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Interpreting and Visualizing Regression Models Using Stata
Interpreting and Visualizing Regression Models Using Stata

Interpreting and Visualizing Regression Models Using Stata, Second Edition provides clear and simple examples illustrating how to interpret and visualize a wide variety of regression models. Including over 200 figures, the book illustrates linear models with continuous predictors (modeled linearly, using polynomials,...

Advanced Data Mining Techniques
Advanced Data Mining Techniques
This book covers the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. The book is organized in three parts. Part I introduces concepts. Part II describes and demonstrates basic data mining algorithms. It also contains chapters on a...
Fixed Effects Regression Methods for Longitudinal Data Using SAS
Fixed Effects Regression Methods for Longitudinal Data Using SAS
Every empirical researcher knows that randomized experiments have major advantages over observational studies in making causal inferences. Randomization of subjects to different treatment conditions ensures that the treatment groups, on average, are identical with respect to all possible characteristics of the subjects, regardless of whether those...
Getting Started With SAS Enterprise Miner 5.2
Getting Started With SAS Enterprise Miner 5.2
SAS defines data mining as the process of uncovering hidden patterns in large amounts of data. Many industries use data mining to address business problems and opportunities such as fraud detection, risk and affinity analyses, database marketing, householding, customer churn, bankruptcy prediction, and...
Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
Assessing and Improving Prediction and Classification: Theory and Algorithms in C++
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting.  This book presents many important techniques for building...
Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R
Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so...
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud

Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book?Amazon, Microsoft, Google, and PythonAnywhere.

You will work through a series of common Python data science problems...

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...

A Statistical Approach to Neural Networks for Pattern Recognition (Wiley Series in Computational Statistics)
A Statistical Approach to Neural Networks for Pattern Recognition (Wiley Series in Computational Statistics)
"The book provides an excellent introduction to neutral networks from a statistical perspective." (International Statistical Review, 2008)

"Successful connects logistic regression and linear discriminant analysis, thus making it critical reference and self-study guide for students and professionals alike in the...

Data Science from Scratch: First Principles with Python
Data Science from Scratch: First Principles with Python

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from...

Apache Mahout Cookbook
Apache Mahout Cookbook

A fast, fresh, developer-oriented dive into the world of Mahout

Overview

  • Learn how to set up a Mahout development environment
  • Start testing Mahout in a standalone Hadoop cluster
  • Learn to find stock market direction using logistic regression
  • Over 35 recipes with...
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems
Build machine learning models, natural language processing applications, and recommender systems with PySpark to solve various business challenges. This book starts with the fundamentals of Spark and its evolution and then covers the entire spectrum of traditional machine learning algorithms along...
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