lecture 5 Singleton Set

Lecture 6 Null or Empty Set

Lecture 7 Power Set

Lecture 8 Equal vs Equivalent Sets

Lecture 9 Set Builder Notation

Lecture 10 Cardinality of Set

Lecture 11 Set Operations: Union, Intersection, Complement, Disjoint and Non-Disjoint Sets

Lecture 12 7 Laws of Set: Idempotent, Associate, Communicative, Distributive, De-Morgan…

Lecture 13 Set: Exercise & Solution 1

Lecture 14 Set: Exercise & Solution 2

Lecture 15 Set: Exercise & Solution 3

Lecture 16 Finite vs Infinite Set

Lecture 17 Range and Domain Sets

Lecture 18 Number Sets: Prime, Odd, Even, Multiple, Odd-Prime, Divisibility

Lecture 19 General Number Sets

Lecture 20 Set: Exercise & Solution 4

Lecture 21 Set: Exercise & Solution 5

Lecture 22 Set: Exercise & Solution 6

Lecture 23 Set: Exercise & Solution 7

Lecture 24 Set: Exercise & Solution 8

Lecture 25 Set: Exercise & Solution 9

Lecture 26 Set: Exercise & Solution 10

Lecture 27 Venn Diagram

Lecture 28 Set: Exercise & Solution 11

Lecture 29 Set: Exercise & Solution 12

Lecture 30 Set: Exercise & Solution 13

Lecture 31 Set: Exercise & Solution 14

Lecture 32 Set: Exercise & Solution 15

Section 2: Combinatorics: Factorial, Permutation & Combination

Lecture 33 Importance of Combinatorics for Machine Learning

Lecture 34 What is Factorial?

Lecture 35 Factorial - Exercise & Solution

Lecture 36 Permutation

Lecture 37 Combination

Lecture 38 Exercise & Solution

Lecture 39 Exercise & Solution

Lecture 40 Exercise & Solution

Lecture 41 Exercise & Solution

Lecture 42 Exercise & Solution

Lecture 43 Exercise & Solution

Lecture 44 Exercise & Solution

Lecture 45 More: Factorial vs Permutation

Lecture 46 Exercise & Solution

Lecture 47 Exercise & Solution

Lecture 48 Exercise & Solution

Section 3: Probability - Basics

Lecture 49 Importance of Probability for Machine Learning

Lecture 50 What is Probability

Lecture 51 Basic Terms in Probability

Lecture 52 General Formula

Lecture 53 Exercise & Solution 1

Lecture 54 Exercise & Solution 2

Lecture 55 Exercise & Solution 3

Lecture 56 Exercise & Solution 4

Lecture 57 Exercise & Solution 5

Lecture 58 Exercise & Solution 6

Lecture 59 Exercise & Solution 7

Lecture 60 Exercise & Solution 8

Lecture 61 Exercise & Solution 9

Lecture 62 Exercise & Solution 10

Section 4: Theoretical Probability

Lecture 63 What is theoretical probability?

Lecture 64 Exercise & Solution 1

Lecture 65 Exercise & Solution 2

Lecture 66 Exercise & Solution 3

Lecture 67 Exercise & Solution 4

Lecture 68 Exercise & Solution 5

Lecture 69 Exercise & Solution 6

Lecture 70 Exercise & Solution 7

Section 5: Empirical (Experimental) Probability

Lecture 71 What is experimental Probability?

