{"title":"AI \u0026 Machine Learning","description":"","products":[{"product_id":"learn-ai-with-python-explore-machine-learning-and-deep-learning-techniques-for-building-smart-ai-systems-using-scikitlearn-nl-new","title":"Learn Ai With Python: Explore Machine Learning And Deep Learning Techniques For Building Smart Ai Systems Using Scikitlearn, Nl,New","description":"\u003cp\u003eBuild Ai Applications Using Python To Intelligently Interact With The World Around You.Key Features? Covers The Practical Aspects Of Machine Learning And Deep Learning Concepts With The Help Of This Examplerich Guide To Python.? Includes Graphical Illustrations Of Natural Language Processing And Its Implementation In Nltk.? Covers Deep Learning Models Such As Rcnn And Yolo For Object Recognition And Teaches How To Build An Image Classifier Using Cnn.Descriptionthe Book Learn Ai With Python Is Intended To Provide You With A Thorough Understanding Of Artificial Intelligence As Well As The Tools Necessary To Create Your Intelligent Applications.This Book Introduces You To Artificial Intelligence And Walks You Through The Process Of Establishing An Ai Environment On A Variety Of Platforms. It Dives Into Machine Learning Models And Various Predictive Modeling Techniques, Including Classification, Regression, And Clustering. Additionally, It Provides Handson Experience With Logic Programming, Asr, Neural Networks, And Natural Language Processing Through Realworld Examples And Fully Functional Python Implementation. Finally, The Book Deals With Profound Models Of Learning Such As Rcnn And Yolo. Object Detection In Images Is Also Explained In Detail Using Convolutional Neural Networks (Cnns), Which Are Also Explained.By The End Of This Book, You Will Have A Firm Grasp Of Machine Learning And Deep Learning Techniques, As Well As A Steered Methodology For Formulating And Solving Related Problems.What You Will Learn? Learn To Implement Various Machine Learning And Deep Learning Algorithms To Achieve Smart Results.? Understand How Ml Algorithms Can Be Applied To Reallife Applications.? Explore Logic Programming And Learn How To Use It Practically To Solve Reallife Problems.? Learn To Develop Different Types Of Artificial Neural Networks With Python.? Understand Reinforcement Learning And How To Build An Environment And Agents Using Python.? Work With Nltk And Build An Automatic Speech Recognition System.Who This Book Is Forthis Book Is For Anyone Interested In Learning About Artificial Intelligence And Putting It Into Practice With Python. This Book Is Also Valuable For Intermediate Machine Learning Practitioners As A Reference Guide. Readers Should Be Familiar With The Fundamental Understanding Of Python Programming And Machine Learning Techniques.Table Of Contents1. Introduction To Ai And Python2. Machine Learning And Its Algorithms3. Classification And Regression Using Supervised Learning4. Clustering Using Unsupervised Learning5. Solving Problems With Logic Programming6. Natural Language Processing With Python7. Implementing Speech Recognition With Python8. Implementing Artificial Neural Network (Ann) With Python9. Implementing Reinforcement Learning With Python10. Implementing Deep Learning And Convolutional Neural Network\u003c\/p\u003e","brand":"Bpb","offers":[{"title":"Default Title","offer_id":46554749174005,"sku":"DADAX939139261X","price":35.38,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/714RXauImcL.jpg?v=1744431501"},{"product_id":"elements-of-causal-inference-foundations-and-learning-algorithms-adaptive-computation-and-machine-learning-series-new","title":"Elements Of Causal Inference: Foundations And Learning Algorithms (Adaptive Computation And Machine Learning Series),New","description":"\u003cp\u003eA Concise And Selfcontained Introduction To Causal Inference, Increasingly Important In Data Science And Machine Learning.The Mathematization Of Causality Is A Relatively Recent Development, And Has Become Increasingly Important In Data Science And Machine Learning. This Book Offers A Selfcontained And Concise Introduction To Causal Models And How To Learn Them From Data.After Explaining The Need For Causal Models And Discussing Some Of The Principles Underlying Causal Inference, The Book Teaches Readers How To Use Causal Models: How To Compute Intervention Distributions, How To Infer Causal Models From Observational And Interventional Data, And How Causal Ideas Could Be Exploited For Classical Machine Learning Problems. All Of These Topics Are Discussed First In Terms Of Two Variables And Then In The More General Multivariate Case. The Bivariate Case Turns Out To Be A Particularly Hard Problem For Causal Learning Because There Are No Conditional Independences As Used By Classical Methods For Solving Multivariate Cases. The Authors Consider Analyzing Statistical Asymmetries Between Cause And Effect To Be Highly Instructive, And They Report On Their Decade Of Intensive Research Into This Problem.The Book Is Accessible To Readers With A Background In Machine Learning Or Statistics, And Can Be Used In Graduate Courses Or As A Reference For Researchers. The Text Includes Code Snippets That Can Be Copied And Pasted, Exercises, And An Appendix With A Summary Of The Most Important Technical Concepts.