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HYPERLINK "" 1. Giving Computers the Ability to Learn from DataBuilding intelligent machines to transform data into knowledgeThe three different types of machine learningMaking predictions about the future with supervised learningClassification for predicting class labelsRegression for predicting continuous outcomesSolving interactive problems with reinforcement learningDiscovering hidden structures with unsupervised learningFinding subgroups with clusteringDimensionality reduction for data compressionIntroduction to the basic terminology and notationsA roadmap for building machine learning systemsPreprocessing – getting data into shapeTraining and selecting a predictive modelEvaluating models and predicting unseen data instancesUsing Python for machine learningInstalling Python and packages from the Python Package IndexUsing the Anaconda Python distribution and package managerPackages for scientific computing, data science, and machine learning2. Training Simple Machine Learning Algorithms for ClassificationArtificial neurons – a brief glimpse into the early history of machine learningThe formal definition of an artificial neuronThe perceptron learning ruleImplementing a perceptron learning algorithm in PythonAn object-oriented perceptron APITraining a perceptron model on the Iris datasetAdaptive linear neurons and the convergence of learningMinimizing cost functions with gradient descentImplementing Adaline in PythonImproving gradient descent through feature scalingLarge-scale machine learning and stochastic gradient descent3. A Tour of Machine Learning Classifiers Using scikit-learnChoosing a classification algorithmFirst steps with scikit-learn – training a perceptronModeling class probabilities via logistic regressionLogistic regression intuition and conditional probabilitiesLearning the weights of the logistic cost functionConverting an Adaline implementation into an algorithm for logistic regressionTraining a logistic regression model with scikit-learnTackling overfitting via regularizationMaximum margin classification with support vector machinesMaximum margin intuitionDealing with a nonlinearly separable case using slack variablesAlternative implementations in scikit-learnSolving nonlinear problems using a kernel SVMKernel methods for linearly inseparable dataUsing the kernel trick to find separating hyperplanes in high-dimensional spaceDecision tree learningMaximizing information gain – getting the most bang for your buckBuilding a decision treeCombining multiple decision trees via random forestsK-nearest neighbors – a lazy learning algorithm4. Building Good Training Sets – Data PreprocessingDealing with missing dataIdentifying missing values in tabular dataEliminating samples or features with missing valuesImputing missing valuesUnderstanding the scikit-learn estimator APIHandling categorical dataNominal and ordinal featuresCreating an example datasetMapping ordinal featuresEncoding class labelsPerforming one-hot encoding on nominal featuresPartitioning a dataset into separate training and test setsBringing features onto the same scaleSelecting meaningful featuresL1 and L2 regularization as penalties against model complexityA geometric interpretation of L2 regularizationSparse solutions with L1 regularizationSequential feature selection algorithmsAssessing feature importance with random forests5. Compressing Data via Dimensionality ReductionUnsupervised dimensionality reduction via principal component analysisThe main steps behind principal component analysisExtracting the principal components step by stepTotal and explained varianceFeature transformationPrincipal component analysis in scikit-learnSupervised data compression via linear discriminant analysisPrincipal component analysis versus linear discriminant analysisThe inner workings of linear discriminant analysisComputing the scatter matricesSelecting linear discriminants for the new feature subspaceProjecting samples onto the new feature spaceLDA via scikit-learnUsing kernel principal component analysis for nonlinear mappingsKernel functions and the kernel trickImplementing a kernel principal component analysis in PythonExample 1 – separating half-moon shapesExample 2 – separating concentric circlesProjecting new data pointsKernel principal component analysis in scikit-learn6. Learning Best Practices for Model Evaluation and Hyperparameter TuningStreamlining workflows with pipelinesLoading the Breast Cancer Wisconsin datasetCombining transformers and estimators in a pipelineUsing k-fold cross-validation to assess model performanceThe holdout methodK-fold cross-validationDebugging algorithms with learning and validation curvesDiagnosing bias and variance problems with learning curvesAddressing over- and underfitting with validation curvesFine-tuning machine learning models via grid searchTuning hyperparameters via grid searchAlgorithm selection with nested cross-validationLooking at different performance evaluation metricsReading a confusion matrixOptimizing the precision and recall of a classification modelPlotting a receiver operating characteristicScoring metrics for multiclass classificationDealing with class imbalance7. Combining Different Models for Ensemble LearningLearning with ensemblesCombining classifiers via majority voteImplementing a simple majority vote classifierUsing the majority voting principle to make predictionsEvaluating and tuning the ensemble classifierBagging – building an ensemble of classifiers from bootstrap samplesBagging in a nutshellApplying bagging to classify samples in the Wine datasetLeveraging weak learners via adaptive boostingHow boosting worksApplying AdaBoost using scikit-learn8. Applying Machine Learning to Sentiment AnalysisPreparing the IMDb movie review data for text processingObtaining the movie review datasetPreprocessing the movie dataset into more convenient formatIntroducing the bag-of-words modelTransforming words into feature vectorsAssessing word relevancy via term frequency-inverse document frequencyCleaning text dataProcessing documents into tokensTraining a logistic regression model for document classificationWorking with bigger data – online algorithms and out-of-core learningTopic modeling with Latent Dirichlet AllocationDecomposing text documents