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Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark
Next-Generation Big Data: A Practical Guide to Apache Kudu, Impala, and Spark

Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies.

Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book...

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...
Testing and Tuning Market Trading Systems: Algorithms in C++
Testing and Tuning Market Trading Systems: Algorithms in C++

Build, test, and tune financial, insurance or other market trading systems using C++ algorithms and statistics. You’ve had an idea and have done some preliminary experiments, and it looks promising. Where do you go from here?  Well, this book discusses and dissects this case study approach.  

...

IoT, AI, and Blockchain for .NET: Building a Next-Generation Application from the Ground Up
IoT, AI, and Blockchain for .NET: Building a Next-Generation Application from the Ground Up
Create applications using Industry 4.0. Discover how artificial intelligence (AI) and machine learning (ML) capabilities can be enhanced using the Internet of things (IoT) and secured using Blockchain, so your latest app can be not just smarter but also more connected and more secure than ever before. This book covers the latest...
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...
Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning, 2nd Edition
Data Science Algorithms in a Week: Top 7 algorithms for scientific computing, data analysis, and machine learning, 2nd Edition

Build a strong foundation of machine learning algorithms in 7 days

Key Features

  • Use Python and its wide array of machine learning libraries to build predictive models
  • Learn the basics of the 7 most widely used machine learning algorithms within a week
  • Know...
TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python, 2nd Edition
TensorFlow Machine Learning Cookbook: Over 60 recipes to build intelligent machine learning systems with the power of Python, 2nd Edition

Skip the theory and get the most out of Tensorflow to build production-ready machine learning models

Key Features

  • Exploit the features of Tensorflow to build and deploy machine learning models
  • Train neural networks to tackle real-world problems in Computer Vision and...
Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
Hands-On Ensemble Learning with R: A beginner's guide to combining the power of machine learning algorithms using ensemble techniques

Explore powerful R packages to create predictive models using ensemble methods

Key Features

  • Implement machine learning algorithms to build ensemble-efficient models
  • Explore powerful R packages to create predictive models using ensemble methods
  • Learn to...
Machine Learning in Java: Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition
Machine Learning in Java: Helpful techniques to design, build, and deploy powerful machine learning applications in Java, 2nd Edition

Leverage the power of Java and its associated machine learning libraries to build powerful predictive models

Key Features

  • Solve predictive modeling problems using the most popular machine learning Java libraries
  • Explore data processing, machine learning, and NLP concepts...
Python: Beginner's Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python
Python: Beginner's Guide to Artificial Intelligence: Build applications to intelligently interact with the world around you using Python

Develop real-world applications powered by the latest advances in intelligent systems

Key Features

  • Gain real-world contextualization using deep learning problems concerning research and application
  • Get to know the best practices to improve and optimize your machine...
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...
Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python
Mastering Predictive Analytics with scikit-learn and TensorFlow: Implement machine learning techniques to build advanced predictive models using Python

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...
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