Mastering Predictive Analytics with Scikit-Learn and TensorFlow : Implement Machine Learning Techniques to Build Advanced Predictive Models Using Python.

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Cover; Title Page; Copyright and Credits; Packt Upsell; Contributor; Table of Contents; Preface; Chapter 1: Ensemble Methods for Regression and Classification; Ensemble methods and their working; Bootstrap sampling; Bagging; Random forests; Boosting; Ensemble methods for regression; The diamond dataset; Training different regression models; KNN model; Bagging model; Random forests model; Boosting model; Using ensemble methods for classification; Predicting a credit card dataset ; Training different regression models; Logistic regression model; Bagging model; Random forest model.

Boosting modelSummary; Chapter 2: Cross-validation and Parameter Tuning; Holdout cross-validation; K-fold cross-validation; Implementing k-fold cross-validation; Comparing models with k-fold cross-validation; Introduction to hyperparameter tuning; Exhaustive grid search; Hyperparameter tuning in scikit-learn; Comparing tuned and untuned models; Summary; Chapter 3: Working with Features; Feature selection methods ; Removing dummy features with low variance; Identifying important features statistically; Recursive feature elimination; Dimensionality reduction and PCA; Feature engineering.

Creating new featuresImproving models with feature engineering; Training your model; Reducible and irreducible error; Summary; Chapter 4: Introduction to Artificial Neural Networks and TensorFlow; Introduction to ANNs; Perceptrons; Multilayer perceptron; Elements of a deep neural network model; Deep learning; Elements of an MLP model; Introduction to TensorFlow; TensorFlow installation; Core concepts in TensorFlow; Tensors; Computational graph; Summary; Chapter 5: Predictive Analytics with TensorFlow and Deep Neural Networks; Predictions with TensorFlow; Introduction to the MNIST dataset.

Building classification models using MNIST datasetElements of the DNN model; Building the DNN; Reading the data; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Defining optimizer and training operations; Training strategy and valuation of accuracy of the classification; Running the computational graph; Regression with Deep Neural Networks (DNN); Elements of the DNN model; Building the DNN; Reading the data; Objects for modeling; Training strategy; Input pipeline for the DNN; Defining the architecture.

Placeholders for input values and labelsBuilding the DNN; The loss function; Defining optimizer and training operations; Running the computational graph; Classification with DNNs; Exponential linear unit activation function; Classification with DNNs; Elements of the DNN model; Building the DNN; Reading the data; Producing the objects for modeling; Training strategy; Input pipeline for DNN; Defining the architecture; Placeholders for inputs and labels; Building the neural network; The loss function; Evaluation nodes; Optimizer and the training operation; Run the computational graph.

Evaluating the model with a set threshold.

In this book, you will find a range of methods to improve the performance of almost any predictive model, from ensemble methods to dimensionality reduction and cross-validation. You will learn the tools to produce advanced predictive models. In addition, you will dive into the exiting field of Deep Learning using TensorFlow.

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