About Program - MLRITM

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SUMMER INTERNSHIP - 2019 @ MLRITMFrom June 3rd to 27th JuneAbout ProgramSmartBridge in collaboration with IBM, powered by Hello Intern is elated to announce flagship event?Summer Internship Program 2019?for students on the latest emerging technologies:Artificial Intelligence - the pace of progressInternet of Things - the automated worldMachine Learning - intelligent computerThis platform could be the turning points of career. Internet of Things (IoT) with IBM CloudModule - 1: Internet of Things (IoT) & IoT ApplicationsIOT Applications & projectsIOT Architecture & Deployment modelsBuilding Blocks of IOTApplicationsModule - 2: IoT Devices & Open Hardware PlatformsIntroduction to Open Hardware PlatformsIntroduction to ESP8266 development board (ESP12E)Programming Analog & Digital I/O' s with Arduino IDEIntegrate Analog & Digital Sensors with ESP8266Module - 3: IoT Communication Technologies & ProtocolsDevice Network ConnectivityClient - Server Communication ModelPublish - Subscribe Communication ModelWorking with ESP8266WIFI & ESP8266WEBSERVER librariesSmart Home Automation using ESP8266Module - 4: IoT Platforms & ArchitectureImportance of IOT Platform & its generic ArchitectureGetting Started with IBM Watson IOT PlatformConnect ESP8266 to Watson IOT PlatformSend Sensor data to Watson IOT Platform using MQTT and HTTPVisualizing real-time data by using boards and cardsIntroduction to Cloudant NoSQL DBQuery and Process Watson IoT Device Data from Cloudant NoSQL DBAPI & Client Libraries for Cloudant NoSQL DBModule - 5:?Application Development with Node-RED & MIT App InventorIntroduction to Application DevelopmentMIT App Inventor for Android App DevelopmentPerform Retrieve & Update data operations from MIT App InventorIntroduction to Node-REDWeb App development using NodeREDCreate a Node-RED application to send commands to deviceModule - 6: IoT Gateways & Gateway ProgrammingIntroduction to IOT GatewaysPurpose of IoT GatewayIntroduction to Raspberry PiOS Installation & ConfigurationsIntroduction to PythonBasic Data Types and AssignmentsIdentifiers and IndentationData OperationsSequence Types, Tuples, ListsOperators and ExpressionsDictionary and SetsProgramming GPIO pinsWorking with DC Motor & Servo MotorModule - 7: Cognitive Computing with IBM Watson PlatformIntroduction to IBM Watson Conversation ServiceCreate an Instance of Conversation ServiceCreate a simple conversation AppWork on Recipe - Talk to your Sensor using the Watson IoT PlatformModule - 8: Build IoT UsecasesBuild IoT UsecasesMachine Learning with Python & IBM Watson StudioModule - 1: Introduction to Machine LearningWhat is Machine LearningUsecases of Machine LearningRole of Machine Learning EngineerMachine Learning AlgorithmsMachine Learning Tools & PackagesModule – 2: Python ProgrammingIntroduction to python programming and Environment SetupPython BasicsData typesExpressions and VariablesString OperationsPython Data StructuresPython Programming FundamentalsConditions and BranchingLoopsFunctionsPackagesModule - 3: Python for Data ScienceIntroduction to NumPy2D NumPy ArrayNumPy: Basic StatisticsIntroduction to MatplotlibBasic Plots with MatplotlibHistogramsCustomizationIntroduction to PandasDictionaries & Data framesData ManipulationsModule - 4: Importing Data in PythonImport data from txt filesImport data from flat files with NumPyImport data from other file typesImport data from DatabasesImport data from web through API’sCleaning Data for AnalysisModule - 5: Getting Started with Machine LearningFundamentals of Machine LearningSupervised & Unsupervised learningRegression & ClassificationMachine Learning TerminologyModule - 6: Supervised Learning - RegressionIntroduction to Scikit-Learn PackageRegression AnalysisLinear RegressionLogistic RegressionPolynomial RegressionSelection of Right Regression ModelModule - 7: Supervised Learning – ClassificationIntroduction