The Data Science Course 2019: Complete Data Science Bootcamp

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The Data Science Course 2019: Complete Data Science Bootcamp

nWhat you’ll learnnnThe course provides the entire toolbox you need to become a data scientistnFill up your resume with in demand data science skills: Statistical analysis, Python programming with NumPy, pandas, matplotlib, and Seaborn, Advanced statistical analysis, Tableau, Machine Learning with stats models and scikit-learn, Deep learning with TensorFlownImpress interviewers by showing an understanding of the data science fieldnLearn how to pre-process datanUnderstand the mathematics behind Machine Learning (an absolute must which other courses don’t teach!)nStart coding in Python and learn how to use it for statistical analysisnPerform linear and logistic regressions in PythonnCarry out cluster and factor analysisnBe able to create Machine Learning algorithms in Python, using NumPy, statsmodels and scikit-learnnApply your skills to real-life business casesnUse state-of-the-art Deep Learning frameworks such as Google’s TensorFlowDevelop a business intuition while coding and solving tasks with big datanUnfold the power of deep neural networksnImprove Machine Learning algorithms by studying underfitting, overfitting, training, validation, n-fold cross validation, testing, and how hyperparameters could improve performancenWarm up your fingers as you will be eager to apply everything you have learned here to more and more real-life situationsnnGet The Data Science Course 2019: Complete Data Science Bootcamp downloadnCourse contentnExpand all 471 lectures28:52:43n-Part 1: Introductionn19:20nA Practical Example: What You Will Learn in This CoursenPreviewn05:05nWhat Does the Course CovernPreviewn03:34nDownload All Resources and Important FAQn10:41n-The Field of Data Science – The Various Data Science Disciplinesn31:11nData Science and Business Buzzwords: Why are there so many?nPreviewn05:21nData Science and Business Buzzwords: Why are there so many?n1 questionnWhat is the difference between Analysis and Analyticsn03:50nWhat is the difference between Analysis and Analyticsn1 questionnBusiness Analytics, Data Analytics, and Data Science: An IntroductionnPreviewn08:26nBusiness Analytics, Data Analytics, and Data Science: An Introductionn3 questionsnContinuing with BI, ML, and AIn09:31nContinuing with BI, ML, and AIn2 questionsnA Breakdown of our Data Science Infographicn04:03nA Breakdown of our Data Science Infographicn1 questionn-The Field of Data Science – Connecting the Data Science Disciplinesn07:19nApplying Traditional Data, Big Data, BI, Traditional Data Science and MLn07:19nApplying Traditional Data, Big Data, BI, Traditional Data Science and MLn1 questionn-The Field of Data Science – The Benefits of Each Disciplinen04:44nThe Reason behind these Disciplinesn04:44nThe Reason behind these Disciplinesn1 questionn-The Field of Data Science – Popular Data Science Techniquesn53:34nTechniques for Working with Traditional Datan08:13nTechniques for Working with Traditional Datan1 questionnReal Life Examples of Traditional Datan01:44nTechniques for Working with Big Datan04:26nTechniques for Working with Big Datan1 questionnReal Life Examples of Big Datan01:32nBusiness Intelligence (BI) Techniquesn06:45nBusiness Intelligence (BI) Techniquesn4 questionsnReal Life Examples of Business Intelligence (BI)n01:42nTechniques for Working with Traditional Methodsn09:08nTechniques for Working with Traditional Methodsn4 questionsnReal Life Examples of Traditional Methodsn02:45nMachine Learning (ML) Techniquesn06:55nMachine Learning (ML) Techniquesn2 questionsnTypes of Machine Learningn08:13nTypes of Machine Learningn2 questionsnReal Life Examples of Machine Learning (ML)n02:11nReal Life Examples of Machine Learning (ML)n5 questionsn-The Field of Data Science – Popular Data