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WELCOME 2
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AI OVERVIEW 3
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History of AI & Machine LearningLecture2.1
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Type of Machine LearningLecture2.2
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Supervised LearningLecture2.3
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INTRODUCTION 2
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What is Multiple Regression?Lecture3.1
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Real World ApplicationLecture3.2
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MODEL CREATION 11
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Data : Data StatingLecture4.1
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Data : Data RequirementLecture4.2
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Model : AssumptionLecture4.3
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Model : How to create model : LogicLecture4.4
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Model : How to create model : Cost FunctionLecture4.5
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Model : How to create model : Finding SolutionLecture4.6
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Model : How to create model : ExerciseLecture4.7
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Model : How to create model : CodeLecture4.8
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Model : Further ReadingLecture4.9
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PredictionLecture4.10
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We are Data ScientistLecture4.11
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BASIC WORKSHOP 26
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Code Pipeline : Workflow & Import LibrariesLecture5.1
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Code Pipeline : Read Data & Clean DataLecture5.2
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Code Pipeline : Split Dataset into Training & Test SetsLecture5.3
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Code Pipeline : Data PreparationLecture5.4
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Code Pipeline : Model Creation – Model ImprovementLecture5.5
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Code Pipeline : Model DeploymentLecture5.6
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Project1 : AI in Marketing : IntroductionLecture5.7
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Project1 : AI in Marketing : Code : Import LibrariesLecture5.8
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Project1 : AI in Marketing : Code : Read Data – Train/TestLecture5.9
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Project1 : AI in Marketing : Code : Data Preparation & Model CreationLecture5.10
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Project1 : AI in Marketing : Code : Prediction – Model EvaluationLecture5.11
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Project1 : AI in Marketing : Code : Model Deployment & ConclusionLecture5.12
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Project2 : AI in Investment : IntroductionLecture5.13
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Project2 : AI in Investment : Code : Import Libraries – Clean DataLecture5.14
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Project2 : AI in Investment : Code : Train/Test – Model CreationLecture5.15
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Project2 : AI in Investment : Code : Prediction – Model DeploymentLecture5.16
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Project2 : AI in Investment : Code : BTCUSD & EURUSD & XAUUSDLecture5.17
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Project3 : Smart Farm : IntroductionLecture5.18
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Project3 : Smart Farm : Code : Ordinal EncodingLecture5.19
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Project3 : Smart Farm : Code : One Hot EncodingLecture5.20
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Project3 : Smart Farm : Code : Model DeploymentLecture5.21
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Project4 : AI in Business : IntroductionLecture5.22
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Project4 : AI in Business : CodeLecture5.23
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Project5 : AI in Insurance : IntroductionLecture5.24
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Project5 : AI in Insurance : CodeLecture5.25
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ConclusionLecture5.26
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MODEL IMPROVEMENT 29
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IntroductionLecture6.1
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Assumption : IntroductionLecture6.2
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Assumption1 – Linear RelationshipLecture6.3
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Assumption2 – Normality of ResidualsLecture6.4
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Assumption3 – HomoscedasticityLecture6.5
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Assumption : Check Assumption : AI in MarketingLecture6.6
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Assumption : Check Assumption : AI in InvestmentLecture6.7
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Assumption : Check Assumption : Smart FarmLecture6.8
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Assumption : Check Assumption : AI in Business & AI in InsuranceLecture6.9
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Assumption : Dealing with Nonlinear RelationshipLecture6.10
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Assumption4 – No Missing FeaturesLecture6.11
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Assumption5 – No MulticollinearityLecture6.12
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Assumption : Effect of MulticollinearityLecture6.13
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Problem with Linearly DependentLecture6.14
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Problem with Moore-PenroseLecture6.15
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Solution : Lagrange MultipliersLecture6.16
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Solution : new Cost FunctionLecture6.17
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Solution : Found Ridge RegressionLecture6.18
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Solution : ConclusionLecture6.19
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Regularization : IntroductionLecture6.20
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Regularization : What is Regularization?Lecture6.21
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Ridge Regression : What is? & Geometric View : Lin DepLecture6.22
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Ridge Regression : Geometric View : No MulLecture6.23
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Ridge Regression : Properties – CodeLecture6.24
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Lasso Regression : What is? & Geometric ViewLecture6.25
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Lasso Regression : Properties : Feature SelectionLecture6.26
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Lasso Regression : Model Creation – CodeLecture6.27
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Elastic NetLecture6.28
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Regularization : ConclusionLecture6.29
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ADVANCE WORKSHOP 6
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IntroductionLecture7.1
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Code PipelineLecture7.2
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Project6 : Car Price : IntroductionLecture7.3
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Project6 : Car Price : Normal EquationLecture7.4
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Project6 : Car Price : RegularizationLecture7.5
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GoodbyeLecture7.6
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Supplementary : DATA PREPARATION 9
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NaNLecture8.1
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OutliersLecture8.2
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Feature Encoding : Type of DataLecture8.3
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Feature Encoding : Ordinal EncodingLecture8.4
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Feature Encoding : One Hot EncodingLecture8.5
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Feature Scaling : What & Why?Lecture8.6
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Feature Scaling : StandardizationLecture8.7
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Feature Scaling : Min-Max ScalingLecture8.8
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Feature Scaling : ConclusionLecture8.9
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Supplementary : MODEL EVALUATION 6
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What & Why?Lecture9.1
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R-squaredLecture9.2
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Mean Squared ErrorLecture9.3
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Mean Absolute ErrorLecture9.4
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Mean Absolute Percentage ErrorLecture9.5
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ConclusionLecture9.6
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Supplementary : BASIC MATH for DEEP LEARNING 6
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Linear Algebra : Matrix & Basic Matrix OperationLecture10.1
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Linear Algebra : Matrix Multiplication & Hadamard ProductLecture10.2
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Linear Algebra : Matrix Multiplication – InverseLecture10.3
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Calculus : Basic DerivativeLecture10.4
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Calculus : Chain Rule & DifferentiationLecture10.5
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Probability & StatisticsLecture10.6
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Supplementary : CROSS VALIDATION 4
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What & Step to CalculateLecture11.1
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Advantage of Cross ValidationLecture11.2
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ExtensionLecture11.3
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ConclusionLecture11.4
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