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High Performance Python
Your Python code may run correctly, but what if you need it to run faster? This practical book shows you how to locate performance bottlenecks and significantly speed up your code in high-data-volume programs. By explaining the fundamental theory behind design choices, this expanded edition of High Performance Python helps experienced Python programmers gain a deeper understanding of Python's implementation.How do you take advantage of multicore architectures or compilation? Or build a system that scales up beyond RAM limits or with a GPU? Authors Micha Gorelick and Ian Ozsvald reveal concrete solutions to many issues and include war stories from companies that use high-performance Python for GenAI data extraction, productionized machine learning, and more.Get a better grasp of NumPy, Cython, and profilersLearn how Python abstracts the underlying computer architectureUse profiling to find bottlenecks in CPU time and memory usageWrite efficient programs by choosing appropriate data structuresSpeed up matrix and vector computationsProcess DataFrames quickly with Pandas, Dask, and PolarsSpeed up your neural networks and GPU computationsUse tools to compile Python down to machine codeManage multiple I/O and computational operations concurrentlyConvert multiprocessing code to run on local or remote clusters
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Guide to Intelligent Data Science
Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results.Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included.Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website.This practical and systematic textbook/reference is a “need-to-have” tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a “need to use, need to keep” resource following one's exploration of the subject.
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Reinforcement Learning Explained
Reinforcement Learning (RL) is a branch of Artificial Intelligence (AI) that teaches agents to learn optimal behavior through interaction, feedback, and long-term goals. After decades of research, RL has matured into a powerful technology driving real-world innovation; it is now used in areas such as robotics, energy systems, finance, and autonomous vehicles.Yet, for many, RL feels inaccessible, buried under dense mathematics and complex theory. This book changes that. It is designed to help newcomers start applying RL as quickly as possible through a classical pedagogical approach: many small, focused examples that build intuition and practical skill step by step.Featuring:• Essential concepts explained from the ground up• Code-based examples that reveal how algorithms work in practice• Worked examples by hand to strengthen intuition, just like in engineering or mathematics• Language-agnostic guidance, easily followed using Python, Java, or C++Even readers without coding or university-level mathematics backgrounds will gain valuable insight into the fascinating world of RL - insight that may become a critical differentiator in the age of AI. Whether you are a student or professional, Reinforcement Learning Explained will give you the tools and confidence to explore one of AI’s most exciting frontiers.
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Statistical Inference via Data Science
Statistical Inference via Data Science: A ModernDive into R and the Tidyverse, Second Edition offers a comprehensive guide to learning statistical inference with data science tools widely used in industry, academia, and government. The first part of this book introduces the tidyverse suite of R packages, including ggplot2 for data visualization and dplyr for data wrangling. The second part introduces data modeling via simple and multiple linear regression. The third part presents statistical inference using simulation-based methods within a general framework implemented in R via the infer package, a suitable complement to the tidyverse. By working with these methods, readers can implement effective exploratory data analyses, conduct statistical modeling with data, and carry out statistical inference via confidence intervals and hypothesis testing. All of these tasks are performed by strongly emphasizing data visualization.Key Features in the Second Edition: Minimal Prerequisites: No prior calculus or coding experience is needed, making the content accessible to a wide audience. Real-World Data: Learn with real-world datasets, including all domestic flights leaving New York City in 2023, the Gapminder project, FiveThirtyEight.com data, and new datasets on health, global development, music, coffee quality, and geyser eruptions. Simulation-Based Inference: Statistical inference through simulation-based methods. Expanded Theoretical Discussions: Includes deeper coverage of theory-based approaches, their connection with simulation-based approaches, and a presentation of intuitive and formal aspects of these methods. Enhanced Use of the infer Package: Leverages the infer package for “tidy” and transparent statistical inference, enabling readers to construct confidence intervals and conduct hypothesis tests through multiple linear regression and beyond. Dynamic Online Resources: All code and output are embedded in the text, with additional interactive exercises, discussions, and solutions available online. Broadened Applications: Suitable for undergraduate and graduate courses, including statistics, data science, and courses emphasizing reproducible research.The first edition of the book has been used in so many different ways--for courses in statistical inference, statistical programming, business analytics, and data science for social policy, and by professionals in many other means. Ideal for those new to statistics or looking to deepen their knowledge, this edition provides a clear entry point into data science and modern statistical methods.
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Machine Learning Guide for Oil and Gas Using Python
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.