Science Toolsn05:51nNecessary Programming Languages and Software Used in Data Sciencen05:51nNecessary Programming Languages and Software Used in Data Sciencen4 questionsn-The Field of Data Science – Careers in Data Sciencen03:29nFinding the Job – What to Expect and What to Look forn03:29nFinding the Job – What to Expect and What to Look forn1 questionn-The Field of Data Science – Debunking Common Misconceptionsn04:10nDebunking Common Misconceptionsn04:10nDebunking Common Misconceptionsn1 questionn-Part 2: Probabilityn23:04nThe Basic Probability Formulan07:09nThe Basic Probability Formulan3 questionsnComputing Expected Valuesn05:29nComputing Expected Valuesn3 questionsnFrequencyn05:00nFrequencyn3 questionsnEvents and Their Complementsn05:26nEvents and Their Complementsn3 questionsn-Probability – Combinatoricsn42:56nFundamentals of Combinatoricsn01:04nFundamentals of Combinatoricsn1 questionnPermutations and How to Use Themn03:21nPermutations and How to Use Themn2 questionsnSimple Operations with Factorialsn03:35nSimple Operations with Factorialsn3 questionsnSolving Variations with Repetitionn02:59nSolving Variations with Repetitionn3 questionsnSolving Variations without Repetitionn03:48nSolving Variations without Repetitionn3 questionsnSolving Combinationsn04:51nSolving Combinationsn4 questionsnSymmetry of Combinationsn03:26nSymmetry of Combinationsn1 questionnSolving Combinations with Separate Sample Spacesn02:52nSolving Combinations with Separate Sample Spacesn1 questionnCombinatorics in Real-Life: The Lotteryn03:12nCombinatorics in Real-Life: The Lotteryn1 questionnA Recap of Combinatoricsn02:55nA Practical Example of Combinatoricsn10:53n-Probability – Bayesian Inferencen54:38nSets and Eventsn04:25nSets and Eventsn3 questionsnWays Sets Can Interactn03:45nWays Sets Can Interactn2 questionsnIntersection of Setsn02:06nIntersection of Setsn3 questionsnUnion of Setsn04:51nUnion of Setsn3 questionsnMutually Exclusive Setsn02:09nMutually Exclusive Setsn4 questionsnDependence and Independence of Setsn03:01nDependence and Independence of Setsn3 questionsnThe Conditional Probability Formulan04:16nThe Conditional Probability Formulan3 questionsnThe Law of Total Probabilityn03:03nThe Additive Rulen02:21nThe Additive Rulen2 questionsnThe Multiplication Lawn04:05nThe Multiplication Lawn2 questionsnBayes’ Lawn05:44nBayes’ Lawn2 questionsnA Practical Example of Bayesian Inferencen14:52n-Probability – Distributionsn01:17:12nFundamentals of Probability Distributionsn06:29nFundamentals of Probability Distributionsn3 questionsnTypes of Probability Distributionsn07:32nTypes of Probability Distributionsn2 questionsnCharacteristics of Discrete Distributionsn02:00nCharacteristics of Discrete Distributionsn2 questionsnDiscrete Distributions: The Uniform Distributionn02:13nDiscrete Distributions: The Uniform Distributionn2 questionsnDiscrete Distributions: The Bernoulli Distributionn03:26nDiscrete Distributions: The Bernoulli Distributionn1 questionnDiscrete Distributions: The Binomial Distributionn07:04nDiscrete Distributions: The Binomial Distributionn1 questionnDiscrete Distributions: The Poisson Distributionn05:27nDiscrete Distributions: The Poisson Distributionn1 questionnCharacteristics of Continuous Distributionsn07:12nCharacteristics of Continuous Distributionsn1 questionnContinuous Distributions: The Normal Distributionn04:08nContinuous Distributions: The Normal Distributionn1 questionnContinuous Distributions: The Standard Normal Distributionn04:25nContinuous Distributions: The Standard Normal Distributionn1 questionnContinuous Distributions: The Students’ T Distributionn02:29nContinuous Distributions: The Students’ T Distributionn1 questionnContinuous Distributions: The Chi-Squared Distributionn02:22nContinuous Distributions: The Chi-Squared Distributionn1 questionnContinuous Distributions: The Exponential Distributionn03:15nContinuous