13 Best New Supervised Learning Books To Read In 2020

You'll review sequence predictions, saving, serving, deploying, and standardized datasets, and then deploy these models to production. All the code presented in the book will be available in the form of executable scripts at Github which allows you to try out the examples and extend them in interesting waysWhat You'll LearnReview the new features of TensorFlow 2.0Use TensorFlow 2.0 to build machine learning and deep learning modelsPerform sequence predictions using TensorFlow 2.0Deploy TensorFlow 2.0 models with practical examplesWho This Book Is ForData scientists, machine and deep learning engineers

Data lakes currently generated by worldwide industrialized business activities are projected to reach 35 zettabytes (ZB) as the Fourth Industrial Revolution produces an exponential increase of volume, velocity, variety, variability, veracity, visualization, and value. Industrialization of ML evolves from AI and studying pattern recognition against the increasingly unstructured resource stored in data lakesIndustrial Machine Learning supplies advanced, yet practical examples in different industries, including finance, public safety, health care, transportation, manufactory, supply chain, 3D printing, education, research, and data science. The book covers: supervised learning, unsupervised learning, reinforcement learning, evolutionary computing principles, soft robotics disruptors, and hard robotics disruptorsWhat You Will LearnGenerate and identify transformational disruptors of artificial intelligence (AI)Understand the field of machine learning (ML) and apply it to handle big data and process the data lakes in your environmentHone the skills required to handle the future of data engineering and data scienceWho This Book Is ForIntermediate to expert level professionals in the fields of data science, data engineering, machine learning, and data management

The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to the field and it is well suited for practitioners already familiar with the basics of optimization

The author includes advanced descriptions about tensor decomposition including Tucker decomposition using high order singular value decomposition as well as higher order orthogonal iteration, and train tenor decomposition. The author concludes by showing unsupervised methods and their application to a wide range of topicsAllows readers to analyze data sets with small samples and many features;Provides a fast algorithm, based upon linear algebra, to analyze big data;Includes several applications to multi-view data analyses, with a focus on bioinformatics

Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018)Includes new advances in clustering and classification using semi-supervised and unsupervised learning;Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning;Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning

Mining information from data enrols classification, clustering and other such methodologies as its subsets. The thesis presents a classical descriptive procedure to compare various classification schemes under single roof and draw analysis over the best scorer in terms of accuracy to draw predictions of Bank Marketing success rate while targeting potential customers. Various data sets can be filtered by the approached schemes to make decision during binary and multi valued classification. However the classification model ranks one over other to determine the best fit choice.

Select the best algorithm using the model evaluation frameworkBook DescriptionSupervised Learning with R covers the complete process of developing applications with supervised machine learning algorithms that cater to your business needs. Your learning curve starts with developing your analytical thinking towards creating a problem statement using business inputs or domain research. You will learn many evaluation metrics that compare various algorithms, and you can then use these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine tune your set of optimal parameters. To avoid overfitting your model, you will also be shown how to add various regularization termsWhen you complete the book, you will find yourself an expert at modelling a supervised machine learning algorithm that precisely fulfills your business needWhat you will learnDevelop analytical thinking to precisely identify a business problemWrangle data with dplyr, tidyr, and reshape2Visualize data with ggplot2Validate your supervised machine learning model using k-foldOptimize hyperparameters with grid and random search and bayesian optimizationDeploy your model on AWS Lambda with plumber, an R packageImprove the model performance with feature selection and dimensionality reductionUse a research paper with a real-world problem, frame a problem statement, and replicate its results with a supervised machine learning model Who This Book Is ForThis book is specially designed for novice and intermediate data analysts, data scientists, and data engineers who want to explore various methods of supervised machine learning and its various use cases. Some background in statistics, probability, calculus, linear algebra, and programming will help you thoroughly understand and follow the content of this book

Applied Machine Learning shows, step by step, how to conceptualize problems, accurately represent data, select and tune algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a non-rigorous mathematical style, the book covers a broad array of machine learning topics with special emphasis on methods that have been profitably employedCoverage includes:•Supervised learning•Statistical learning•Learning with support vector machines (SVM)•Learning with neural networks (NN)•Fuzzy inference systems•Data clustering•Data transformations•Decision tree learning•Business intelligence•Data mining•And much more

Learn all about deployment strategies and take your ML application from prototype to production ready Book Description Machine learning is an essential part of today's data-driven world and is extensively used across industries, including financial forecasting, robotics, and web technology. This book will teach you how to efficiently develop machine learning applications in Go The book starts with an introduction to machine learning and its development process, explaining the types of problems that it aims to solve and the solutions it offers. It then covers setting up a frictionless Go development environment, including running Go interactively with Jupyter notebooks. Finally, common data processing techniques are introduced The book then teaches the reader about supervised and unsupervised learning techniques through worked examples that include the implementation of evaluation metrics. These worked examples make use of the prominent open-source libraries GoML and Gonum The book also teaches readers how to load a pre-trained model and use it to make predictions. It then moves on to the operational side of running machine learning applications: deployment, Continuous Integration, and helpful advice for effective logging and monitoring At the end of the book, readers will learn how to set up a machine learning project for success, formulating realistic success criteria and accurately translating business requirements into technical ones What you will learn Understand the types of problem that machine learning solves, and the various approaches Import, pre-process, and explore data with Go to make it ready for machine learning algorithms Visualize data with gonum/plot and Gophernotes Diagnose common machine learning problems, such as overfitting and underfitting Implement supervised and unsupervised learning algorithms using Go libraries Build a simple web service around a model and use it to make predictions Who this book is for This book is for developers and data scientists with at least beginner-level knowledge of Go, and a vague idea of what types of problem Machine Learning aims to tackle. No advanced knowledge of Go (and no theoretical understanding of the math that underpins Machine Learning) is requiredTable of Contents Introducing Machine Leaning with Go Setting Up the Development Environment Supervised Learning Unsupervised Learning Using Pretrained Models Deploying Machine Learning Applications Conclusion - Successful ML Projects

Explore some of the most popular scientific and mathematical libraries in the Python language Book Description Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine “learns” under the hood This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning By the end of this book, you'll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems What you will learn Crack how a machine learns a concept and generalize its understanding to new data Uncover the fundamental differences between parametric and non-parametric models Implement and grok several well-known supervised learning algorithms from scratch Work with models in domains such as ecommerce and marketing Expand your expertise and use various algorithms such as regression, decision trees, and clustering Build your own models capable of making predictions Delve into the most popular approaches in deep learning such as transfer learning and neural networks Who this book is for This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming―and some fundamental knowledge of supervised learning―are expectedTable of Contents First step towards supervised learning Implementing parametric models Working with non-parametric models Advanced topics in supervised machine learning

The aim of supervised machine learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models.•Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or benign. Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. This book develops Supervised Machine Learning Techniques for Classification.•Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.

Author Ankur Patel—an applied machine-learning researcher and data scientist with expertise in financial markets—provides the concepts, intuition, and tools necessary for you to apply this technology to problems you tackle every day. Through the course of this book, you’ll learn how to build production-ready systems with PythonChapters for this Early Release edition will be available as they are completed, including the first five chapters in this initial release:Examine the difference between supervised and unsupervised learning, and the relative strengths and weaknesses of eachSet up and manage a machine learning project end-to-end—everything from data acquisition to building a model and implementing a solution in productionExplore dimensionality reduction algorithms that learn the underlying structure of a dataset’s most salient informationBuild a credit card fraud detection system using dimensionality reduction methods