\u003c\/p\u003e","brand":"Mit Press","offers":[{"title":"Default Title","offer_id":46555091435765,"sku":"DADAX0262037319","price":60.53,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/91acXKRW7jL.jpg?v=1744435855"},{"product_id":"introduction-to-natural-language-processing-adaptive-computation-and-machine-learning-series-new","title":"Introduction To Natural Language Processing (Adaptive Computation And Machine Learning Series),New","description":"\u003cp\u003eA Survey Of Computational Methods For Understanding, Generating, And Manipulating Human Language, Which Offers A Synthesis Of Classical Representations And Algorithms With Contemporary Machine Learning Techniques.This Textbook Provides A Technical Perspective On Natural Language Processingmethods For Building Computer Software That Understands, Generates, And Manipulates Human Language. It Emphasizes Contemporary Datadriven Approaches, Focusing On Techniques From Supervised And Unsupervised Machine Learning. The First Section Establishes A Foundation In Machine Learning By Building A Set Of Tools That Will Be Used Throughout The Book And Applying Them To Wordbased Textual Analysis. The Second Section Introduces Structured Representations Of Language, Including Sequences, Trees, And Graphs. The Third Section Explores Different Approaches To The Representation And Analysis Of Linguistic Meaning, Ranging From Formal Logic To Neural Word Embeddings. The Final Section Offers Chapterlength Treatments Of Three Transformative Applications Of Natural Language Processing: Information Extraction, Machine Translation, And Text Generation. Endofchapter Exercises Include Both Paperandpencil Analysis And Software Implementation.The Text Synthesizes And Distills A Broad And Diverse Research Literature, Linking Contemporary Machine Learning Techniques With The Field'S Linguistic And Computational Foundations. It Is Suitable For Use In Advanced Undergraduate And Graduatelevel Courses And As A Reference For Software Engineers And Data Scientists. Readers Should Have A Background In Computer Programming And Collegelevel Mathematics. After Mastering The Material Presented, Students Will Have The Technical Skill To Build And Analyze Novel Natural Language Processing Systems And To Understand The Latest Research In The Field.\u003c\/p\u003e","brand":"Mit Press","offers":[{"title":"Default Title","offer_id":46555149172981,"sku":"DADAX0262042843","price":91.92,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/81ZJsEjdkEL.jpg?v=1744436556"},{"product_id":"reinforcement-learning-an-introduction-adaptive-computation-and-machine-learning-used","title":"Reinforcement Learning: An Introduction (Adaptive Computation And Machine Learning),Used","description":"\u003cp\u003eRichard Sutton And Andrew Barto Provide A Clear And Simple Account Of The Key Ideas And Algorithms Of Reinforcement Learning. Their Discussion Ranges From The History Of The Field'S Intellectual Foundations To The Most Recent Developments And Applications.Reinforcement Learning, One Of The Most Active Research Areas In Artificial Intelligence, Is A Computational Approach To Learning Whereby An Agent Tries To Maximize The Total Amount Of Reward It Receives When Interacting With A Complex, Uncertain Environment. In Reinforcement Learning, Richard Sutton And Andrew Barto Provide A Clear And Simple Account Of The Key Ideas And Algorithms Of Reinforcement Learning. Their Discussion Ranges From The History Of The Field'S Intellectual Foundations To The Most Recent Developments And Applications. The Only Necessary Mathematical Background Is Familiarity With Elementary Concepts Of Probability.The Book Is Divided Into Three Parts. Part I Defines The Reinforcement Learning Problem In Terms Of Markov Decision Processes. Part Ii Provides Basic Solution Methods: Dynamic Programming, Monte Carlo Methods, And Temporaldifference Learning. Part Iii Presents A Unified View Of The Solution Methods And Incorporates Artificial Neural Networks, Eligibility Traces, And Planning; The Two Final Chapters Present Case Studies And Consider The Future Of Reinforcement Learning.