Distributions: The Exponential Distributionn1 questionnContinuous Distributions: The Logistic Distributionn04:07nContinuous Distributions: The Logistic Distributionn1 questionnA Practical Example of Probability Distributionsn15:03n-Probability – Probability in Other Fieldsn18:51nProbability in Financen07:46nProbability in Statisticsn06:18nProbability in Data Sciencen04:47n-Part 3: Statisticsn04:02nPopulation and Samplen04:02nPopulation and Samplen2 questionsn-Statistics – Descriptive Statisticsn48:11nTypes of Datan04:33nTypes of Datan2 questionsnLevels of Measurementn03:43nLevels of Measurementn2 questionsnCategorical Variables – Visualization TechniquesnPreviewn04:52nCategorical Variables – Visualization Techniquesn1 questionnCategorical Variables Exercisen00:03nNumerical Variables – Frequency Distribution Tablen03:09nNumerical Variables – Frequency Distribution Tablen1 questionnNumerical Variables Exercisen00:03nThe Histogramn02:14nThe Histogramn1 questionnHistogram Exercisen00:03nCross Tables and Scatter Plotsn04:44nCross Tables and Scatter Plotsn1 questionnCross Tables and Scatter Plots Exercisen00:03nMean, median and moden04:20nMean, Median and Mode Exercisen00:03nSkewnessn02:37nSkewnessn1 questionnSkewness Exercisen00:03nVariancen05:55nVariance Exercisen00:15nStandard Deviation and Coefficient of Variationn04:40nStandard Deviationn1 questionnStandard Deviation and Coefficient of Variation Exercisen00:03nCovariancen03:23nCovariancen1 questionnCovariance Exercisen00:03nCorrelation Coefficientn03:17nCorrelationn1 questionnCorrelation Coefficient Exercisen00:03n-Statistics – Practical Example: Descriptive Statisticsn16:18nPractical Example: Descriptive StatisticsnPreviewn16:15nPractical Example: Descriptive Statistics Exercisen00:03n-Statistics – Inferential Statistics Fundamentalsn21:53nIntroductionn01:00nWhat is a Distributionn04:33nWhat is a Distributionn1 questionnThe Normal Distributionn03:54nThe Normal Distributionn1 questionnThe Standard Normal Distributionn03:30nThe Standard Normal Distributionn1 questionnThe Standard Normal Distribution Exercisen00:03nCentral Limit Theoremn04:20nCentral Limit Theoremn1 questionnStandard errorn01:26nStandard Errorn1 questionnEstimators and Estimatesn03:07nEstimators and Estimatesn1 questionn-Statistics – Inferential Statistics: Confidence Intervalsn44:25nWhat are Confidence Intervals?n02:41nWhat are Confidence Intervals?n1 questionnConfidence Intervals; Population Variance Known; z-scoren08:01nConfidence Intervals; Population Variance Known; z-score; Exercisen00:03nConfidence Interval Clarificationsn04:38nStudent’s T Distributionn03:22nStudent’s T Distributionn1 questionnConfidence Intervals; Population Variance Unknown; t-scoren04:36nConfidence Intervals; Population Variance Unknown; t-score; Exercisen00:03nMargin of Errorn04:52nMargin of Errorn1 questionnConfidence intervals. Two means. Dependent samplesn06:04nConfidence intervals. Two means. Dependent samples Exercisen00:03nConfidence intervals. Two means. Independent samples (Part 1)n04:31nConfidence intervals. Two means. Independent samples (Part 1) Exercisen00:03nConfidence intervals. Two means. Independent samples (Part 2)n03:57nConfidence intervals. Two means. Independent samples (Part 2) Exercisen00:03nConfidence intervals. Two means. Independent samples (Part 3)n01:27n-Statistics – Practical Example: Inferential Statisticsn10:08nPractical Example: Inferential Statisticsn10:05nPractical Example: Inferential Statistics Exercisen00:03n-Statistics – Hypothesis Testingn48:24nNull vs Alternative HypothesisnPreviewn05:51nFurther Reading on Null and Alternative Hypothesisn01:16nNull vs Alternative Hypothesisn2 questionsnRejection Region and Significance Leveln07:05nRejection Region and Significance Leveln2 questionsnType I Error and Type II Errorn04:14nType I Error and Type II Errorn4 questionsnTest for the Mean. Population Variance