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Data Science
This two volume set (CCIS 1257 and 1258) constitutes the refereed proceedings of the 6th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2020 held in Taiyuan, China, in September 2020.The 98 papers presented in these two volumes were carefully reviewed and selected from 392 submissions. The papers are organized in topical sections: database, machine learning, network, graphic images, system, natural language processing, security, algorithm, application, and education.
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Data Science Revealed
Get insight into data science techniques such as data engineering and visualization, statistical modeling, machine learning, and deep learning. This book teaches you how to select variables, optimize hyper parameters, develop pipelines, and train, test, and validate machine and deep learning models. Each chapter includes a set of examples allowing you to understand the concepts, assumptions, and procedures behind each model.The book covers parametric methods or linear models that combat under- or over-fitting using techniques such as Lasso and Ridge. It includes complex regression analysis with time series smoothing, decomposition, and forecasting. It takes a fresh look at non-parametric models for binary classification (logistic regression analysis) and ensemble methods such as decision trees, support vector machines, and naive Bayes. It covers the most popular non-parametric method for time-event data (the Kaplan-Meier estimator). It also covers ways of solving classification problems using artificial neural networks such as restricted Boltzmann machines, multi-layer perceptrons, and deep belief networks. The book discusses unsupervised learning clustering techniques such as the K-means method, agglomerative and Dbscan approaches, and dimension reduction techniques such as Feature Importance, Principal Component Analysis, and Linear Discriminant Analysis. And it introduces driverless artificial intelligence using H2O.After reading this book, you will be able to develop, test, validate, and optimize statistical machine learning and deep learning models, and engineer, visualize, and interpret sets of data.What You Will LearnDesign, develop, train, and validate machine learning and deep learning modelsFind optimal hyper parameters for superior model performanceImprove model performance using techniques such as dimension reduction and regularizationExtract meaningful insights for decision making using data visualizationWho This Book Is ForBeginning and intermediate level data scientists and machine learning engineers
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Mastering Text Analytics
This book is a comprehensive guide to mastering Natural Language Processing (NLP), a rapidly growing field in AI-powered text and data analytics. It equips you with tools and techniques to extract valuable insights from both structured and unstructured data, enabling you to uncover insights beyond the reach of traditional data analysis methods and stay competitive in this evolving domain. The book starts with foundational concepts, such as collecting and extracting data for NLP projects, before progressing to advanced topics like applications of transfer learning in NLP and Large Language Models (LLMs). Each chapter emphasizes real-world applications and includes practical case studies to ensure the knowledge is immediately applicable. Throughout the book, readers will find Python code demonstrations, hands-on projects, and detailed explanations of key concepts. Special features include business use cases from industries like healthcare and customer service, practice exercises to reinforce learning, and explorations of emerging NLP technologies. These elements make the book not only informative but also highly engaging and interactive. By the end of the book, the reader will have a solid foundation in Generative AI techniques to apply them to complex challenges. Whether you're a budding data scientist or a seasoned professional, this guide will help you harness the power of AI-driven text and data analytics effectively. What you will learn: Understand NLP with easy-to-follow explanations, examples, and Python implementations. Explore techniques such as transformers, word embeddings, and pragmatic analysis in real-world contexts. Work with real-world datasets and apply pre-processing, tokenization, and text extraction using NLP libraries. How to build complete NLP pipelines from data collection to model implementation, including sentiment analysis and chatbots. Learn state-of-the-art methods like deep learning techniques in NLP, large language models (LLMs), and zero-shot learning in NLP. Who this book is for: This book is tailored for data scientists, machine learning engineers, AI practitioners, and software developers seeking to learn NLP techniques and apply them to solve problems.
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Data Science
This two volume set (CCIS 901 and 902) constitutes the refereed proceedings of the 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018 (originally ICYCSEE) held in Zhengzhou, China, in September 2018. The 125 revised full papers presented in these two volumes were carefully reviewed and selected from 1057 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including mathematical issues in data science, computational theory for data science, big data management and applications, data quality and data preparation, evaluation and measurement in data science, data visualization, big data mining and knowledge management, infrastructure for data science, machine learning for data science, data security and privacy, applications of data science, case study of data science, multimedia data management and analysis, data-driven scientific research, data-driven bioinformatics, data-driven healthcare, data-driven management, data-driven eGovernment, data-driven smart city/planet, data marketing and economics, social media and recommendation systems, data-driven security, data-driven business model innovation, social and/or organizational impacts of data science.