\u003c\/p\u003e","brand":"A Bradford Book","offers":[{"title":"Default Title","offer_id":46561570291957,"sku":"SONG0262193981","price":47.37,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/91_bG6uIDkL.jpg?v=1744626244"},{"product_id":"data-mining-practical-machine-learning-tools-and-techniques-the-morgan-kaufmann-series-in-data-management-systems-used","title":"Data Mining: Practical Machine Learning Tools And Techniques (The Morgan Kaufmann Series In Data Management Systems),Used","description":"\u003cp\u003eData Mining: Practical Machine Learning Tools And Techniques, Third Edition, Offers A Thorough Grounding In Machine Learning Concepts As Well As Practical Advice On Applying Machine Learning Tools And Techniques In Realworld Data Mining Situations. This Highly Anticipated Third Edition Of The Most Acclaimed Work On Data Mining And Machine Learning Will Teach You Everything You Need To Know About Preparing Inputs, Interpreting Outputs, Evaluating Results, And The Algorithmic Methods At The Heart Of Successful Data Mining.Thorough Updates Reflect The Technical Changes And Modernizations That Have Taken Place In The Field Since The Last Edition, Including New Material On Data Transformations, Ensemble Learning, Massive Data Sets, Multiinstance Learning, Plus A New Version Of The Popular Weka Machine Learning Software Developed By The Authors. Witten, Frank, And Hall Include Both Triedandtrue Techniques Of Today As Well As Methods At The Leading Edge Of Contemporary Research.The Book Is Targeted At Information Systems Practitioners, Programmers, Consultants, Developers, Information Technology Managers, Specification Writers, Data Analysts, Data Modelers, Database R\u0026amp;D Professionals, Data Warehouse Engineers, Data Mining Professionals. The Book Will Also Be Useful For Professors And Students Of Upperlevel Undergraduate And Graduatelevel Data Mining And Machine Learning Courses Who Want To Incorporate Data Mining As Part Of Their Data Management Knowledge Base And Expertise. Provides A Thorough Grounding In Machine Learning Concepts As Well As Practical Advice On Applying The Tools And Techniques To Your Data Mining Projects Offers Concrete Tips And Techniques For Performance Improvement That Work By Transforming The Input Or Output In Machine Learning Methods Includes Downloadable Weka Software Toolkit, A Collection Of Machine Learning Algorithms For Data Mining Tasksin An Updated, Interactive Interface. Algorithms In Toolkit Cover: Data Preprocessing, Classification, Regression, Clustering, Association Rules, Visualization\u003c\/p\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46563889512693,"sku":"SONG0123748569","price":13.13,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/81sYy1D9n7L.jpg?v=1744703962"},{"product_id":"machine-learning-the-new-ai-the-mit-press-essential-knowledge-series-used","title":"Machine Learning: The New Ai (The Mit Press Essential Knowledge Series),Used","description":"\u003cp\u003eA Concise Overview Of Machine Learningcomputer Programs That Learn From Datawhich Underlies Applications That Include Recommendation Systems, Face Recognition, And Driverless Cars.Today, Machine Learning Underlies A Range Of Applications We Use Every Day, From Product Recommendations To Voice Recognitionas Well As Some We Don'T Yet Use Everyday, Including Driverless Cars. It Is The Basis Of The New Approach In Computing Where We Do Not Write Programs But Collect Data; The Idea Is To Learn The Algorithms For The Tasks Automatically From Data. As Computing Devices Grow More Ubiquitous, A Larger Part Of Our Lives And Work Is Recorded Digitally, And As Big Data Has Gotten Bigger, The Theory Of Machine Learningthe Foundation Of Efforts To Process That Data Into Knowledgehas Also Advanced. In This Book, Machine Learning Expert Ethem Alpaydin Offers A Concise Overview Of The Subject For The General Reader, Describing Its Evolution, Explaining Important Learning Algorithms, And Presenting Example Applications.Alpaydin Offers An Account Of How Digital Technology Advanced From Numbercrunching Mainframes To Mobile Devices, Putting Today'S Machine Learning Boom In Context. He Describes The Basics Of Machine Learning And Some Applications; The Use Of Machine Learning Algorithms For Pattern Recognition; Artificial Neural Networks Inspired By The Human Brain; Algorithms That Learn Associations Between Instances, With Such Applications As Customer Segmentation And Learning Recommendations; And Reinforcement Learning, When An Autonomous Agent Learns Act So As To Maximize Reward And Minimize Penalty. Alpaydin Then Considers Some Future Directions For Machine Learning And The New Field Of Data Science, And Discusses The Ethical And Legal Implications For Data Privacy And Security.\u003c\/p\u003e","brand":"Mit Press","offers":[{"title":"Default Title","offer_id":46564218929397,"sku":"SONG0262529513","price":9.51,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/613gt2vB-sL.jpg?v=1744711515"},{"product_id":"machine-learning-for-evolution-strategies-studies-in-big-data-20-used","title":"Machine Learning For Evolution Strategies (Studies In Big Data, 20),Used","description":"\u003cp\u003eThis Book Introduces Numerous Algorithmic Hybridizations Between Both Worlds That Show How Machine Learning Can Improve And Support Evolution Strategies. The Set Of Methods Comprises Covariance Matrix Estimation, Metamodeling Of Fitness And Constraint Functions, Dimensionality Reduction For Search And Visualization Of Highdimensional Optimization Processes, And Clusteringbased Niching. After Giving An Introduction To Evolution Strategies And Machine Learning, The Book Builds The Bridge Between Both Worlds With An Algorithmic And Experimental Perspective. Experiments Mostly Employ A (1+1)Es And Are Implemented In Python Using The Machine Learning Library Scikitlearn. The Examples Are Conducted On Typical Benchmark Problems Illustrating Algorithmic Concepts And Their Experimental Behavior. The Book Closes With A Discussion Of Related Lines Of Research.