Lecture 72 Exercise & Solution 1

Lecture 73 Exercise & Solution 2

Lecture 74 Exercise & Solution 3

Lecture 75 Exercise & Solution 4

Lecture 76 Exercise & Solution 5

Section 6: Probability Addition Rules

Lecture 77 Mutually Exclusive and Non-Mutually Exclusive Events

Lecture 78 Exercise & Solution 1

Lecture 79 Exercise & Solution 2

Lecture 80 Exercise & Solution 3

Lecture 81 Exercise & Solution 4

Lecture 82 Exercise & Solution 5

Lecture 83 Exercise & Solution 6

Lecture 84 Exercise & Solution 7

Lecture 85 Exercise & Solution 8

Section 7: Probability Multiplication Rule

Lecture 86 Dependent and Independent Events

Lecture 87 Exercise & Solution 1

Lecture 88 Exercise & Solution 2

Lecture 89 Exercise & Solution 3

Lecture 90 Exercise & Solution 4

Lecture 91 Exercise & Solution 5

Lecture 92 Exercise & Solution 6

Section 8: Probability - Tons of Exercises & Solutions

Lecture 93 Exercise & Solution 1

Lecture 94 Exercise & Solution 2

Lecture 95 Exercise & Solution 3

Lecture 96 Exercise & Solution 4

Lecture 97 Exercise & Solution 5

Lecture 98 Exercise & Solution 6

Lecture 99 Exercise & Solution 7

Lecture 100 Exercise & Solution 8

Lecture 101 Exercise & Solution 9

Lecture 102 Exercise & Solution 10

Lecture 103 Exercise & Solution 11

Lecture 104 Exercise & Solution 12

Lecture 105 Exercise & Solution 13

Lecture 106 Exercise & Solution 14

Lecture 107 Exercise & Solution 15

Lecture 108 Exercise & Solution 16

Lecture 109 Exercise & Solution 17

Lecture 110 Exercise & Solution 18

Lecture 111 Exercise & Solution 19

Lecture 112 Exercise & Solution 20

Section 9: Binomial Distribution, Poisson Distribution, Normal Distribution, T-Distribution

Lecture 113 Random Variable

Lecture 114 Binomial Probability Distribution

Lecture 115 Exercise & Solution 1

Lecture 116 Exercise & Solution 2

Lecture 117 Exercise & Solution 3

Lecture 118 Exercise & Solution 4

Lecture 119 Exercise & Solution 5

Lecture 120 Poisson Distribution

Lecture 121 Exercise & Solution 6

Lecture 122 Normal Distribution

Lecture 123 Z - Score

Lecture 124 Exercise & Solution 7

Lecture 125 Exercise & Solution 7

Lecture 126 Skewness

Lecture 127 Kurtosis

Lecture 128 T - Distribution

Section 10: Conditional Probability

Lecture 129 Conditional Probability

Section 11: Theorem of Total Probability

Lecture 130 Theorem of Total Probability

Lecture 131 Theorem of Total Probability - Exceptional Case

Section 12: Bayes' Theorem

Lecture 132 Bayes' Theorem

Lecture 133 Bayes' Theorem - Exceptional Case

Section 13: Bayes' Theorem, Total Probability and Depend Events - Decision Tree

Lecture 134 Decision Tree of Probability

Lecture 135 Decision Tree on Dependent Events - Exercise and Solution

Lecture 136 Decision Tree on Dependent Events - Exercise and Solution

Lecture 137 Decision Tree on Total Probability - Exercise and Solution

Lecture 138 Exercise and Solution

Lecture 139 Bayes' Theorem - Exercise and Solution

Lecture 140 Total Probability - Exercise and Solution

Lecture 141 Total Probability - Exercise and Solution

Section 14: Importance of Probability

Lecture 142 In Brief - Importance of Probability

Section 15: Statistics

Lecture 143 Importance of Statistics for Machine Learning

Lecture 144 Introduction

Lecture 145 Frequency and Tally

Lecture 146 Population and Sample

Lecture 147 Raw Data and Array

Section 16: Statistics - Mean

Lecture 148 Introduction

Lecture 149 Question and Answer 1

Lecture 150 Question and Answer 2

Lecture 151 Question and Answer 3

Lecture 152 Question and Answer 4

Lecture 153 Question and Answer 5

Lecture 154 Question and Answer 6

Lecture 155 Question and Answer 7

Lecture 156 Question and Answer 8

Section 17: Statistics - Weighted Mean

Lecture 157 Weighted Mean

Lecture 158 Question and Answer 1

Lecture 159 Question and Answer 2

Section 18: Statistics - Properties of Mean

Lecture 160 Basic Properties of Mean

Lecture 161 Question and Answer 1

Lecture 162 Question and Answer 2

Lecture 163 Question and Answer 3

Lecture 164 Question and Answer 4

Lecture 165 Question and Answer 5

Lecture 166 Question and Answer 6

Section 19: Statistics - Mean Frequency Distribution

Lecture 167 Mean Frequency Distribution

Lecture 168 Question and Answer 1

Lecture 169 Question and Answer 2

Lecture 170 Question and Answer 3

Section 20: Statistics - Median

Lecture 171 Median

Lecture 172 Question and Answer

Section 21: Statistics - Median Frequency Distribution

Lecture 173 Median Frequency Distribution

Lecture 174 Question and Answer 1

Lecture 175 Question and Answer 2

Lecture 176 Question and Answer 3

Section 22: Statistics - Mode

Lecture 177 Mode

Lecture 178 Question and Answer 1

Lecture 179 Question and Answer 2

Section 23: Statistics - Mode Frequency Distribution

Lecture 180 Question and Answer 1

Lecture 181 Question and Answer 2

Section 24: Statistics - Measurement of Spread

Lecture 182 Measures of Spread (Variation / Dispersion)