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Building Modern Data Applications Using Databricks Lakehouse
Get up to speed with the Databricks Data Intelligence Platform to build and scale modern data applications, leveraging the latest advancements in data engineeringKey FeaturesLearn how to work with real-time data using Delta Live TablesUnlock insights into the performance of data pipelines using Delta Live TablesApply your knowledge to Unity Catalog for robust data security and governancePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionWith so many tools to choose from in today’s data engineering development stack as well as operational complexity, this often overwhelms data engineers, causing them to spend less time gleaning value from their data and more time maintaining complex data pipelines. Guided by a lead specialist solutions architect at Databricks with 10+ years of experience in data and AI, this book shows you how the Delta Live Tables framework simplifies data pipeline development by allowing you to focus on defining input data sources, transformation logic, and output table destinations.This book gives you an overview of the Delta Lake format, the Databricks Data Intelligence Platform, and the Delta Live Tables framework. It teaches you how to apply data transformations by implementing the Databricks medallion architecture and continuously monitor the data quality of your pipelines. You’ll learn how to handle incoming data using the Databricks Auto Loader feature and automate real-time data processing using Databricks workflows. You’ll master how to recover from runtime errors automatically.By the end of this book, you’ll be able to build a real-time data pipeline from scratch using Delta Live Tables, leverage CI/CD tools to deploy data pipeline changes automatically across deployment environments, and monitor, control, and optimize cloud costs.What you will learnDeploy near-real-time data pipelines in Databricks using Delta Live TablesOrchestrate data pipelines using Databricks workflowsImplement data validation policies and monitor/quarantine bad dataApply slowly changing dimensions (SCD), Type 1 and 2, data to lakehouse tablesSecure data access across different groups and users using Unity CatalogAutomate continuous data pipeline deployment by integrating Git with build tools such as Terraform and Databricks Asset BundlesWho this book is forThis book is for data engineers looking to streamline data ingestion, transformation, and orchestration tasks. Data analysts responsible for managing and processing lakehouse data for analysis, reporting, and visualization will also find this book beneficial. Additionally, DataOps/DevOps engineers will find this book helpful for automating the testing and deployment of data pipelines, optimizing table tasks, and tracking data lineage within the lakehouse. Beginner-level knowledge of Apache Spark and Python is needed to make the most out of this book.
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Confident Data Science
The global data market is estimated to be worth $64 billion dollars, making it a more valuable resource than oil. But data is useless without the analysis, interpretation and innovations of data scientists. With Confident Data Science, learn the essential skills and build your confidence in this sector through key insights and practical tools for success. In this book, you will discover all of the skills you need to understand this discipline, from primers on the key analytic and visualization tools to tips for pitching to and working with clients. Adam Ross Nelson draws upon his expertise as a data science consultant and, as someone who made moved into the industry late in his career, to provide an overview of data science, including its key concepts, its history and the knowledge required to become a successful data scientist. Whether you are considering a career in this industry or simply looking to expand your knowledge, Confident Data Science is the essential guide to the world of data science.About the Confident series...From coding and data science to cloud and cyber security, the Confident books are perfect for building your technical knowledge and enhancing your professional career.
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Data Analysis for Business Students
Data Analysis for Business Students: A Mind-Mapping Approach is an innovative educational resource that guides business students in mastering the essential concepts and methodologies of data analysis. This textbook's unique instructional design incorporates mind maps, a proven cognitive tool to streamline the learning experience, making complex data analysis concepts more digestible. Organised into five parts, the textbook covers an introduction to data analysis, data preparation and exploratory analysis, basic data analytics techniques, advanced data analytics techniques, and data analysis in a digital world. By integrating mind maps with practical applications and case studies, the textbook equips students with the analytical skills required to succeed in the data-rich business world.This unique pedagogical approach offers three key advantages:Simplifying complex information: Mind maps systematically organise information, making it easier for students to understand complex data analysis concepts.Improving memory retention: By linking pieces of information together, mind maps activate long-term memory and support better memory retention.Increasing student engagement: Mind maps can help students prepare for class activities, enabling a deeper understanding of the subject and allowing instructors to focus more on problem-solving skills and critical thinking.With extensive online resources, including PowerPoint slides, an instructor's manual, a quiz bank, tutorial questions, instructional videos on creating mind maps, and Python code for performing data analysis that students can access, use, and experiment with, Data Analysis for Business Students: A Mind-Mapping Approach offers a structured and accessible approach for advanced undergraduate and postgraduate business students.