Knownn06:34nTest for the Mean. Population Variance Known Exercisen00:03np-valuen04:13np-valuen4 questionsnTest for the Mean. Population Variance Unknownn04:48nTest for the Mean. Population Variance Unknown Exercisen00:03nTest for the Mean. Dependent Samplesn05:18nTest for the Mean. Dependent Samples Exercisen00:03nTest for the mean. Independent samples (Part 1)n04:22nTest for the mean. Independent samples (Part 1). Exercisen00:03nTest for the mean. Independent samples (Part 2)n04:26nTest for the mean. Independent samples (Part 2)n1 questionnTest for the mean. Independent samples (Part 2) Exercisen00:03n-Statistics – Practical Example: Hypothesis Testingn07:19nPractical Example: Hypothesis Testingn07:16nPractical Example: Hypothesis Testing Exercisen00:03n-Part 4: Introduction to Pythonn32:49nIntroduction to Programmingn05:04nIntroduction to Programmingn2 questionsnWhy Python?n05:11nWhy Python?n2 questionsnWhy Jupyter?n03:29nWhy Jupyter?n2 questionsnInstalling Python and Jupytern06:49nUnderstanding Jupyter’s Interface – the Notebook Dashboardn03:15nPrerequisites for Coding in the Jupyter Notebooksn06:15nJupyter’s Interfacen3 questionsnPython 2 vs Python 3n02:46n-Python – Variables and Data Typesn19:17nVariablesn04:52nVariablesn1 questionnNumbers and Boolean Values in Pythonn03:05nNumbers and Boolean Values in Pythonn1 questionnPython Stringsn11:20nPython Stringsn3 questionsn-Python – Basic Python Syntaxn15:13nUsing Arithmetic Operators in Pythonn03:23nUsing Arithmetic Operators in Pythonn1 questionnThe Double Equality Signn01:33nThe Double Equality Signn1 questionnHow to Reassign Valuesn01:08nHow to Reassign Valuesn1 questionnAdd Commentsn03:20nAdd Commentsn1 questionnUnderstanding Line Continuationn00:49nnGet immediately download The Data Science Course 2019: Complete Data Science BootcampnIndexing Elementsn01:18nIndexing Elementsn1 questionnStructuring with Indentationn03:42nStructuring with Indentationn1 questionn-Python – Other Python Operatorsn07:45nComparison Operatorsn02:10nComparison Operatorsn2 questionsnLogical and Identity Operatorsn05:35nLogical and Identity Operatorsn2 questionsn-Python – Conditional Statementsn27:44nThe IF Statementn06:13nThe IF Statementn1 questionnThe ELSE Statementn05:37nThe ELIF Statementn11:16nA Note on Boolean Valuesn04:38nA Note on Boolean Valuesn1 questionn-Python – Python Functionsn29:26nDefining a Function in Pythonn04:20nHow to Create a Function with a Parametern07:58nDefining a Function in Python – Part IIn05:29nHow to Use a Function within a Functionn01:49nConditional Statements and Functionsn03:06nFunctions Containing a Few Argumentsn02:48nBuilt-in Functions in Pythonn03:56nPython Functionsn2 questionsn-Python – Sequencesn34:49nListsn08:18nListsn1 questionnUsing Methodsn06:54nUsing Methodsn1 questionnList Slicingn04:30nTuplesn06:40nDictionariesn08:27nDictionariesn1 questionn-Python – Iterationsn32:30nFor LoopsnPreviewn05:40nFor Loopsn1 questionnWhile Loops and Incrementingn05:10nLists with the range() Functionn06:22nLists with the range() Functionn1 questionnConditional Statements and Loopsn06:30nConditional Statements, Functions, and Loopsn02:27nHow to Iterate over Dictionariesn06:21n-Python – Advanced Python Toolsn12:56nObject Oriented Programmingn05:00nObject Oriented Programmingn2 questionsnModules and Packagesn01:05nModules and Packagesn2 questionsnWhat is the Standard Library?n02:47nWhat is the Standard Library?n1 questionnImporting Modules in Pythonn04:04nImporting Modules in Pythonn2 questionsn-Part 5: Advanced Statistical Methods in Pythonn01:27nIntroduction to Regression Analysisn01:27nIntroduction to Regression Analysisn1 questionn-Advanced Statistical Methods – Linear regression with StatsModelsn40:55nThe Linear Regression Modeln05:50nThe Linear Regression Modeln2 questionsnCorrelation vs Regressionn01:43nCorrelation vs Regressionn1 questionnGeometrical Representation