Configurability Scala supports dependency injection using a combination of abstract variables, self-referenced composition, and stackable traits. Here are some examples of these styles and an explanation of their meaning. The best analogy I can find to describe the need, desire, and urgency to extract knowledge from large datasets is the process of extracting a precious metal from a mine, and in some cases, extracting blood from a stone. The next module, Mastering Scala Machine Learning, is the final step in this course. The approach of each chapter is such that it allows you to understand key concepts easily. A review of the Akka framework and Apache Spark clusters concludes the tutorial. Those tasks consist of high-order Scala methods such as flatMap, map, fold, reduce, collect, join, or filter applied to a large collection of observations.
Modeling A model by any other name Model versus design Selecting a model's features Extracting features Designing a workflow The computational framework The pipe operator Monadic data transformation Dependency injection Workflow modules The workflow factory Examples of workflow components The preprocessing module The clustering module Assessing a model Validation Key metrics Implementation K-fold cross-validation Bias-variance decomposition Overfitting Summary 3. I'd also like to thank Packt Publishing and its staff for the opportunity to contribute to this book. However, Packt Publishing cannot guarantee the accuracy of this information. The discovery of information through data clustering and classification is becoming a key differentiator for competitive organizations. Getting Started It is critical for any computer scientist to understand the different classes of machine learning algorithms and be able to select the ones that are relevant to the domain of their expertise and dataset.
Naïve Bayes Classifiers Probabilistic graphical models Naïve Bayes classifiers Introducing the multinomial Naïve Bayes Formalism The frequentist perspective The predictive model The zero-frequency problem Implementation Software design Training Classification Labeling Results Multivariate Bernoulli classification Model Implementation Naïve Bayes and text mining Basics of information retrieval Implementation Extraction of terms Scoring of terms Testing Retrieving textual information Evaluation Pros and cons Summary 6. Why is Scala the ideal programming language to implement machine learning algorithms? The first module introduces you to Scala libraries to ingest, store, manipulate, process, and visualize data. Regression Regression is a classification technique that is particularly suitable for a continuous model. Following this, you'll move on to unsupervised learning techniques such as clustering and dimension reduction, followed by probabilistic graphical models such as Naïve Bayes, hidden Markov models and Monte Carlo inference. You can imagine that fighting the spread of a new virus requires optimizing a process that may evolve over time as more symptoms and cases are uncovered. Hard to read and probably not what you're looking for I received a free ebook copy for review I have a hard time deciding who this book is for: it won't teach you Scala and it won't teach you machine learning.
Contents: Cover; Copyright; Credits; About the Author; About the Reviewers; www. He is focused on designing and implementing highly efficient, concurrent, and scalable systems as well as machine learning solutions. Machine learning techniques improve the chances that the optimization method converges toward a solution intelligent search. Mathematical notation for the curious Each chapter contains a small section dedicated to the formulation of the algorithms for those interested in the mathematical concepts behind the science and art of machine learning. However, the information contained in this book is sold without warranty, either express or implied. Clustering The purpose of data clustering is to partition a collection of data into a number of clusters or data segments.
Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. Short test applications using financial data illustrate the large variety of predictive, regression, and classification models. Among the capabilities of the language, the following features are deemed essential to machine learning and statistical analysis. You will be provided with in-text citation of papers, conference, books, and instructional videos throughout the book. Optimization Some global optimization problems are intractable using traditional linear and non-linear optimization methods. Machine learning applications are everywhere, from self-driving cars, engineering design, logistics, manufacturing, and trading strategies, to detection of genetic anomalies. Errata Although we have taken every care to ensure the accuracy of our content, mistakes do happen.
It might serve as a reference book, but given how many errors there are in code samples and how little attention is payed to giving the reader a decent understanding of each algorithm, I think you should wait for the second edition. Stochastic Gradient Descent The stochastic gradient descent optimizer is a randomized approximation of the batched gradient descent algorithm used to minimize a continuous differentiable objective function. Table of Contents Scala for Machine Learning Credits About the Author About the Reviewers www. He believes that choosing the right programming language, tool, and framework for the problem at hand is more important than trying to fit all problems in one technology. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book. Chapter 4, Unsupervised Learning, focuses on the implementation of some of the most widely used clustering techniques, such as K-means, the expectation-maximization, and the principal component analysis as a dimension reduction method. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits.
His interests include real-time analytics, modeling, and the development of nonlinear models. After this, you will also learn how to build interactive visualizations with web frameworks. Selecting the model features and the appropriate machine learning algorithm s. Regression And Regularization Chapter 10. The book begins with an introduction to the functional capabilities of the language that are critical to the creation of machine learning algorithms such as dependency injection and implicits.
The text is extremely dry, few examples other than code , mathematical formulae are dropped in text with little or no explanations. Downloading the example code for this book. The learning rates are zipped with the old weights line 15 to update the weights newWeights as a new dense vector line 21. He has 25 years of experience in software engineering and building large-scale applications in C++, Java, and Scala, and has held several managerial positions. Parallelism In Scala And Akka Chapter 17. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. Nicolas Style and approach A complete package with all the information necessary to start building useful data engineering and data science solutions straight away.
With Safari, you learn the way you learn best. In terms of implementation, Actors are the core elements that make Scala scalable. The next step consists of converting the existing old weights into a Breeze dense vector brzWeights line 14. Scala for Machine Learning, 2nd Edition by Patrick R. Conventions In this book, you will find a number of text styles that distinguish between different kinds of information. The book is your one stop guide that introduces you to the functional capabilities of the Scala programming language that are critical to the creation of machine learning algorithms such as dependency injection and implicits.