Alan Fontaine - Mastering Predictive Analytics with scikit-learn and TensorFlow

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Alan Fontaine is a data scientist with more than 12 years of experience in analytical roles. He has been a consultant for many projects in fields such as: business, education, medicine, mass media, among others. He is a big Python fan and has been using it routinely for five years for analyzing data, building models, producing reports, making predictions, and building interactive applications that transform data into intelligence.

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Python is a programming language that provides various features in the field of data science. In this book, we will be touching upon two Python libraries, scikit-learn and TensorFlow. We will learn about the various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems.

This book starts with studying ensemble methods and their features. We will look at how scikit-learn provides the right tools to choose hyperparameters for models. From there, we will get down to the nitty-gritty of predictive analytics and explore its various features and characteristics. We will be introduced to artificial neural networks, TensorFlow, and the core concepts used to build neural networks.
In the final section, we will consider factors such as computational power, improved methods, and software enhancements for efficient predictive analytics. You will become well versed in using DNNs to solve common challenges.

This book is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to go from basic predictive models to building advanced models and producing better predictions. Knowledge of Python and familiarity with predictive analytics concepts are assumed.

, Ensemble Methods for Regression and Classification , covers the application of ensemble methods or algorithms to produce accurate predictions of models. We will go through the application of ensemble methods for regression and classification problems.

, Cross-validation and Parameter Tuning , explores various techniques to combine and build better models. We will learn different methods of cross-validation, including holdout cross-validation and k-fold cross-validation. We will also discuss what hyperparameter tuning is.

, Working with Features , explores feature selection methods, dimensionality reduction, PCA, and feature engineering. We will also study methods to improve models with feature engineering.

, Introduction to Artificial Neural Networks and TensorFlow , is an introduction to ANNs and TensorFlow. We will explore the various elements in the network and their functions. We will also learn the basic concepts of TensorFlow in it.

, Predictive Analytics with TensorFlow and Deep Neural Networks, explores predictive analytics with the help of TensorFlow and deep learning. We will study the MNIST dataset and classification of models using this dataset. We will learn about DNNs, their functions, and the application of DNNs to the MNIST dataset.

This book presents some of the most advanced predictive analytics tools, models, and techniques. The main goal is to show the viewer how to improve the performance of predictive models, firstly, by showing how to build more complex models, and secondly by showing how to use related techniques that dramatically improve the quality of predictive models.