of the Linear Regression Modeln01:25nGeometrical Representation of the Linear Regression Modeln1 questionnPython Packages Installationn04:39nFirst Regression in Pythonn07:11nFirst Regression in Python Exercisen00:39nUsing Seaborn for Graphsn01:21nHow to Interpret the Regression Tablen05:47nHow to Interpret the Regression Tablen3 questionsnDecomposition of Variabilityn03:37nDecomposition of Variabilityn1 questionnWhat is the OLS?n03:13nWhat is the OLSn1 questionnR-Squaredn05:30nR-Squaredn2 questionsn-Advanced Statistical Methods – Multiple Linear Regression with StatsModelsn42:18nMultiple Linear Regressionn02:55nMultiple Linear Regressionn1 questionnAdjusted R-Squaredn06:00nAdjusted R-Squaredn3 questionsnMultiple Linear Regression Exercisen00:03nTest for Significance of the Model (F-Test)n02:01nOLS Assumptionsn02:21nOLS Assumptionsn1 questionnA1: Linearityn01:50nA1: Linearityn2 questionsnA2: No Endogeneityn04:09nA2: No Endogeneityn1 questionnA3: Normality and Homoscedasticityn05:47nA4: No Autocorrelationn03:31nA4: No autocorrelationn2 questionsnA5: No Multicollinearityn03:26nA5: No Multicollinearityn1 questionnDealing with Categorical Data – Dummy Variablesn06:43nDealing with Categorical Data – Dummy Variablesn00:03nMaking Predictions with the Linear Regressionn03:29n-Advanced Statistical Methods – Linear Regression with sklearnn54:27nWhat is sklearn and How is it Different from Other Packagesn02:14nHow are Going to Approach this Section?n01:56nSimple Linear Regression with sklearnnPreviewn05:38nSimple Linear Regression with sklearn – A StatsModels-like Summary TablenPreviewn04:49nA Note on Normalizationn00:09nSimple Linear Regression with sklearn – Exercisen00:03nMultiple Linear Regression with sklearnn03:10nCalculating the Adjusted R-Squared in sklearnn04:45nCalculating the Adjusted R-Squared in sklearn – Exercisen00:03nFeature Selection (F-regression)n04:41nA Note on Calculation of P-values with sklearnn00:13nCreating a Summary Table with p-valuesn02:10nMultiple Linear Regression – Exercisen00:03nFeature Scaling (Standardization)n05:38nFeature Selection through Standardization of Weightsn05:22nPredicting with the Standardized Coefficientsn03:53nFeature Scaling (Standardization) – Exercisen00:03nUnderfitting and Overfittingn02:42nTrain – Test Split Explainedn06:54n-Advanced Statistical Methods – Practical Example: Linear Regressionn37:58nPractical Example: Linear Regression (Part 1)n11:59nPractical Example: Linear Regression (Part 2)n06:12nA Note on Multicollinearityn00:14nPractical Example: Linear Regression (Part 3)n03:15nDummies and Variance Inflation Factor – Exercisen00:03nPractical Example: Linear Regression (Part 4)n08:10nDummy Variables – Exercisen00:14nPractical Example: Linear Regression (Part 5)n07:34nLinear Regression – Exercisen00:16n-Advanced Statistical Methods – Logistic Regressionn40:49nIntroduction to Logistic Regressionn01:19nA Simple Example in Pythonn04:42nLogistic vs Logit Functionn04:00nBuilding a Logistic Regressionn02:48nBuilding a Logistic Regression – Exercisen00:03nAn Invaluable Coding Tipn02:26nUnderstanding Logistic Regression Tablesn04:06nUnderstanding Logistic Regression Tables – Exercisen00:03nWhat do the Odds Actually Meann04:30nBinary Predictors in a Logistic Regressionn04:32nBinary Predictors in a Logistic Regression – Exercisen00:03nCalculating the Accuracy of the Modeln03:21nCalculating the Accuracy of the Modeln00:03nUnderfitting and Overfittingn03:43nTesting the Modeln05:05nTesting the Model – Exercisen00:03n-Advanced Statistical Methods – Cluster Analysisn14:03nIntroduction to Cluster Analysisn03:41nSome Examples of Clustersn04:31nDifference between Classification and Clusteringn02:32nMath Prerequisitesn03:19n-Advanced Statistical Methods – K-Means Clusteringn49:01nK-Means Clusteringn04:41nA Simple Example of Clusteringn07:48nA Simple Example of Clustering – Exercisen00:03nClustering