to Classification ProblemsLogistic RegressionDecision TreeSupport Vector MachineK-Nearest NeighboringNaive-BayesRandom ForestModule - 8: Machine Learning – IBM Watson StudioGetting started with IBM Watson StudioUnderstand the featuresOrganize resources in a projectSet up a projectWatson Data Platform projectsProject CollaboratorsAdd associated servicesPrepare dataAdd data to a projectRefine dataIngest streaming dataWorking with Jupyter NotebooksCreate notebooksCode and run notebooksShare and publish notebooksWatson Machine LearningSetting up your machine learning environmentBuilding modelsDeploying the model & integration to AppsModule - 9: Project DevelopmentProject Work - 1Project Work – 2Artificial IntelligenceModule - 1: Introduction to Artificial intelligence and PythonIntroduction to Artificial IntelligenceIntroduction to python programming and Environment SetupPython BasicsHello World ExampleData typesExpressions and VariablesString OperationsPython Data StructuresLists and TuplesSetsDictionariesPython Programming FundamentalsConditions and BranchingLoopsFunctionsModule - 2 : Python ProgrammingPython - Files I/OFile HandlingCreate a New FileWrite to an Existing FileDelete a FilePython - Exceptions HandlingWhat is Exception?Handling an exceptionArgument of an ExceptionRaising an ExceptionsUser-Defined ExceptionsPython - Object OrientedOverview of OOP TerminologyCreating ClassesCreating Instance ObjectsAccessing AttributesBuilt-In Class AttributesModule - 3: Python for AIWorking with Data in PythonReading files with openWriting files with openLoading data with PandasWorking with and Saving data with PandasIntroduction to Visualization ToolsIntroduction to Data VisualizationIntroduction to MatplotlibBasic Plotting with MatplotlibDataset on Immigration to CanadaLine PlotsData PreprocessingImporting the DatasetHandle Missing DataCategorical DataSplitting the Dataset into the Training set and Test setFeature ScalingModule - 4: Introduction to Neural NetworksIntroduction to Neural NetworksThe NeuronThe Activation FunctionHow do Neural Networks work?How do Neural Networks learn?Gradient DescentStochastic Gradient DescentBackpropagationUnderstanding Neural Networks with TensorFlowActivation FunctionsIllustrate PerceptronTraining a PerceptronWhat is TensorFlow?TensorFlow code-basicsConstants, Placeholders, VariablesCreating a ModelBuilding ANN Using Tensorflow using sample datasetEvaluating, Improving and Tuning the ANNModule - 5: Working with Keras FrameworkIntroduction to Keras FrameworkIntroduction to the Sequential ModeActivation functionsLayersTrainingLoss functionsBuilding ANN Using Keras (Tensorflow backend) using sample datasetEvaluating, Improving and Tuning the ANNModule - 6: Convolutional Neural NetworksIntroduction to Convolutional Neural NetworksWhat are convolutional neural networks?Step 1 - Convolution OperationStep 1(b) - ReLU LayerStep 2 - PoolingStep 3 - FlatteningStep 4 - Full ConnectionClassification of images using CNNEvaluating, Improving and Tuning the CNNModule - 7: Recurrent Neural NetworksIntroduction to Recurrent Neural NetworksThe idea behind Recurrent Neural NetworksThe Vanishing Gradient ProblemLSTMsLSTM VariationsPredicting Google stock prices using RNNEvaluating, Improving and Tuning the RNNModule - 8: Natural Language ProcessingIntroduction to Natural Language ProcessingIntroduction to NTLKBag of Words modelNatural Language Processing in PythonSentiment analysis using Natural Language ProcessingCleaning the textsCreating the Bag of Words modelClassification of textsModule - 9: Explore IBM Watson StudioIntroduction to IBM CloudIntroduction to AI in IBM CloudExplore IBM Conversation ServiceBuild Chatbot' s using IBM Conversation serviceIntegrate Chatbot to ApplicationsExplore Visual Recognition serviceExplore Watson StudioBuild Deep learning models in Watson StudioDeploy models as web service ................
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