Section 25: Statistics - Range

Lecture 183 What is Range

Lecture 184 Question and Answer 1

Lecture 185 Question and Answer 2

Lecture 186 Question and Answer 3

Section 26: Statistics - Mean Deviation

Lecture 187 Understanding Mean Deviation

Lecture 188 Question and Answer 1

Lecture 189 Question and Answer 2

Lecture 190 Mean Deviation for Frequency Distribution

Section 27: Statistics - Variance & Standard Deviation

Lecture 191 Understanding Variance and Standard Deviation

Lecture 192 Basic Properties of Variance and Standard Deviation

Lecture 193 Question and Answer 1

Lecture 194 Question and Answer 2

Lecture 195 Question and Answer 3

Lecture 196 Question and Answer 4

Lecture 197 Question and Answer 5

Lecture 198 Question and Answer 6

Lecture 199 Question and Answer 7

Lecture 200 Question and Answer 8

Lecture 201 Question and Answer 9

Lecture 202 Question and Answer 10

Section 28: Statistics - Variable | Dependent- Independent - Moderating - Ordinal…

Lecture 203 Variable

Lecture 204 Types of Variable

Lecture 205 Types of Variable Explained

Lecture 206 Dependent, Independent, Control Moderating and Mediating Variables

Section 29: Statistics - Correlation

Lecture 207 Correlation

Section 30: Statistics - Regression & Collinearity

Lecture 208 Regression - Linear, Multiple Regression…

Lecture 209 Collinearity

Section 31: Statistics - Pearson and Spearman Correlation Methods

Lecture 210 Understanding Pearson and Spearman correlation

Lecture 211 Spearman Formula

Lecture 212 Pearson Formula

Lecture 213 Spearman Example

Lecture 214 Pearson Example

Section 32: Statistics - Regression Error Metrics

Lecture 215 Understanding Regression Error Metrics

Lecture 216 Mean Squared Error

Lecture 217 Mean Absolute Error

Lecture 218 Root Mean Squared Error

Lecture 219 R-Squared or Coefficient of Determination

Lecture 220 Adjusted R-Squared

Lecture 221 Summary on Regression Error Metrics

Section 33: Indices & Logarithms

Lecture 222 Importance of Logarithms for Machine Learning

Lecture 223 The laws of Indices

Lecture 224 Exercise and Solution 1

Lecture 225 Exercise and Solution 2

Lecture 226 Exercise and Solution 3

Lecture 227 Exercise and Solution 4

Lecture 228 Exercise and Solution 5

Lecture 229 Exercise and Solution 6

Lecture 230 Exercise and Solution 7

Lecture 231 Exercise and Solution 8

Lecture 232 Exercise and Solution 9

Lecture 233 Exercise and Solution 10

Lecture 234 Indices Involving Equation

Lecture 235 Exercise and Solution 11

Lecture 236 Exercise and Solution 12

Lecture 237 Exercise and Solution 13

Lecture 238 Exercise and Solution 14

Lecture 239 Exercise and Solution 15

Lecture 240 Exercise and Solution 16

Lecture 241 Introduction to Logarithm

Lecture 242 The First Law of Logarithm

Lecture 243 The Second Law of Logarithm

Lecture 244 The Third Law of Logarithm

Lecture 245 The Fourth Law of Logarithm

Lecture 246 The Fifth Law of Logarithm

Lecture 247 The Sixth Law of Logarithm

Lecture 248 The Seventh Law of Logarithm

Lecture 249 The Eight Law of Logarithm

Lecture 250 The Summary of the Laws of Logarithm

Lecture 251 Exercise and Solution 1

Lecture 252 Exercise and Solution 2

Lecture 253 Exercise and Solution 3

Lecture 254 Exercise and Solution 4

Lecture 255 Exercise and Solution 5

Lecture 256 Exercise and Solution 6

Lecture 257 Exercise and Solution 7

Lecture 258 Exercise and Solution 8

Lecture 259 Exercise and Solution 9

Lecture 260 Exercise and Solution 10

Lecture 261 Change Base In Logarithm

Lecture 262 Exercise and Solution 11

Lecture 263 Exercise and Solution 12

Lecture 264 Exercise and Solution 13

Lecture 265 Exercise and Solution 14

Lecture 266 Logarithm to Base 10 and Base e

Section 34: Linear Algebra - Matrices

Lecture 267 Importance of Matrix for Machine Learning

Lecture 268 Introduction to Matrix

Lecture 269 Addition and Subtraction - Matrices

Lecture 270 Multiplication - Matrice

Lecture 271 Square of Matrix

Lecture 272 Transpose - Matrix

Lecture 273 Special Matrix

Lecture 274 Determinant of Matrix

Lecture 275 Determinant of Singular Matrix - Example

Lecture 276 Cofactor | Minor | Place Sign

