Data Science Quick Reference Manual Advanced Machine Learning And Deployment

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Data Science Quick Reference Manual - Advanced Machine Learning and Deployment

Author : Mario A. B. Capurso
Publisher : Mario Capurso
ISBN 13 :
Total Pages : 278 pages
Book Rating : 4./5 ( download)

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Book Synopsis Data Science Quick Reference Manual - Advanced Machine Learning and Deployment by : Mario A. B. Capurso

Download or read book Data Science Quick Reference Manual - Advanced Machine Learning and Deployment written by Mario A. B. Capurso and published by Mario Capurso. This book was released on 2023-09-08 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.

Machine Learning Quick Reference

Author : Rahul Kumar
Publisher : Packt Publishing Ltd
ISBN 13 : 1788831616
Total Pages : 283 pages
Book Rating : 4.7/5 (888 download)

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Book Synopsis Machine Learning Quick Reference by : Rahul Kumar

Download or read book Machine Learning Quick Reference written by Rahul Kumar and published by Packt Publishing Ltd. This book was released on 2019-01-31 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your hands-on reference guide to developing, training, and optimizing your machine learning models Key FeaturesYour guide to learning efficient machine learning processes from scratchExplore expert techniques and hacks for a variety of machine learning conceptsWrite effective code in R, Python, Scala, and Spark to solve all your machine learning problemsBook Description Machine learning makes it possible to learn about the unknowns and gain hidden insights into your datasets by mastering many tools and techniques. This book guides you to do just that in a very compact manner. After giving a quick overview of what machine learning is all about, Machine Learning Quick Reference jumps right into its core algorithms and demonstrates how they can be applied to real-world scenarios. From model evaluation to optimizing their performance, this book will introduce you to the best practices in machine learning. Furthermore, you will also look at the more advanced aspects such as training neural networks and work with different kinds of data, such as text, time-series, and sequential data. Advanced methods and techniques such as causal inference, deep Gaussian processes, and more are also covered. By the end of this book, you will be able to train fast, accurate machine learning models at your fingertips, which you can easily use as a point of reference. What you will learnGet a quick rundown of model selection, statistical modeling, and cross-validationChoose the best machine learning algorithm to solve your problemExplore kernel learning, neural networks, and time-series analysisTrain deep learning models and optimize them for maximum performanceBriefly cover Bayesian techniques and sentiment analysis in your NLP solutionImplement probabilistic graphical models and causal inferencesMeasure and optimize the performance of your machine learning modelsWho this book is for If you’re a machine learning practitioner, data scientist, machine learning developer, or engineer, this book will serve as a reference point in building machine learning solutions. You will also find this book useful if you’re an intermediate machine learning developer or data scientist looking for a quick, handy reference to all the concepts of machine learning. You’ll need some exposure to machine learning to get the best out of this book.

Data Science Quick Reference Manual – Deep Learning

Author : Mario A. B. Capurso
Publisher : Mario Capurso
ISBN 13 :
Total Pages : 261 pages
Book Rating : 4./5 ( download)

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Book Synopsis Data Science Quick Reference Manual – Deep Learning by : Mario A. B. Capurso

Download or read book Data Science Quick Reference Manual – Deep Learning written by Mario A. B. Capurso and published by Mario Capurso. This book was released on 2023-09-04 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Deep Learning techniques are described considering the architectures of the Perceptron, Neocognitron, the neuron with Backpropagation and the activation functions, the Feed Forward Networks, the Autoencoders, the recurrent networks and the LSTM and GRU, the Transformer Neural Networks, the Convolutional Neural Networks and Generative Adversarial Networks and analyzed the building blocks. Regularization techniques (Dropout, Early stopping and others), visual design and simulation techniques and tools, the most used algorithms and the best known architectures (LeNet, VGGnet, ResNet, Inception and others) are considered, closing with a set of practical tips and tricks. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.