Categorical Datan02:50nClustering Categorical Data – Exercisen00:03nHow to Choose the Number of Clustersn06:11nHow to Choose the Number of Clusters – Exercisen00:03nPros and Cons of K-Means Clusteringn03:23nTo Standardize or not to Standardizen04:32nRelationship between Clustering and Regressionn01:31nMarket Segmentation with Cluster Analysis (Part 1)n06:03nMarket Segmentation with Cluster Analysis (Part 2)n06:58nHow is Clustering Useful?n04:47nEXERCISE: Species Segmentation with Cluster Analysis (Part 1)n00:03nEXERCISE: Species Segmentation with Cluster Analysis (Part 2)n00:03n-Advanced Statistical Methods – Other Types of Clusteringn13:34nTypes of Clusteringn03:39nDendrogramn05:21nHeatmapsnPreviewn04:34n-Part 6: Mathematicsn51:01nWhat is a matrix?n03:37nWhat is a Matrix?n6 questionsnScalars and Vectorsn02:58nScalars and Vectorsn5 questionsnLinear Algebra and Geometryn03:06nLinear Algebra and Geometryn3 questionsnArrays in Python – A Convenient Way To Represent Matricesn05:09nWhat is a Tensor?n03:00nWhat is a Tensor?n2 questionsnAddition and Subtraction of Matricesn03:36nAddition and Subtraction of Matricesn3 questionsnErrors when Adding Matricesn02:01nTranspose of a Matrixn05:13nDot Productn03:48nDot Product of Matricesn08:23nWhy is Linear Algebra Useful?n10:10n-Part 7: Deep Learningn03:07nWhat to Expect from this Part?n03:07nWhat is Machine Learningn4 questionsn-Deep Learning – Introduction to Neural Networksn42:38nIntroduction to Neural Networksn04:09nIntroduction to Neural Networksn1 questionnTraining the Modeln02:54nTraining the Modeln3 questionsnTypes of Machine Learningn03:43nnGet immediately download The Data Science Course 2019: Complete Data Science BootcampTypes of Machine Learningn4 questionsnThe Linear Model (Linear Algebraic Version)n03:08nThe Linear Modeln2 questionsnThe Linear Model with Multiple Inputsn02:25nThe Linear Model with Multiple Inputsn2 questionsnThe Linear model with Multiple Inputs and Multiple Outputsn04:25nThe Linear model with Multiple Inputs and Multiple Outputsn3 questionsnGraphical Representation of Simple Neural Networksn01:47nGraphical Representation of Simple Neural Networksn1 questionnWhat is the Objective Function?n01:27nWhat is the Objective Function?n2 questionsnCommon Objective Functions: L2-norm Lossn02:04nCommon Objective Functions: L2-norm Lossn3 questionsnCommon Objective Functions: Cross-Entropy Lossn03:55nCommon Objective Functions: Cross-Entropy Lossn4 questionsnOptimization Algorithm: 1-Parameter Gradient Descentn06:33nOptimization Algorithm: 1-Parameter Gradient Descentn4 questionsnOptimization Algorithm: n-Parameter Gradient Descentn06:08nOptimization Algorithm: n-Parameter Gradient Descentn3 questionsn-Deep Learning – How to Build a Neural Network from Scratch with NumPyn20:35nBasicExample (Part 1)n03:06nBasicExample (Part 2)n04:58nBasicExample (Part 3)n03:25nBasicExample (Part 4)n08:15nBasicExample Exercisesn00:51n-Deep Learning – TensorFlow 2.0: Introductionn28:10nHow to Install TensorFlow 2.0n05:02nTensorFlow Outline and Comparison with Other Librariesn03:28nTensorFlow 1 vs TensorFlow 2n02:33nA Note on TensorFlow 2 Syntaxn00:58nTypes of File Formats Supporting TensorFlown02:34nOutlining the Model with TensorFlow 2n05:48nInterpreting the Result and Extracting the Weights and Biasn04:09nCustomizing a TensorFlow 2 Modeln02:51nBasicwith TensorFlow: Exercisesn00:47n-Deep Learning – Digging Deeper into NNs: Introducing Deep Neural Networksn25:44nWhat is a Layer?n01:53nWhat is a Deep Net?n02:18nDigging into a Deep Netn04:58nNon-Linearities and their Purposen02:59nActivation Functionsn03:37nActivation Functions: Softmax Activationn03:24nBackpropagationn03:12nBackpropagation picturen03:02nBackpropagation – A Peek into the Mathematics of Optimizationn00:21n-Deep Learning – Overfittingn19:36nWhat is Overfitting?n03:51nUnderfitting and Overfitting for Classificationn01:52nWhat