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Statistics You Need to Know for Machine Learning · LearnSpace
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Statistics You Need to Know for Machine Learning

Курс от SAS
Средний≈ 51.5 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

When it comes to using data, there are two main camps, traditional statistics and machine learning, and the two camps complement each other. Statistics remains highly relevant, irrespective of the size of data. Its role remains what it has always been, but it is even more important now. There is a need to transition from traditional statistical modeling to the machine learning world. This course introduces the statistical background necessary for machine learning. Knowledge of statistics relevant to machine learning will prepare you to become a data scientist. The course prepares you for future instruction on machine learning (including its underlying methodology that has statistical foundations) and enables you to develop a deeper understanding of machine learning models. This course is aimed at anyone in the field of data science who does not yet have a deep understanding of statistical and machine learning concepts or wants to enhance their knowledge, which might include business analysts, data analysts, marketing analysts, marketing managers, data scientists, data engineers, financial analysts, data miners, statisticians, mathematicians, and others who work in allied areas.

Навыки, которые вы освоите

Logistic RegressionModel EvaluationCorrelation AnalysisApplied Machine LearningExploratory Data AnalysisData Modeling

Программа курса

5 модулей · 287 учебных материалов

01Statistics and Machine Learning36 материалов

Course Overview

Welcome to the CourseВидеоData DictionaryЧтениеUsing SAS Viya for Learners with This Course (Required)ЧтениеAccess SAS Viya for LearnersВнешний инструмент

Relevance of Statistics in Big Data and Machine Learning

Учитесь у экспертов

Catherine Truxillo

Director, Analytical Education

Statistics You Need to Know for Machine Learning
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Обучение откроется на Coursera
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Обучение на Coursera

≈ 51.5 ч

5 модулей

Язык: Английский

Субтитры: Арабский, Французский, Итальянский, Бразильский португальский, Корейский, Немецкий, Пушту, Испанский, Дари, Японский, Венгерский

Часть программы вашего университета
OverviewВидео
Data and Digital EconomiesВидео
What Is Big Data?Чтение
Question: Big DataЗадание
Big Data Produced Smart ApplicationsВидео
Question: Computer VisionЗадание
Knowing Statistics (1)Видео
Demo: Automated Machine Learning in SAS StudioЧтение
Knowing Statistics (2)Видео
Relevance of Statistics in Big DataВидео
More about the Relevance of Statistics in Big DataЧтение
Transitioning to the Machine Learning WorldВидео
Analyzing DataВидео
Statistical Modeling: The Two CulturesВидео
Question: Modeling CulturesЗадание
Modeling ApproachesВидео
Why Is Statistics Important to Machine Learning?Видео
Journey from Statistics to Machine LearningВидео
More about the Journey from Statistics to Machine LearningЧтение

Terminology and Vocabulary

Basic TerminologyВидеоQuestion: Basic Terminology (1)ЗаданиеQuestion: Basic Terminology (2)ЗаданиеVariable Type and Level of MeasurementВидеоQuestion: Measurement ScalesЗаданиеModeling VocabularyВидеоQuestion: Modeling VocabularyЗадание

Introduction to SAS Viya and SAS Studio

Introduction to SAS ViyaВидеоSAS Viya ServersВидеоMore about SAS ViyaЧтениеIntroduction to SAS Studio and SAS Studio FlowsВидеоQuestion: SAS ViyaЗадание

Review

Lesson SummaryЧтение
02Fundamental Statistical Concepts62 материалов

Introduction to Statistical Analysis

OverviewВидеоPopulations and SamplesВидеоProcess of Statistical AnalysisВидеоQuestion: SamplesЗаданиеSamplingВидеоSampling MethodsВидеоMore about SamplingЧтениеQuestion: Sampling MethodsЗаданиеEvent-Based SamplingВидеоOversampling and Undersampling: Advantages and DisadvantagesЧтениеQuestion: Event Based SamplingЗаданиеAnalysis DataВидеоAnalysis GoalsВидеоDemo: Listing ObservationsЧтение

Descriptive Statistics

Describing Your DataВидеоMeasures of Central TendencyВидеоMeasures of PositionВидеоQuestion: Central TendencyЗаданиеMeasures of DispersionВидеоQuestion: DispersionЗаданиеQuestion: Summary Statistics

Inferential Statistics

Making Inferences from DataВидеоPoint EstimatesВидеоStandard ErrorВидеоQuestion: Variability in the Sample StatisticЗаданиеSampling DistributionВидеоQuestion: Sampling DistributionЗаданиеConfidence Intervals