The Data Science Design Manual

Author : Steven S. Skiena
Publisher : Springer
ISBN 13 : 3319554441
Total Pages : 445 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis The Data Science Design Manual by : Steven S. Skiena

Download or read book The Data Science Design Manual written by Steven S. Skiena and published by Springer. This book was released on 2017-07-01 with total page 445 pages. Available in PDF, EPUB and Kindle. Book excerpt: This engaging and clearly written textbook/reference provides a must-have introduction to the rapidly emerging interdisciplinary field of data science. It focuses on the principles fundamental to becoming a good data scientist and the key skills needed to build systems for collecting, analyzing, and interpreting data. The Data Science Design Manual is a source of practical insights that highlights what really matters in analyzing data, and provides an intuitive understanding of how these core concepts can be used. The book does not emphasize any particular programming language or suite of data-analysis tools, focusing instead on high-level discussion of important design principles. This easy-to-read text ideally serves the needs of undergraduate and early graduate students embarking on an “Introduction to Data Science” course. It reveals how this discipline sits at the intersection of statistics, computer science, and machine learning, with a distinct heft and character of its own. Practitioners in these and related fields will find this book perfect for self-study as well. Additional learning tools: Contains “War Stories,” offering perspectives on how data science applies in the real world Includes “Homework Problems,” providing a wide range of exercises and projects for self-study Provides a complete set of lecture slides and online video lectures at www.data-manual.com Provides “Take-Home Lessons,” emphasizing the big-picture concepts to learn from each chapter Recommends exciting “Kaggle Challenges” from the online platform Kaggle Highlights “False Starts,” revealing the subtle reasons why certain approaches fail Offers examples taken from the data science television show “The Quant Shop” (www.quant-shop.com)

Learn Amazon SageMaker

Author : Julien Simon
Publisher : Packt Publishing Ltd
ISBN 13 : 1800203594
Total Pages : 490 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Learn Amazon SageMaker by : Julien Simon

Download or read book Learn Amazon SageMaker written by Julien Simon and published by Packt Publishing Ltd. This book was released on 2020-08-27 with total page 490 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor Key FeaturesBuild, train, and deploy machine learning models quickly using Amazon SageMakerAnalyze, detect, and receive alerts relating to various business problems using machine learning algorithms and techniquesImprove productivity by training and fine-tuning machine learning models in productionBook Description Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You’ll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you’ll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You’ll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you’ll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you’ll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation. What you will learnCreate and automate end-to-end machine learning workflows on Amazon Web Services (AWS)Become well-versed with data annotation and preparation techniquesUse AutoML features to build and train machine learning models with AutoPilotCreate models using built-in algorithms and frameworks and your own codeTrain computer vision and NLP models using real-world examplesCover training techniques for scaling, model optimization, model debugging, and cost optimizationAutomate deployment tasks in a variety of configurations using SDK and several automation toolsWho this book is for This book is for software engineers, machine learning developers, data scientists, and AWS users who are new to using Amazon SageMaker and want to build high-quality machine learning models without worrying about infrastructure. Knowledge of AWS basics is required to grasp the concepts covered in this book more effectively. Some understanding of machine learning concepts and the Python programming language will also be beneficial.

Deep Learning Quick Reference

Author : Michael Bernico
Publisher : Packt Publishing Ltd
ISBN 13 : 1788838912
Total Pages : 261 pages
Book Rating : 4.7/5 (888 download)

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Book Synopsis Deep Learning Quick Reference by : Michael Bernico

