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Python + AIIntroduction to PythonBrief historyWhy Python?Where to use?AnacondaHow to install anacondaPython BasicsThe print statementCommentsPython Data structure & Data typesString operations in PythonSimple Input & OutputOutput FormattingPython Program FlowIndentationConditional statementsifif-elseif-elif-elseNested ifLoopsforwhileNested loopsThe range statementbreak, continue and passAssertLoop examples- Star patternsList and TuplesAbout SequencesIndexing and SlicingIterating through a sequenceSequence functions, keywords and operatorsList & its methodsTuples & its methodsList ComprehensionsNested SequencesDictionaries and SetsAbout dictionariesCreating, accessing dictionariesIterating through a dictionaryDictionary methodsAbout setsCreating, accessing setsSet operationsFrozen setsFunctions & ModulesFunction – definition, callingTypes of functionsFunction parametersVariable argumentsScope of a functionFunction Documentation/ DocstringsLambda function & map, filter, reduceFunction exerciseCreate moduleStandard ModulesOOPs in PythonClass & ObjectsVariable TypeStatic variable in classCreate classesInstance methodsConstructor and destructorsInheritance and its typesPolymorphismEncapsulationScope and visibility of variablesExceptionsErrors and its typesException Handling with tryHandling multiple ExceptionsWriting own exception/ custom exceptionsRaise an ExceptionFile HandlingFile Handling modesReading filesWriting & Appending to FilesHandling file exceptionsThe with statementRegular ExpressionsSimple Character MatchesSpecial CharactersCharacter classesQuantifiersThe Dot CharacterGreedy MatchesGroupingMatching at beginning or endMatch ObjectsSubstitutingSplitting a StringData StructuresList ComprehensionsNested List comprehensionsDictionary comprehensionsIteratorsGeneratorsThe functions any and allThe with statementData CompressionCloserDecoratorWriting GUI in PythonIntroductionComponents and eventsAn example GUIThe root componentAdding a buttonEntry widgetsText widgetsCheckbuttonsRadiobuttonsListboxesFramesMenusBinding Events to widgetsThread in PythonThread life CycleThread DefinitionThread ImplementationPython MySQL Database Access IntroductionInstallationDB ConnectionCreating DB TableINSERT, READ, UPDATE, DELETE operationsCOMMIT & ROLLBACK operationHandling ErrorsINTRODUCTION TO MACHINE LEARNINGWhat is ML?Types of MLML package: scikit-learnAnacondaHow to install anacondaBasic Introduction numpy and PandasIntroduction to NumPyCreating an arrayClass and Attributes of ndarrayBasic OperationsActivity-SliceStack operationsMathematical Functions of NumPyIntroduction to PandasUnderstanding DataFrameSeriesConcatenating and appending DataFramesloc and ilocDrop columns or rowsGroupbyMap and applyData PreprocessingIntroductionDealing with missing dataHandling categorical dataEncoding class labelsOne-not encodingSplit data into training and testing setsBringing features into same scaleRegressionIntroductionSimple Linear RegressionMultiple Linear RegressionPolynomial RegressionEvaluate performance of a linear regression modelOverfitting and UnderfittingK-Nearest Neighbours (KNN)KNN theoryImplementing KNN with scikit-learnKNN parametersn_neighborsmetricHow to find nearest neighborsWriting own KNN classifier from scratchLogistic RegressionLogistic Regression theoryImplementing Logistic Regression with scikit-learnLogistic Regression ParametersMulti-class classificationMNIST digit dataset with Logistic RegressionPredictive modelling on adult income datasetSupport Vector Machine (SVM)SVM theoryImplementing SVM with scikit-learnSVM parameters:C and gammaPlot hyperplane for linear classificationDecision functionDecision Tree and Random ForestTheory behind decision treeImplementing decision tree with scikit-learnDecision tree parametersCombining multiple decision trees via Random forestHow random forest works?Na?ve Bayes ClassificationTheory Naive Bayes AlgorithmFeatures extractionCountvectorizerTF-IDFText ClassificationModel Evaluation and Parameter TuningCross validation via K-FoldTuning hyperparameters via grid searchConfusion matrixRecall and PrecisionROC and AUCClustering and Dimension ReductionK-means ClusteringElbow methodPrincipal components analysis(PCA)PCA step by stepImplementing PCA with scikit-learnLDA with scikit-learnEnsemble TechniquesIntroductionTypes of ensemble techniquesBaggingBoostingTypes of BoostingAdaBoostGradient Tree BoostingXGBoostNatural Language ProcessingInstall nltkTokenize wordsTokenizing sentencesStop words with NLTKStemming words with NLTKSpeech taggingSentiment analysis with NLTKOpenCVBasics of Computer Vision & OpenCVImage ManipulationsImage segmentationObject detectionMachine learning in Computer VisionBasics of Neural NetworksDefinition of an artificial neural networkPerceptronMinimizing cost function with Gradient descentClassifying MNIST Handwritten digits with Multilayer PerceptronIntroduction to Neural NetworksWhat is Neural Network?How neural network works?Stochastic Gradient descent(SGD)Single Layer PerceptronMulti-layer perceptronBackpropagationBuilding deep learning environmentWhat is deep learning?Deep learning packagesDeep learning applicationsDL environment setupInstalling TensorflowInstalling KerasTensorflow BasicsWhat is TensorflowDifference between Tensorflow & numpyVariables, Placeholders and constantsComputation graphVisualize graph with Tensor boardActivation FunctionsWhat are activation functions?Hyperbolic tangent function (tanh)ReLU – Rectified Linear UnitSoftmax functionVanishing Gradient problemBuild feed forward neural networksExploring the MNIST datasetLoad MNIST dataset using Tensor flowDefining the hyper parametersInitialize weight & biasModel definitionDefining lost/cost functionTraining a neural networkImproving a NN by optimizers & RegularizationTypes of OptimizersSGD with momentumAdagradRmsPropAdamDropout Layers and RegularizationBatch NormalizationBuild a Neural Network Using KerasWhat is Keras?Installing KerasKeras fundamentals for deep learningKeras sequential model & functional APISolve linear regression & classification problem with exampleSaving & Loading a Keras ModelConvolution Neural Networks (CNNs)Introduction to CNNConvolutional operationsPooling, stridge and padding operationsData AugmentationAuto encoders for CNNPre-trained CNN modelsLeNetVGGNetResidual NetworkTransfer learningWord representation using Word2VecWord EmbeddingKeras Embedding LayerVisualize word embeddingLoad Google Word2Vc EmbeddingExample of Using pre-trained glove embeddingRecurrent Neural Networks (RNNs)An overview of RNNRNN ArchitectureTypes of RNNImplementing basic RNN in tensorflowNed for LSTM or GRUDeep RNNImplementing RNN for spam predictionSequence to sequence modelingDeveloping a prediction model for time-series dataText classification with LSTM ................
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