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.