with LDALDA with scikit-learn9. Embedding a Machine Learning Model into a Web ApplicationSerializing fitted scikit-learn estimatorsSetting up an SQLite database for data storageDeveloping a web application with FlaskOur first Flask web applicationForm validation and renderingSetting up the directory structureImplementing a macro using the Jinja2 templating engineAdding style via CSSCreating the result pageTurning the movie review classifier into a web applicationFiles and folders – looking at the directory treeImplementing the main application as app.pySetting up the review formCreating a results page templateDeploying the web application to a public serverCreating a PythonAnywhere accountUploading the movie classifier applicationUpdating the movie classifier10. Predicting Continuous Target Variables with Regression AnalysisIntroducing linear regressionSimple linear regressionMultiple linear regressionExploring the Housing datasetLoading the Housing dataset into a data frameVisualizing the important characteristics of a datasetLooking at relationships using a correlation matrixImplementing an ordinary least squares linear regression modelSolving regression for regression parameters with gradient descentEstimating coefficient of a regression model via scikit-learnFitting a robust regression model using RANSACEvaluating the performance of linear regression modelsUsing regularized methods for regressionTurning a linear regression model into a curve – polynomial regressionAdding polynomial terms using scikit-learnModeling nonlinear relationships in the Housing datasetDealing with nonlinear relationships using random forestsDecision tree regressionRandom forest regression11. Working with Unlabeled Data – Clustering AnalysisGrouping objects by similarity using k-meansK-means clustering using scikit-learnA smarter way of placing the initial cluster centroids using k-means++Hard versus soft clusteringUsing the elbow method to find the optimal number of clustersQuantifying the quality of clustering via silhouette plotsOrganizing clusters as a hierarchical treeGrouping clusters in bottom-up fashionPerforming hierarchical clustering on a distance matrixAttaching dendrograms to a heat mapApplying agglomerative clustering via scikit-learnLocating regions of high density via DBSCAN12. Implementing a Multilayer Artificial Neural Network from ScratchModeling complex functions with artificial neural networksSingle-layer neural network recapIntroducing the multilayer neural network architectureActivating a neural network via forward propagationClassifying handwritten digitsObtaining the MNIST datasetImplementing a multilayer perceptronTraining an artificial neural networkComputing the logistic cost functionDeveloping your intuition for backpropagationTraining neural networks via backpropagationAbout the convergence in neural networksA few last words about the neural network implementation13. Parallelizing Neural Network Training with TensorFlowTensorFlow and training performanceWhat is TensorFlow?How we will learn TensorFlowFirst steps with TensorFlowWorking with array structuresDeveloping a simple model with the low-level TensorFlow APITraining neural networks efficiently with high-level TensorFlow APIsBuilding multilayer neural networks using TensorFlow's Layers APIDeveloping a multilayer neural network with KerasChoosing activation functions for multilayer networksLogistic function recapEstimating class probabilities in multiclass classification via the softmax functionBroadening the output spectrum using a hyperbolic tangentRectified linear unit activationHYPERLINK ""14. Going Deeper – The Mechanics of TensorFlowKey features of TensorFlowTensorFlow ranks and tensorsHow to get the rank and shape of a tensorUnderstanding TensorFlow's computation graphsPlaceholders in TensorFlowDefining placeholdersFeeding placeholders with dataDefining placeholders for data arrays with varying batchsizesVariables in TensorFlowDefining variablesInitializing variablesVariable scopeReusing variablesBuilding a regression modelExecuting objects in a TensorFlow graph using their namesSaving and restoring a model in TensorFlowTransforming Tensors as multidimensional data arraysUtilizing control flow mechanics in building graphsVisualizing the graph with TensorBoardExtending your TensorBoard experience HYPERLINK "" 15. Classifying Images with Deep Convolutional Neural NetworksBuilding blocks of convolutional neural networksUnderstanding CNNs and learning feature hierarchiesPerforming discrete convolutionsPerforming a discrete convolution in one dimensionThe effect of zero-padding in a convolutionDetermining the size of the convolution outputPerforming a discrete convolution in 2DSubsamplingPutting everything together to build a CNNWorking with multiple input or color channelsRegularizing a neural network with dropoutImplementing a deep convolutional neural network using TensorFlowThe multilayer CNN architectureLoading and preprocessing the dataImplementing a CNN in the TensorFlow low-level APIImplementing a CNN in the TensorFlow Layers API16. Modeling Sequential Data Using Recurrent Neural NetworksIntroducing sequential dataModeling sequential data – order mattersRepresenting sequencesThe different categories of sequence modelingRNNs for modeling sequencesUnderstanding the structure and flow of an RNNComputing activations in an RNNThe challenges of learning long-range interactionsLSTM unitsImplementing a multilayer RNN for sequence modeling in TensorFlowProject one – performing sentiment analysis of IMDb movie reviews using multilayer RNNsPreparing the dataEmbeddingBuilding an RNN modelThe SentimentRNN class constructorThe build methodStep 1 – defining multilayer RNN cellsStep 2 – defining the initial states for the RNN cellsStep 3 – creating the RNN using the RNN cells and their statesThe train methodThe predict methodInstantiating the SentimentRNN classTraining and optimizing the sentiment analysis RNN modelProject two – implementing an RNN for character-level language modeling in TensorFlowPreparing the dataBuilding a character-level RNN modelThe constructorThe build methodThe train methodThe sample methodCreating and training the CharRNN ModelThe CharRNN model in the sampling mode ................
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