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:"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 industryMaster the core Mathematics, Probability & Statistics for Business Analytics, Data Science, AI, Machine & Deep Learning.