Download or read book Deep Learning Quick Reference written by Michael Bernico and published by Packt Publishing Ltd. This book was released on 2018-03-09 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 models such as CNNs, RNNs, LSTMs, and more Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training and fine-tuning your models in Keras and Tensorflow. Book Description Deep learning has become an essential necessity to enter the world of artificial intelligence. With this book deep learning techniques will become more accessible, practical, and relevant to practicing data scientists. It moves deep learning from academia to the real world through practical examples. You will learn how Tensor Board is used to monitor the training of deep neural networks and solve binary classification problems using deep learning. Readers will then learn to optimize hyperparameters in their deep learning models. The book then takes the readers through the practical implementation of training CNN's, RNN's, and LSTM's with word embeddings and seq2seq models from scratch. Later the book explores advanced topics such as Deep Q Network to solve an autonomous agent problem and how to use two adversarial networks to generate artificial images that appear real. For implementation purposes, we look at popular Python-based deep learning frameworks such as Keras and Tensorflow, Each chapter provides best practices and safe choices to help readers make the right decision while training deep neural networks. By the end of this book, you will be able to solve real-world problems quickly with deep neural networks. What you will learn Solve regression and classification challenges with TensorFlow and Keras Learn to use Tensor Board for monitoring neural networks and its training Optimize hyperparameters and safe choices/best practices Build CNN's, RNN's, and LSTM's and using word embedding from scratch Build and train seq2seq models for machine translation and chat applications. Understanding Deep Q networks and how to use one to solve an autonomous agent problem. Explore Deep Q Network and address autonomous agent challenges. Who this book is for If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.

The The Data Science Workshop

Author : Anthony So
Publisher : Packt Publishing Ltd
ISBN 13 : 1800569408
Total Pages : 823 pages
Book Rating : 4.8/5 (5 download)

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Book Synopsis The The Data Science Workshop by : Anthony So

Download or read book The The Data Science Workshop written by Anthony So and published by Packt Publishing Ltd. This book was released on 2020-08-28 with total page 823 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms Key FeaturesGain a full understanding of the model production and deployment processBuild your first machine learning model in just five minutes and get a hands-on machine learning experienceUnderstand how to deal with common challenges in data science projectsBook Description Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently. What you will learnExplore the key differences between supervised learning and unsupervised learningManipulate and analyze data using scikit-learn and pandas librariesUnderstand key concepts such as regression, classification, and clusteringDiscover advanced techniques to improve the accuracy of your modelUnderstand how to speed up the process of adding new featuresSimplify your machine learning workflow for productionWho this book is for This is one of the most useful data science books for aspiring data analysts, data scientists, database engineers, and business analysts. It is aimed at those who want to kick-start their careers in data science by quickly learning data science techniques without going through all the mathematics behind machine learning algorithms. Basic knowledge of the Python programming language will help you easily grasp the concepts explained in this book.

Machine Learning with Go Quick Start Guide

Author : Michael Bironneau
Publisher : Packt Publishing Ltd
ISBN 13 : 1838551654
Total Pages : 159 pages
Book Rating : 4.8/5 (385 download)

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Book Synopsis Machine Learning with Go Quick Start Guide by : Michael Bironneau

Download or read book Machine Learning with Go Quick Start Guide written by Michael Bironneau and published by Packt Publishing Ltd. This book was released on 2019-05-31 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: This quick start guide will bring the readers to a basic level of understanding when it comes to the Machine Learning (ML) development lifecycle, will introduce Go ML libraries and then will exemplify common ML methods such as Classification, Regression, and Clustering Key FeaturesYour handy guide to building machine learning workflows in Go for real-world scenariosBuild predictive models using the popular supervised and unsupervised machine learning techniquesLearn all about deployment strategies and take your ML application from prototype to production readyBook 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 learnUnderstand the types of problem that machine learning solves, and the various approachesImport, pre-process, and explore data with Go to make it ready for machine learning algorithmsVisualize data with gonum/plot and GophernotesDiagnose common machine learning problems, such as overfitting and underfittingImplement supervised and unsupervised learning algorithms using Go librariesBuild a simple web service around a model and use it to make predictionsWho 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 required.

Python: Advanced Guide to Artificial Intelligence

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1789951720
Total Pages : 748 pages
Book Rating : 4.7/5 (899 download)

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Book Synopsis Python: Advanced Guide to Artificial Intelligence by : Giuseppe Bonaccorso