is Validation?n03:22nTraining, Validation, and Test Datasetsn02:30nN-Fold Cross Validationn03:07nEarly Stopping or When to Stop Trainingn04:54n-Deep Learning – Initializationn08:04nWhat is Initialization?n02:32nTypes of Simple Initializationsn02:47nState-of-the-Art Method – (Xavier) Glorot Initializationn02:45n-Deep Learning – Digging into Gradient Descent and Learning Rate Schedulesn20:40nStochastic Gradient Descentn03:24nProblems with Gradient Descentn02:02nMomentumn02:30nLearning Rate Schedules, or How to Choose the Optimal Learning Raten04:25nLearning Rate Schedules Visualizedn01:32nAdaptive Learning Rate Schedules (AdaGrad and RMSprop )n04:08nAdam (Adaptive Moment Estimation)n02:39n-Deep Learning – Preprocessingn14:33nPreprocessing Introductionn02:51nTypes of Basic Preprocessingn01:17nStandardizationn04:31nPreprocessing Categorical Datan02:15nBinary and One-Hot Encodingn03:39n-Deep Learning – Classifying on the MNIST Datasetn36:34nMNIST: The Datasetn02:25nMNIST: How to Tackle the MNISTn02:44nMNIST: Importing the Relevant Packages and Loading the Datan02:11nMNIST: Preprocess the Data – Create a Validation Set and Scale Itn04:43nMNIST: Preprocess the Data – Scale the Test Data – Exercisen00:03nMNIST: Preprocess the Data – Shuffle and Batchn06:30nMNIST: Preprocess the Data – Shuffle and Batch – Exercisen00:03nMNIST: Outline the Modeln04:54nMNIST: Select the Loss and the Optimizern02:05nMNIST: Learningn05:38nMNIST – Exercisesn01:21nMNIST: Testing the Modeln03:56n-Deep Learning – Business Case Examplen39:19nBusiness Case: Exploring the Dataset and Identifying Predictorsn07:54nBusiness Case: Outlining the Solutionn01:31nBusiness Case: Balancing the Datasetn03:39nBusiness Case: Preprocessing the Datan11:32nBusiness Case: Preprocessing the Data – Exercisen00:12nBusiness Case: Load the Preprocessed Datan03:23nBusiness Case: Load the Preprocessed Data – Exercisen00:03nBusiness Case: Learning and Interpreting the Resultn04:15nBusiness Case: Setting an Early Stopping Mechanismn05:01nSetting an Early Stopping Mechanism – Exercisen00:08nBusiness Case: Testing the Modeln01:23nBusiness Case: Final Exercisen00:16n-Deep Learning – Conclusionn17:26nSummary on What You’ve Learnedn03:41nWhat’s Further out there in terms of Machine Learningn01:47nDeepMind and Deep Learningn00:21nAn overview of CNNsn04:55nAn Overview of RNNsn02:50nAn Overview of non-NN Approachesn03:52n-Appendix: Deep Learning – TensorFlow 1: Introductionn28:52nREAD ME!!!!n00:21nHow to Install TensorFlow 1n02:20nA Note on Installing Packages in Anacondan01:14nTensorFlow Intron03:46nActual Introduction to TensorFlown01:40nTypes of File Formats, supporting Tensorsn02:38nBasicExample with TF: Inputs, Outputs, Targets, Weights, Biasesn06:05nBasicExample with TF: Loss Function and Gradient Descentn03:41nBasicExample with TF: Model Outputn06:05nBasicExample with TF Exercisesn01:01n-Appendix: Deep Learning – TensorFlow 1: Classifying on the MNIST Datasetn39:31nMNIST: What is the MNIST Dataset?n02:26nMNIST: How to Tackle the MNISTn02:48nMNIST: Relevant Packagesn01:34nMNIST: Model Outlinen06:51nMNIST: Loss and Optimization Algorithmn02:39nCalculating the Accuracy of the Modeln04:18nMNIST: Batching and Early Stoppingn02:08nMNIST: Learningn07:35nMNIST: Results and Testingn06:11nMNIST: Solutionsn01:31nMNIST: Exercisesn01:29n-Appendix: Deep Learning – TensorFlow 1: Business Casen50:57nBusiness Case: Getting acquainted with the datasetn07:55nBusiness Case: Outlining the Solutionn01:57nThe Importance of Working with a Balanced Datasetn03:39nBusiness Case: Preprocessingn11:35nBusiness Case: Preprocessing Exercisen00:13nCreating a Data Providern06:37nBusiness Case: Model Outlinen05:34nBusiness Case: Optimizationn05:10nBusiness Case: Interpretationn02:05nBusiness Case: Testing the Modeln02:04nBusiness Case: A Comment on the Homeworkn03:51nBusiness Case: Final Exercisen00:17n-Software