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Algorithms for Data Science
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses.This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials.This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
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Data Science
Learn how to build a data science team within your organization rather than hiring from the outside. Teach your team to ask the right questions to gain actionable insights into your business.Most organizations still focus on objectives and deliverables. Instead, a data science team is exploratory. They use the scientific method to ask interesting questions and run small experiments. Your team needs to see if the data illuminate their questions. Then, they have to use critical thinking techniques to justify their insights and reasoning. They should pivot their efforts to keep their insights aligned with business value. Finally, your team needs to deliver these insights as a compelling story.Insight!: How to Build Data Science Teams that Deliver Real Business Value shows that the most important thing you can do now is help your team think about data. Management coach Doug Rose walks you through the process of creating and managing effective data science teams. You will learn how to find the right people inside your organization and equip them with the right mindset. The book has three overarching concepts:You should mine your own company for talent. You can’t change your organization by hiring a few data science superheroes.You should form small, agile-like data teams that focus on delivering valuable insights early and often.You can make real changes to your organization by telling compelling data stories. These stories are the best way to communicate your insights about your customers, challenges, and industry.What Your Will Learn:Create data science teams from existing talent in your organization to cost-efficiently extract maximum business value from your organization’s dataUnderstand key data science terms and concepts Follow practical guidance to create and integrate an effective data science team with key roles and the responsibilities for each team member Utilize the data science life cycle (DSLC) to model essential processes and practices for delivering valueUse sprints and storytelling to help your team stay on track and adapt to new knowledgeWho This Book Is ForData science project managers and team leaders. The secondary readership is data scientists, DBAs, analysts, senior management, HR managers, and performance specialists.
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Exploratory Data Analysis
This book is a comprehensive guide to exploratory data analysis (EDA), providing readers with the tools, techniques, and knowledge needed to conduct effective and thorough data exploration. Throughout the seven main chapters, this book details various aspects of EDA, from data description and preprocessing to visualization, storytelling, and dashboard design. We will explore real‑world datasets, uncovering hidden patterns and gaining insights along the way. This book is filled with illustrations using practical examples, Python codes, and different types of exercises designed to reinforce the concepts and processes discussed. Whether you are a student just starting out in the field of data science, a senior professional looking to improve your skills, or a curious individual interested in the power of data, this book is for you.
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DevOps for Data Science
Data Scientists are experts at analyzing, modelling and visualizing data but, at one point or another, have all encountered difficulties in collaborating with or delivering their work to the people and systems that matter. Born out of the agile software movement, DevOps is a set of practices, principles and tools that help software engineers reliably deploy work to production. This book takes the lessons of DevOps and aplies them to creating and delivering production-grade data science projects in Python and R.This book’s first section explores how to build data science projects that deploy to production with no frills or fuss. Its second section covers the rudiments of administering a server, including Linux, application, and network administration before concluding with a demystification of the concerns of enterprise IT/Administration in its final section, making it possible for data scientists to communicate and collaborate with their organization’s security, networking, and administration teams.Key Features:• Start-to-finish labs take readers through creating projects that meet DevOps best practices and creating a server-based environment to work on and deploy them.• Provides an appendix of cheatsheets so that readers will never be without the reference they need to remember a Git, Docker, or Command Line command.• Distills what a data scientist needs to know about Docker, APIs, CI/CD, Linux, DNS, SSL, HTTP, Auth, and more.• Written specifically to address the concern of a data scientist who wants to take their Python or R work to production.There are countless books on creating data science work that is correct. This book, on the otherhand, aims to go beyond this, targeted at data scientists who want their work to be than merely accurate and deliver work that matters.
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Exploring Data Science with R and the Tidyverse
This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader. Upon completion of the text, the reader will be able to: Gain proficiency in R programming Load and manipulate data frames, and "tidy" them using tidyverse tools Conduct statistical analyses and draw meaningful inferences from them Perform modeling from numerical and textual data Generate data visualizations (numerical and spatial) using ggplot2 and understand what is being representedAn accompanying R package "edsdata" contains synthetic and real datasets used by the textbook and is meant to be used for further practice. An exercise set is made available and designed for compatibility with automated grading tools for instructor use.