Review

Lesson SummaryЧтение
03Explanatory Modeling Using Linear Regression75 материалов

Correlation and Simple Linear Regression

OverviewВидеоExplanatory ModelingВидеоExplore Your Data before Regression ModelingВидеоCorrelation CoefficientВидеоQuestion: Scatter PlotsЗаданиеCorrelation Differs from CovarianceВидеоPearson Correlation Is Inappropriate for Some DataВидеоUsing Correlation for Variable ScreeningВидеоRelevant versus Irrelevant PredictorsВидеоRedundant versus Non-redundant PredictorsВидеоExamples of Irrelevancy and RedundancyВидеоCorrelation Does Not Imply CausationВидеоQuestion: Pearson CorrelationЗаданиеCorrelation versus RegressionВидеоHistory of the Term RegressionЧтениеSimple Linear RegressionВидеоLeast Squares RegressionВидеоOLS Regression Parameter EstimatesЧтениеLinear Regression Hypothesis TestsВидеоQuestion: Simple Linear RegressionЗаданиеExplained versus Unexplained VariabilityВидеоCoefficient of DeterminationВидеоMore about Least Square RegressionЧтениеConfidence and Prediction IntervalsВидеоQuestion: Relationships between VariablesЗаданиеCorrelations and Simple Linear RegressionВидеоDemo: Correlation and Linear RegressionЧтение

Multiple Regression and Model Selection

Multiple RegressionВидеоMultiple Regression Hypothesis TestВидеоCategorical Predictors in RegressionВидеоDummy Coding of Categorical InputsВидеоMultiple Regression with Categorical PredictorsВидеоRegression and ANOVAВидео

Model Diagnostics

Model DiagnosticsВидеоAssumptions of Linear RegressionВидеоVerifying Assumptions with Residual PlotsВидеоNonrandom Patterns Indicate ProblemsВидеоCheck Normality with Other PlotsВидеоQuestion: Regression AssumptionsЗадание

Review

Lesson SummaryЧтение
04Predictive Modeling Using Logistic Regression36 материалов

Introduction to Predictive Modeling

OverviewВидеоTo Explain or to Predict?ВидеоQuestion: Explanation versus PredictionЗаданиеPredictive ModelingВидеоAnalytical Methods for Predictive ModelingЧтениеQuestion: Score Code in SAS ViyaЗаданиеTimeframes for ModelingЧтениеHonest AssessmentВидеоQuestion: Honest AssessmentЗаданиеCandidate ModelsВидеоQuestion: Model DiversityЗаданиеOptimizing Model ComplexityВидеоDecomposing Prediction ErrorЧтениеQuestion: Predictive ModelsЗадание

Categorical Associations

Question: Categorical VariablesЗаданиеAssociations between Categorical VariablesВидеоQuestion: AssociationЗаданиеCramer's V StatisticЧтениеOdds RatioВидеоQuestion: Strength of an AssociationЗаданиеDemo: Examining Categorical Association

Logistic Regression Model

Question: Regression ModelsЗаданиеLogistic RegressionВидеоQuestion: Bounds for a LogitЗаданиеQuestion: Logistic Regression ModelЗаданиеInterpreting the Odds RatioВидеоQuestion: Odds RatioЗаданиеAssessing the Model Fit

Model Deployment

Model DeploymentВидеоDemo: Scoring a Logistic Regression ModelЧтениеQuestion: Allocation RuleЗадание

Review

Lesson SummaryЧтение
05Statistical Foundations of Machine Learning78 материалов

Overview of Machine Learning

OverviewВидеоMachine LearningВидеоSupervised LearningВидеоUnsupervised LearningВидеоSemi-supervised LearningВидеоReinforcement LearningВидеоMore about Reinforcement LearningЧтениеHow Does Machine Learning Work?ВидеоQuestion: Machine Learning MethodsЗаданиеNeural NetworksВидеоQuestion: Neural NetworksЗаданиеMore Information: Common AlgorithmsЧтениеData Preparation for Machine LearningВидеоData Preprocessing for Machine LearningВидеоQuestion: Data Preparation and Data PreprocessingЗадание

Data Pre-processing for Machine Learning Models

Data Difficulties and Modeling IssuesВидеоData VisualizationВидеоBig Data Visualization ChallengesВидеоVisualization ExamplesВидеоMore about Visualization ExamplesЧтениеQuestion: Data Visualization ChallengesЗадание

Model Evaluation, Estimation, and Post-training Tasks

Modeling Challenges with Machine Learning DataВидеоQuestion: Signal and NoiseЗаданиеMore about Modeling Challenges with Machine Learning DataВидеоCross Validation ExampleВидеоBootstrap AggregationВидеоQuestion: Cross ValidationЗадание