Lecture 277 Cofactor - Example

Lecture 278 Adjoint of a Square Matrix

Lecture 279 Inverse of Matrix

Lecture 280 Inverse of Matrix - Example

Lecture 281 Exercise & Solution 1

Lecture 282 Exercise & Solution 2

Lecture 283 Exercise & Solution 3

Lecture 284 Exercise & Solution 4

Lecture 285 Exercise & Solution 5

Lecture 286 Exercise & Solution 6

Lecture 287 Exercise & Solution 7

Lecture 288 Exercise & Solution 8

Lecture 289 Exercise & Solution 9

Lecture 290 Matrix for Simultaneous Equation - Exercise & Solution 10

Lecture 291 Exercise & Solution 11

Lecture 292 Cramer's Rule

Lecture 293 Cramer's Rule Example

Section 35: Eigenvalues and Eigenvectors

Lecture 294 Introduction

Lecture 295 Example 1

Lecture 296 Example 2

Lecture 297 Example 3

Lecture 298 Example 4

Section 36: Euclidean Distance and Manhattan Distance

Lecture 299 Understanding Euclidean Distance and Manhattan Distance

Section 37: Calculus - Introduction to Differentiation

Lecture 300 Importance of Calculus for Machine Learning

Lecture 301 Gradient of a Straight Line

Lecture 302 Question and Solution 1

Lecture 303 Gradient of a Curve to Understanding Differentiation

Section 38: Calculus - Derivatives By First Principle

Lecture 304 Derived Definition Form of First Principle

Lecture 305 Question and Solution 1

Lecture 306 General Formula

Lecture 307 Question and Solution 2

Lecture 308 Question and Solution 3

Lecture 309 Question and Solution 4

Lecture 310 Question and Solution 5

Lecture 311 Question and Solution 6

Lecture 312 Question and Solution 7

Lecture 313 Question and Solution 8

Section 39: Calculus - Second Derivatives

Lecture 314 Understanding Second Derivatives

Section 40: Calculus - Special Derivatives

Lecture 315 Understanding Special Derivatives

Section 41: Calculus - Differentiation Using Chain Rule

Lecture 316 Understanding Chain Rule

Lecture 317 Question and Solution 1

Lecture 318 Question and Solution 2

Lecture 319 Question and Solution 3

Lecture 320 Question and Solution 4

Section 42: Calculus - Differentiation Using Product Rule

Lecture 321 Understanding Product Rule

Lecture 322 Question and Solution 1

Lecture 323 Question and Solution 2

Lecture 324 Question and Solution 3

Lecture 325 Question and Solution 4

Lecture 326 Question and Solution 5

Lecture 327 Question and Solution 6

Lecture 328 Question and Solution 7

Section 43: Calculus - Differentiation Using Chain and Product Rules | Examples

Lecture 329 Question and Solution 1

Lecture 330 Question and Solution 2

Lecture 331 Question and Solution 3

Section 44: Calculus - Differentiation Using Quotient Rule

Lecture 332 Understanding Quotient

Lecture 333 Question and Solution 1

Lecture 334 Question and Solution 2

Section 45: Calculus - Differentiation Using Quotient and Chain Rules | Examples

Lecture 335 Question and Solution 1

Lecture 336 Question and Solution 1

Section 46: Integration - Introduction

Lecture 337 Introduction

Section 47: Integration - Indefinite Integrals

Lecture 338 Example

Lecture 339 Must Know

Lecture 340 Question and Solution 1

Lecture 341 Question and Solution 2

Lecture 342 Question and Solution 3

Lecture 343 Question and Solution 4

Lecture 344 Question and Solution 5

Section 48: Integration - Indefinite Integrals II

Lecture 345 Question and Solution 1

Lecture 346 Question and Solution 2

Lecture 347 Question and Solution 3

Section 49: Integration - Definite Integrals I

Lecture 348 Question and Solution 1

Lecture 349 Question and Solution 2

Lecture 350 Question and Solution 3

Section 50: Integration - Definite Integrals II

Lecture 351 Example

Lecture 352 General Pattern

Lecture 353 Question and Solution 1

Lecture 354 Question and Solution 2

Lecture 355 Question and Solution 3

Section 51: Area Under Curve - Using Integration

Lecture 356 Importance of Area Under Curve

Lecture 357 Understanding Area Under Curve

Lecture 358 Question and Solution 1

Lecture 359 Question and Solution 2

Lecture 360 Question and Solution 3

Section 52: BONUS SECTION

Lecture 361 Check out my other courses:

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