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Google Cloud Platform for Data Science
Data science and machine learning are increasingly becoming critical to businesses of all sizes, and the cloud provides a powerful platform for these applications. Google Cloud Platform (GCP) offers a range of data science services that can be used to store, process, and analyze large datasets, as well as train and deploy machine learning models. This book provides a comprehensive guide to learning GCP for data science, using only the free tier services offered by the platform.The book is organized into seven chapters covering various topics such as GCP account setup, Google Colaboratory, Big Data and Machine Learning, Data Visualization and Business Intelligence, Data Processing and Transformation, Data Analytics and Storage, and Advanced Topics. Each chapter provides step-by-step instructions and examples that illustrate how to use GCP services for data science and big data projects.Readers will learn how to set up a Google Colaboratory account and run Jupyter notebooks, access GCP services, and data from Colaboratory, use BigQuery for data analytics, and deploy machine learning models using Vertex AI. The book also covers how to visualize data using Looker Data Studio, run data processing pipelines using Google Cloud Dataflow and Dataprep, and store data using Google Cloud Storage and SQL.What you Will LearnHow to set up a GCP account and projectBigQuery and its use cases, including machine learningGoogle Cloud AI Platform and its capabilities How to use Vertex AI for training and deploying machinelearning models Google Cloud Dataproc and its use cases for big data processingHow to create and share data visualizations and reports with Looker Data StudioGoogle Cloud Dataflow and its use cases for batch and stream data processing Running data processing pipelines on Cloud DataflowGoogle Cloud Storage and its use cases for data storage An introduction to Google Cloud SQL and its use cases for relational databases An introduction to Google Cloud Pub/Sub and its use cases for real-time data streamingWho This Book Is for:A practical guide designed for data scientists, machine learning engineers, and analysts who want to learn how to use Google Cloud Platform (GCP) for their data science and big data projects.
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Machine Learning and AI with Simple Python and Matlab Scripts
A practical guide to AI applications for Simple Python and Matlab scripts Machine Learning and AI with Simple Python and Matlab Scripts: Courseware for Non-computing Majors introduces basic concepts and principles of machine learning and artificial intelligence to help readers develop skills applicable to many popular topics in engineering and science. Step-by-step instructions for simple Python and Matlab scripts mimicking real-life applications will enter the readers into the magical world of AI, without requiring them to have advanced math and computational skills. The book is supported by instructor only lecture slides and sample exams with multiple-choice questions. Machine Learning and AI with Simple Python and Matlab Scripts includes information on: Artificial neural networks applied to real-world problems such as algorithmic trading of financial assets, Alzheimer’s disease prognosisConvolution neural networks for speech recognition and optical character recognitionRecurrent neural networks for chatbots and natural language translatorsTypical AI tasks including flight control for autonomous drones, dietary menu planning, and route planningAdvanced AI tasks including particle swarm optimization and differential and grammatical evolution as well as the current state of the art in AI tools Machine Learning and AI with Simple Python and Matlab Scripts is an accessible, thorough, and practical learning resource for undergraduate and graduate students in engineering and science programs along with professionals in related industries seeking to expand their skill sets.
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First Course in Network Science
Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.
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Data Science on AWS
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 up your 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 moreUse automated machine learning to implement a specific subset of use cases with Amazon SageMaker AutopilotDive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, and moreTie everything together into a repeatable machine learning operations pipelineExplore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache KafkaLearn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more
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Hands-On Data Science for Librarians
Librarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there’s a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through each skill and its application to library work before walking the reader through each line of code, this book will support librarians who want to apply data science in their daily work. Hands-On Data Science for Librarians is intended for librarians (and other information professionals) in any library type (public, academic or special) as well as graduate students in library and information science (LIS).Key Features: Only data science book available geared toward librarians that includes step-by-step code examples Examples include all library types (public, academic, special) Relevant datasets Accessible to non-technical professionals Focused on job skills and their applications
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Data Science in Practice
This book approaches big data, artificial intelligence, machine learning, and business intelligence through the lens of Data Science. We have grown accustomed to seeing these terms mentioned time and time again in the mainstream media. However, our understanding of what they actually mean often remains limited. This book provides a general overview of the terms and approaches used broadly in data science, and provides detailed information on the underlying theories, models, and application scenarios. Divided into three main parts, it addresses what data science is; how and where it is used; and how it can be implemented using modern open source software. The book offers an essential guide to modern data science for all students, practitioners, developers and managers seeking a deeper understanding of how various aspects of data science work, and of how they can be employed to gain a competitive advantage.
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