Integrationn29:38nWhat are Data, Servers, Clients, Requests, and Responsesn04:43nWhat are Data, Servers, Clients, Requests, and Responsesn2 questionsnWhat are Data Connectivity, APIs, and Endpoints?n07:05nWhat are Data Connectivity, APIs, and Endpoints?n2 questionsnTaking a Closer Look at APIsn08:05nTaking a Closer Look at APIsn2 questionsnCommunication between Software Products through Text Filesn04:20nCommunication between Software Products through Text Filesn1 questionnSoftware Integration – Explainedn05:25nSoftware Integration – Explainedn2 questionsn-Case Study – What’s Next in the Course?n10:14nGame Plan for this Python, SQL, and Tableau Business Exercisen04:08nThe Business Taskn02:48nIntroducing the Data Setn03:18nIntroducing the Data Setn1 questionn-Case Study – Preprocessing the ‘Absenteeism_data’n01:29:34nWhat to Expect from the Following Sections?n01:28nImporting the Absenteeism Data in Pythonn03:23nChecking the Content of the Data Setn05:53nIntroduction to Terms with Multiple Meaningsn03:27nWhat’s Regression Analysis – a Quick Refreshern01:50nUsing a Statistical Approach towards the Solution to the Exercisen02:17nDropping a Column from a DataFrame in Pythonn06:27nEXERCISE – Dropping a Column from a DataFrame in Pythonn00:26nSOLUTION – Dropping a Column from a DataFrame in Pythonn00:01nAnalyzing the Reasons for Absencen05:04nObtaining Dummies from a Single Featuren08:37nEXERCISE – Obtaining Dummies from a Single Featuren00:04nSOLUTION – Obtaining Dummies from a Single Featuren00:00nDropping a Dummy Variable from the Data Setn01:32nMore on Dummy Variables: A Statistical Perspectiven01:28nClassifying the Various Reasons for Absencen08:35nUsing .concat() in Pythonn04:35nEXERCISE – Using .concat() in Pythonn00:04nSOLUTION – Using .concat() in Pythonn00:01nReordering Columns in a Pandas DataFrame in Pythonn01:43nEXERCISE – Reordering Columns in a Pandas DataFrame in Pythonn00:06nSOLUTION – Reordering Columns in a Pandas DataFrame in Pythonn00:12nCreating Checkpoints while Coding in Jupytern02:52nEXERCISE – Creating Checkpoints while Coding in Jupytern00:04nSOLUTION – Creating Checkpoints while Coding in Jupytern00:00nAnalyzing the Dates from the Initial Data Setn07:48nExtracting the Month Value from the “Date” Columnn07:00nExtracting the Day of the Week from the “Date” Columnn03:36nEXERCISE – Removing the “Date” Columnn00:37nAnalyzing Several “Straightforward” Columns for this Exercisen03:17nWorking on “Education”, “Children”, and “Pets”n04:38nFinal Remarks of this Sectionn01:59nA Note on Exporting Your Data as a *.csv Filen00:26n-Case Study – Applying Machine Learning to Create the ‘absenteeism_module’n01:07:05nExploring the Problem with a Machine Learning Mindsetn03:20nCreating the Targets for the Logistic Regressionn06:32nSelecting the Inputs for the Logistic Regressionn02:41nStandardizing the Datan03:26nSplitting the Data for Training and Testingn06:12nFitting the Model and Assessing its Accuracyn05:39nCreating a Summary Table with the Coefficients and Interceptn05:16nInterpreting the Coefficients for Our Problemn06:14nStandardizing only the Numerical Variables (Creating a Custom Scaler)n04:12nInterpreting the Coefficients of the Logistic Regressionn05:10nBackward Elimination or How to Simplify Your Modeln04:02nTesting the Model We Createdn04:43nSaving the Model and Preparing it for Deploymentn04:06nARTICLE – A Note on ‘pickling’n01:15nEXERCISE – Saving the Model (and Scaler)n00:13nPreparing the Deployment of the Model through a Modulen04:04n-Case Study – Loading the ‘absenteeism_module’n10:58nAre You Sure You’re All Set?n00:14nDeploying the ‘absenteeism_module’ – Part In03:50nDeploying the ‘absenteeism_module’ – PartINSTITUTIONAL RISK DISCLOSURE: Trading foreign exchange, cryptocurrencies, and algorithmic assets on margin carries a high level of risk and may not be 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