Review

Lesson SummaryЧтение
Задание
Visualizing DistributionsВидео
HistogramsВидео
Normal (Gaussian) DistributionВидео
Usefulness of Normal Distribution in Machine LearningВидео
Question: Normal (Gaussian) DistributionЗадание
Plots beyond HistogramsВидео
Why “1.5” in IQR Method of Outlier Detection?Чтение
Question: Visualizing DistributionsЗадание
Measures of Shape: SkewnessВидео
Question: SkewnessЗадание
Measures of Shape: KurtosisВидео
Platykurtic and Leptokurtic DistributionsЧтение
Question: Normal DistributionЗадание
Usefulness of Distribution Analysis in Machine LearningВидео
Demo: Exploring DataЧтение
Видео
Confidence Interval for MeanЧтение
Question: Interval EstimatesЗадание
Statistical Hypothesis TestВидео
Performing a Hypothesis TestВидео
Statistical Hypothesis Test: Coin ExampleВидео
Statistical Hypothesis Test: Types of ErrorsВидео
Statistical Hypothesis Test: Effect Size InfluenceВидео
Statistical Hypothesis Test: Sample Size InfluenceВидео
p-Values and Statistical SignificanceВидео
Question: Hypothesis Testing TerminologyЗадание
Question: Type I and Type II ErrorsЗадание
Hypothesis Tests for MeansВидео
One-Sample t Test ScenarioВидео
Performing a t TestВидео
Question: t DistributionЗадание
p-Values in Machine LearningВидео
Demo: Testing a Hypothesis Using One-Sample t TestЧтение
Question: Confidence IntervalЗадание
Interaction EffectsВидео
Question: InteractionЗадание
InteractionsЧтение
Many Possible Models!Видео
Comparing Regression ModelsВидео
Adjusted R SquareВидео
Information CriteriaВидео
Akaike's Information Criterion (AIC)Видео
Most Used Information CriteriaЧтение
Question: AICЗадание
Common Regression Model Selection MethodsВидео
Select Variables/Choose ModelЧтение
Sequential Selection: ForwardВидео
Sequential Selection: BackwardВидео
Sequential Selection: StepwiseВидео
Question: Sequential Selection MethodsЗадание
Problems with Stepwise Selection MethodsЧтение
Shrinkage MethodsЧтение
Multiple Regression and Model SelectionВидео
Demo: Multiple Regression and Model SelectionЧтение
Candidate ModelsЧтение
Which Model to Use?Чтение
Potential Problems: CollinearityВидео
Illustration of CollinearityВидео
How to Detect CollinearityВидео
Variance Inflation Factor (VIF)Видео
Removing CollinearityЧтение
Question: CollinearityЗадание
Potential Problems: Extreme ValuesВидео
Outliers, High Leverage Points, and Influential ObservationsВидео
Influence DiagnosticsВидео
Detecting Outliers and Influential ObservationsВидео
Assessing the ModelВидео
Demo: Model DiagnosticsЧтение
What Did We Discover about the Model?Видео
Чтение
Видео
Question: Comparing Pairs for Model AssessmentЗадание
Multiple Logistic Regression ModelВидео
Demo: Fitting a Multiple Logistic Regression ModelЧтение
Question: Default and IncomeЗадание
Dirty Data: Errors, Missing Values, and OutliersВидео
Question: ErrorsЗадание
ErrorsВидео
Demo: Modifying and Correcting DataЧтение
Question: Missing ValuesЗадание
Missing DataВидео
Missing Data ProblemsВидео
Missing Data CausesЧтение
Question: Missing Value RepresentationЗадание
Analysis Strategies for Missing DataВидео
Question: Mean or Median ImputationЗадание
Question: Missing Stock Exchange PriceЗадание
Cluster ImputationВидео
Tailored-Value ImputationВидео
Question: Imputation MethodsЗадание
Excessive MissingnessВидео
Missing Value IndicatorsВидео
Demo: Managing Missing ValuesЧтение
Question: Missing Value ImputationЗадание
Question: OutliersЗадание
What is an Outlier?Видео
Outliers and Machine Learning ModelsВидео
Dealing with OutliersВидео
What Do Transformations Do?Видео
Simple TransformationsВидео
Question: Variable TransformationsЗадание
Demo: Transforming InputsЧтение
Problems With Too Many VariablesВидео
Types of Feature EngineeringВидео
Question: Feature EngineeringЗадание
Demo: Performing Feature SelectionЧтение
Distinctly Scaled VariablesВидео
Feature ScalingВидео
Think About It: Feature Scaling MethodЧтение
Question: Feature ScalingЗадание
Model FittingВидео
Question: Model ComplexityЗадание
Effect of Magnitude of CoefficientsВидео
Shrinking the CoefficientsВидео
L1 versus L2Чтение
Question: Regularization MethodsЗадание
Learning ProcessВидео
Parameters versus HyperparametersЧтение
More about Estimation CriterionЧтение
Question: Learning TerminologyЗадание
Question: Finding Parameter ValuesЗадание
Demo: Running a Neural Network Model and Tuning Its HyperparametersЧтение
Model InterpretabilityВидео
Why Interpretability MattersЧтение
Question: Interpreting Black-Box ModelsЗадание