Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python

Buy

Learn advanced techniques to improve the performance and quality of your predictive models

Key Features

  • Use ensemble methods to improve the performance of predictive analytics models
  • Implement feature selection, dimensionality reduction, and cross-validation techniques
  • Develop neural network models and master the basics of deep learning

Book Description

Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers 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 ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics.

By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis.

What you will learn

  • Use ensemble algorithms to obtain accurate predictions
  • Apply dimensionality reduction techniques to combine features and build better models
  • Choose the optimal hyperparameters using cross-validation
  • Implement different techniques to solve current challenges in the predictive analytics domain
  • Understand various elements of deep neural network (DNN) models
  • Implement neural networks to solve both classification and regression problems

Who this book is for

Mastering Predictive Analytics with scikit-learn and TensorFlow 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 progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.

Table of Contents

  1. Ensemble Methods for Regression and Classification
  2. Cross-validation and Parameter Tuning
  3. Working with Features
  4. Introduction to Artificial Neural Networks and TensorFlow
  5. Predictive Analytics with TensorFlow and Deep Neural Networks
(HTML tags aren't allowed.)

Kivy Cookbook
Kivy Cookbook

Enhance your skills in developing multi-touch applications with Kivy

About This Book

  • Create most diverse apps and learn how to distribute them with the help of the Kivy framework
  • Explore Kivy API to develop user interfaces and control multi-touch events
  • Step-by-step recipes...
Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles
Hands-On Neural Networks with Keras: Design and create neural networks using deep learning and artificial intelligence principles

Your one-stop guide to learning and implementing artificial neural networks with Keras effectively

Key Features

  • Design and create neural network architectures on different domains using Keras
  • Integrate neural network models in your applications using this highly practical...
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms

With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research, one that’s paving the way for modern machine learning. In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field.

...

Kivy: Interactive Applications in Python - Second Edition
Kivy: Interactive Applications in Python - Second Edition

Create responsive cross-platform UI/UX applications and games in Python using the open source Kivy library

About This Book

  • Utilize the power of Kivy to develop applications that run on all the major platforms
  • Build user interfaces (UI) and control multi-touch events to improve the user...
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and...

Kivy Blueprints
Kivy Blueprints

Build your very own app-store-ready, multi-touch games and applications with Kivy!

About This Book

  • Learn how to create simple to complex functional apps quickly and easily with the Kivy framework
  • Bend Kivy according to your needs by customizing, overriding, and bypassing the built-in...
©2020 LearnIT (support@pdfchm.net) - Privacy Policy