Download or read book Python: Advanced Guide to Artificial Intelligence written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2018-12-21 with total page 748 pages. Available in PDF, EPUB and Kindle. Book excerpt: Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems Key FeaturesMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation Build deep learning models for object detection, image classification, similarity learning, and moreBuild, deploy, and scale end-to-end deep neural network models in a production environmentBook Description This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries. You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more. By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: Mastering Machine Learning Algorithms by Giuseppe BonaccorsoMastering TensorFlow 1.x by Armando FandangoDeep Learning for Computer Vision by Rajalingappaa ShanmugamaniWhat you will learnExplore how an ML model can be trained, optimized, and evaluatedWork with Autoencoders and Generative Adversarial NetworksExplore the most important Reinforcement Learning techniquesBuild end-to-end deep learning (CNN, RNN, and Autoencoders) modelsWho this book is for This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model. You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

Data Science with SQL Server Quick Start Guide

Author : Dejan Sarka
Publisher : Packt Publishing Ltd
ISBN 13 : 1789537134
Total Pages : 196 pages
Book Rating : 4.7/5 (895 download)

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Book Synopsis Data Science with SQL Server Quick Start Guide by : Dejan Sarka

Download or read book Data Science with SQL Server Quick Start Guide written by Dejan Sarka and published by Packt Publishing Ltd. This book was released on 2018-08-31 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Get unique insights from your data by combining the power of SQL Server, R and Python Key Features Use the features of SQL Server 2017 to implement the data science project life cycle Leverage the power of R and Python to design and develop efficient data models find unique insights from your data with powerful techniques for data preprocessing and analysis Book Description SQL Server only started to fully support data science with its two most recent editions. If you are a professional from both worlds, SQL Server and data science, and interested in using SQL Server and Machine Learning (ML) Services for your projects, then this is the ideal book for you. This book is the ideal introduction to data science with Microsoft SQL Server and In-Database ML Services. It covers all stages of a data science project, from businessand data understanding,through data overview, data preparation, modeling and using algorithms, model evaluation, and deployment. You will learn to use the engines and languages that come with SQL Server, including ML Services with R and Python languages and Transact-SQL. You will also learn how to choose which algorithm to use for which task, and learn the working of each algorithm. What you will learn Use the popular programming languages,T-SQL, R, and Python, for data science Understand your data with queries and introductory statistics Create and enhance the datasets for ML Visualize and analyze data using basic and advanced graphs Explore ML using unsupervised and supervised models Deploy models in SQL Server and perform predictions Who this book is for SQL Server professionals who want to start with data science, and data scientists who would like to start using SQL Server in their projects will find this book to be useful. Prior exposure to SQL Server will be helpful.

Building Machine Learning and Deep Learning Models on Google Cloud Platform

Author : Ekaba Bisong
Publisher : Apress
ISBN 13 : 1484244702
Total Pages : 703 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Building Machine Learning and Deep Learning Models on Google Cloud Platform by : Ekaba Bisong

Download or read book Building Machine Learning and Deep Learning Models on Google Cloud Platform written by Ekaba Bisong and published by Apress. This book was released on 2019-09-27 with total page 703 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a systematic approach to understanding the fundamentals of machine learning and deep learning from the ground up and how they are applied in practice. You will use this comprehensive guide for building and deploying learning models to address complex use cases while leveraging the computational resources of Google Cloud Platform. Author Ekaba Bisong shows you how machine learning tools and techniques are used to predict or classify events based on a set of interactions between variables known as features or attributes in a particular dataset. He teaches you how deep learning extends the machine learning algorithm of neural networks to learn complex tasks that are difficult for computers to perform, such as recognizing faces and understanding languages. And you will know how to leverage cloud computing to accelerate data science and machine learning deployments. Building Machine Learning and Deep Learning Models on Google Cloud Platform is divided into eight parts that cover the fundamentals of machine learning and deep learning, the concept of data science and cloud services, programming for data science using the Python stack, Google Cloud Platform (GCP) infrastructure and products, advanced analytics on GCP, and deploying end-to-end machine learning solution pipelines on GCP. What You’ll Learn Understand the principles and fundamentals of machine learning and deep learning, the algorithms, how to use them, when to use them, and how to interpret your resultsKnow the programming concepts relevant to machine and deep learning design and development using the Python stack Build and interpret machine and deep learning models Use Google Cloud Platform tools and services to develop and deploy large-scale machine learning and deep learning products Be aware of the different facets and design choices to consider when modeling a learning problem Productionalize machine learning models into software products Who This Book Is For Beginners to the practice of data science and applied machine learning, data scientists at all levels, machine learning engineers, Google Cloud Platform data engineers/architects, and software developers

