ISBN
9781782162292
Over 70 hands-on recipes to quickly and effectively integrate Lucene into your search application :
This book is for software developers who are new to Lucene and who want to explore the more advanced topics to build a search engine. Knowledge of Java is necessary to follow the code samples. You will learn core concepts, best practices, and also advanced features, in order to build an effective search application.
Author/Authors: Ng, Edwood | Mohan, Vineeth
Pages: 220 | Published Date: 42181
Category: Data
ISBN
9781783550944
More than 100 recipes to develop Business Intelligence solutions using Analysis Services :
This book offers uses practical applications using recipes with step-by-step instructions and useful information to help you master how to produce professional architectural visualizations in Lumion. The cookbook approach means you need to think and explore how a particular feature can be applied in your project and perform the intended task. This book is written to be accessible to all Lumion users and is a useful guide to follow when becoming familiar with this cutting-edge real-time technology.This practical guide is designed for all levels of Lumion users who know how to model buildings in 3D and a basic understanding of Lumion, who want to enhance their skills to the next level.
Author/Authors: Cardoso, Ciro
Pages: 258 | Published Date: 41810
Category: Business & Other
ISBN
9781785884511
Build strong foundation for entering the world of Machine Learning and data science with the help of this comprehensive guide :
This book is for IT professionals who want to enter the field of data science and are very new to Machine Learning. Familiarity with languages such as R and Python will be invaluable here.
Author/Authors: Bonaccorso, Giuseppe
Pages: 360 | Published Date: 42940
Category: Data
ISBN
9781789345483
Popular algorithms for data science and machine learning, 2nd Edition :
Machine learning explores the study and construction of algorithms that can learn from and make predictions on data. This book will act as an entry point for anyone who wants to make a career in Machine Learning. It covers algorithms like Linear regression, Logistic Regression, SVM, Naïve Bayes, K-Means, Random Forest, and Feature engineering.
Author/Authors: Bonaccorso, Giuseppe
Pages: 522 | Published Date: 43342
Category: Data
ISBN
9781786466969
Your one-stop guide to becoming a Machine Learning expert. :
This book will appeal to any developer who wants to know what Machine Learning is and is keen to use Machine Learning to make their day-to-day apps fast, high performing, and accurate. Any developer who wants to enter the field of Machine Learning can effectively use this book as an entry point.
Author/Authors: Bonnin, Rodolfo
Pages: 270 | Published Date: 43034
Category: Data
ISBN
9781789532524
Build smart AI applications using neural network methodologies across the healthcare vertical market :
Machine Learning in the healthcare domain is booming because of its abilities to provide accurate and stabilized techniques. This book is packed with new methodologies to create efficient solutions for healthcare analytics. We will build five end-to-end projects to evaluate the efficiency of AI apps to carry out simple-to-complex healthcare analytics tasks.
Author/Authors: Solutions, Eduonix Learning
Pages: 134 | Published Date: 43403
Category: Data
ISBN
9781783980291
Expand your OpenCV knowledge and master key concepts of machine learning using this practical, hands-on guide. :
This book targets Python programmers who are already familiar with OpenCV; this book will give you the tools and understanding required to build your own Machine Learning systems, tailored to practical real-world tasks.
Author/Authors: Beyeler, Michael
Pages: 382 | Published Date: 42930
Category: Data
ISBN
9781785888724
Explore the web and make smarter predictions using Python :
The book is aimed at upcoming and new data scientists who have little experience of machine learning or users who are interested in and are working on developing smart (predictive) web applications. Knowledge of Django would be beneficial. The reader is expected to have a background in Python programming and good knowledge of statistics.
Author/Authors: Isoni, Andrea
Pages: 298 | Published Date: 42580
Category: Data
ISBN
9781789801767
Use Python and scikit-learn to get up and running with the hottest developments in machine learning :
<p><b>With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level</b></p> <h4>Key Features</h4> <ul><li>Explore scikit-learn uniform API and its application into any type of model </li> <li>Understand the difference between supervised and unsupervised models </li> <li>Learn the usage of machine learning through real-world examples </li> </ul> <h4>Book Description</h4> <p>As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem. </p> <p>The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters. </p> <p>By the end of this book, you will have gain all the skills required to start programming machine learning algorithms. </p> <h4>What you will learn</h4> <ul><li>Understand the importance of data representation </li> <li>Gain insights into the differences between supervised and unsupervised models </li> <li>Explore data using the Matplotlib library </li> <li>Study popular algorithms, such as k-means, Mean-Shift, and DBSCAN </li> <li>Measure model performance through different metrics </li> <li>Implement a confusion matrix using scikit-learn </li> <li>Study popular algorithms, such as Na?ve-Bayes, Decision Tree, and SVM </li> <li>Perform error analysis to improve the performance of the model </li> <li>Learn to build a comprehensive machine learning program</li></ul> <h4>Who this book is for</h4> <p>Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikit-learn or machine learning algorithms. </p>
Author/Authors: Saleh, Hyatt
Pages: 240 | Published Date: 43433
Category: Data
ISBN
9781788473897
Helpful techniques to design, build, and deploy powerful machine learning applications in Java :
<p><b>Leverage the power of Java and its associated machine learning libraries to build powerful predictive models</b></p> <h4>Key Features</h4> <ul><li>Solve predictive modeling problems using the most popular machine learning Java libraries </li> <li>Explore data processing, machine learning, and NLP concepts using JavaML, WEKA, MALLET libraries </li> <li>Practical examples, tips, and tricks to help you understand applied machine learning in Java</li></ul> <h4>Book Description</h4> <p>As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. </p> <p>Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11. </p> <p>Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level. </p> <p>By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.</p> <h4>What you will learn</h4> <ul><li>Discover key Java machine learning libraries </li> <li>Implement concepts such as classification, regression, and clustering </li> <li>Develop a customer retention strategy by predicting likely churn candidates </li> <li>Build a scalable recommendation engine with Apache Mahout </li> <li>Apply machine learning to fraud, anomaly, and outlier detection </li> <li>Experiment with deep learning concepts and algorithms </li> <li>Write your own activity recognition model for eHealth applications</li></ul> <h4>Who this book is for</h4> <p>If you want to learn how to use Java's machine learning libraries to gain insight from your data, this book is for you. It will get you up and running quickly and provide you with the skills you need to successfully create, customize, and deploy machine learning applications with ease. You should be familiar with Java programming and some basic data mining concepts to make the most of this book, but no prior experience with machine learning is required.</p>
Author/Authors: Bhatia, AshishSingh| Kaluza, Bostjan |
Pages: 300 | Published Date: 43432
Category: Data
