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Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition
Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithms

Key Features

  • Explore statistics and complex mathematics for data-intensive applications
  • Discover new developments in EM algorithm, PCA, and bayesian...
Artificial Intelligence and Machine Learning Fundamentals: Develop real-world applications powered by the latest AI advances
Artificial Intelligence and Machine Learning Fundamentals: Develop real-world applications powered by the latest AI advances

Create AI applications in Python and lay the foundations for your career in data science

Key Features

  • Practical examples that explain key machine learning algorithms
  • Explore neural networks in detail with interesting examples
  • Master core AI concepts with...
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...

Applied Econometrics with R (Use R!)
Applied Econometrics with R (Use R!)

Here is the first book on applied econometrics using the R system for statistical computing and graphics. It presents hands-on examples for a wide range of models, from classical linear regression models for cross-section to recent semiparametric extensions.

R is a language and environment for data analysis and
...
Mathematical Statistics with Resampling and R
Mathematical Statistics with Resampling and R
This book bridges the latest software applications with the benefits of modern resampling techniques

Resampling helps students understand the meaning of sampling distributions, sampling variability, P-values, hypothesis tests, and confidence intervals. This groundbreaking book shows how to apply modern resampling techniques...

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Graphics in this book are printed in black and white.

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data....

Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques
Artificial Intelligence for Big Data: Complete guide to automating Big Data solutions using Artificial Intelligence techniques

Build next-generation artificial intelligence systems with Java

Key Features

  • Implement AI techniques to build smart applications using Deeplearning4j
  • Perform big data analytics to derive quality insights using Spark MLlib
  • Create self-learning systems using...
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning
TensorFlow for Deep Learning: From Linear Regression to Reinforcement Learning

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...

Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization
Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization
Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and...
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most...

Machine Learning in Action
Machine Learning in Action

After college I went to work for Intel in California and mainland China. Originally my plan was to go back to grad school after two years, but time flies when you are having fun, and two years turned into six. I realized I had to go back at that point, and I didn’t want to do night school or online learning, I wanted to sit on...

Deep Learning Quick Reference: Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras
Deep Learning Quick Reference: Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras

Dive deeper into neural networks and get your models trained, optimized with this quick reference guide

Key Features

  • A quick reference to all important deep learning concepts and their implementations
  • Essential tips, tricks, and hacks to train a variety of deep learning...
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