Graph Algorithms

Author : Mark Needham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492047635
Total Pages : 297 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Graph Algorithms by : Mark Needham

Download or read book Graph Algorithms written by Mark Needham and published by "O'Reilly Media, Inc.". This book was released on 2019-05-16 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark

Data Science and Machine Learning

Author : Dirk P. Kroese
Publisher : CRC Press
ISBN 13 : 1000730778
Total Pages : 538 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Data Science and Machine Learning by : Dirk P. Kroese

Download or read book Data Science and Machine Learning written by Dirk P. Kroese and published by CRC Press. This book was released on 2019-11-20 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code

Machine Learning for Data Science Handbook

Author : Lior Rokach
Publisher : Springer Nature
ISBN 13 : 3031246284
Total Pages : 975 pages
Book Rating : 4.0/5 (312 download)

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Book Synopsis Machine Learning for Data Science Handbook by : Lior Rokach

Download or read book Machine Learning for Data Science Handbook written by Lior Rokach and published by Springer Nature. This book was released on 2023-08-17 with total page 975 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book organizes key concepts, theories, standards, methodologies, trends, challenges and applications of data mining and knowledge discovery in databases. It first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. It also gives in-depth descriptions of data mining applications in various interdisciplinary industries.

Data Science on AWS

Author : Chris Fregly
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492079367
Total Pages : 524 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Data Science on AWS by : Chris Fregly

Download or read book Data Science on AWS written by Chris Fregly and published by "O'Reilly Media, Inc.". This book was released on 2021-04-07 with total page 524 pages. Available in PDF, EPUB and Kindle. Book excerpt: With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance. Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and more Use automated machine learning to implement a specific subset of use cases with SageMaker Autopilot Dive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deployment Tie everything together into a repeatable machine learning operations pipeline Explore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache Kafka Learn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

Learn By Examples - A Quick Guide To Data Science With Python

Author : Eric M. H. Goh
Publisher : SVBook Pte. Ltd.
ISBN 13 : 1635352991
Total Pages : 101 pages
Book Rating : 4.6/5 (353 download)

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Book Synopsis Learn By Examples - A Quick Guide To Data Science With Python by : Eric M. H. Goh

Download or read book Learn By Examples - A Quick Guide To Data Science With Python written by Eric M. H. Goh and published by SVBook Pte. Ltd. . This book was released on with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aim to equip the reader with Python Programming and Data Science basics. There will be many examples and explanations that are straight to the point. You will be walked through data mining process from data preparation to data analysis (descriptive statistics) and data visualization to prediction modeling (machine learning) and deployment using Python. Content Covered: IntroductionGetting Started (Installing WinPython, IDE, ...)Language Essentials (variables, list, data types manipulations, ...)Language Essentials II (conditional statements, loops, ...)Object Essentials (Modules, Class and Objects, ...)Data Mining with Python (Pandas, ScikitLearn, ...) We will be using opensource tools and IDE, hence, you don't have to worry about buying any softwares. The book is designed for non-programmers only. It will gives you a head start into python programming, with a touch on data mining. This book has been taught at Udemy and EMHAcademy.com. Use the following Coupon to get the Udemy Course at $11.99: https://www.udemy.com/fundamentals-of-python-for-data-mining/?couponCode=EBOOKSPECIAL ISBN: 978-163535299-3

Handbook of HydroInformatics

Author : Saeid Eslamian
Publisher : Elsevier
ISBN 13 : 012821970X
Total Pages : 484 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Handbook of HydroInformatics by : Saeid Eslamian

Download or read book Handbook of HydroInformatics written by Saeid Eslamian and published by Elsevier. This book was released on 2022-11-30 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series.? Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc.?It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques. This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering. Key insights from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. Introduces classic soft-computing techniques, necessary for a range of disciplines.