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":46564274045173,"sku":"SONG331933381X","price":83.33,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/61rSTq1UC_L.jpg?v=1744712010"},{"product_id":"machine-learning-for-factor-investing-r-version-r-version-chapman-and-hall-crc-financial-mathematics-series-new","title":"Machine Learning For Factor Investing: R Version: R Version (Chapman And Hall\/Crc Financial Mathematics Series),New","description":"\u003cp\u003eMachine Learning (Ml) Is Progressively Reshaping The Fields Of Quantitative Finance And Algorithmic Trading. Ml Tools Are Increasingly Adopted By Hedge Funds And Asset Managers, Notably For Alpha Signal Generation And Stocks Selection. The Technicality Of The Subject Can Make It Hard For Nonspecialists To Join The Bandwagon, As The Jargon And Coding Requirements May Seem Out Of Reach. Machine Learning For Factor Investing: R Version Bridges This Gap. It Provides A Comprehensive Tour Of Modern Mlbased Investment Strategies That Rely On Firm Characteristics.The Book Covers A Wide Array Of Subjects Which Range From Economic Rationales To Rigorous Portfolio Backtesting And Encompass Both Data Processing And Model Interpretability. Common Supervised Learning Algorithms Such As Tree Models And Neural Networks Are Explained In The Context Of Style Investing And The Reader Can Also Dig Into More Complex Techniques Like Autoencoder Asset Returns, Bayesian Additive Trees, And Causal Models.All Topics Are Illustrated With Selfcontained R Code Samples And Snippets That Are Applied To A Large Public Dataset That Contains Over 90 Predictors. The Material, Along With The Content Of The Book, Is Available Online So That Readers Can Reproduce And Enhance The Examples At Their Convenience. If You Have Even A Basic Knowledge Of Quantitative Finance, This Combination Of Theoretical Concepts And Practical Illustrations Will Help You Learn Quickly And Deepen Your Financial And Technical Expertise.\u003c\/p\u003e","brand":"Crc Press","offers":[{"title":"Default Title","offer_id":46564377493749,"sku":"DADAX0367545861","price":84.22,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/71qhOd0JnkL.jpg?v=1744717094"},{"product_id":"introduction-to-machine-learning-adaptive-computation-and-machine-learning-new","title":"Introduction To Machine Learning (Adaptive Computation And Machine Learning),New","description":"\u003cp\u003eA Substantially Revised Third Edition Of A Comprehensive Textbook That Covers A Broad Range Of Topics Not Often Included In Introductory Texts.The Goal Of Machine Learning Is To Program Computers To Use Example Data Or Past Experience To Solve A Given Problem. Many Successful Applications Of Machine Learning Exist Already, Including Systems That Analyze Past Sales Data To Predict Customer Behavior, Optimize Robot Behavior So That A Task Can Be Completed Using Minimum Resources, And Extract Knowledge From Bioinformatics Data. Introduction To Machine Learning Is A Comprehensive Textbook On The Subject, Covering A Broad Array Of Topics Not Usually Included In Introductory Machine Learning Texts. Subjects Include Supervised Learning; Bayesian Decision Theory; Parametric, Semiparametric, And Nonparametric Methods; Multivariate Analysis; Hidden Markov Models; Reinforcement Learning; Kernel Machines; Graphical Models; Bayesian Estimation; And Statistical Testing.Machine Learning Is Rapidly Becoming A Skill That Computer Science Students Must Master Before Graduation. The Third Edition Of Introduction To Machine Learning Reflects This Shift, With Added Support For Beginners, Including Selected Solutions For Exercises And Additional Example Data Sets (With Code Available Online). Other Substantial Changes Include Discussions Of Outlier Detection; Ranking Algorithms For Perceptrons And Support Vector Machines; Matrix Decomposition And Spectral Methods; Distance Estimation; New Kernel Algorithms; Deep Learning In Multilayered Perceptrons; And The Nonparametric Approach To Bayesian Methods. All Learning Algorithms Are Explained So That Students Can Easily Move From The Equations In The Book To A Computer Program. The Book Can Be Used By Both Advanced Undergraduates And Graduate Students. It Will Also Be Of Interest To Professionals Who Are Concerned With The Application Of Machine Learning Methods.\u003c\/p\u003e","brand":"The Mit Press","offers":[{"title":"Default Title","offer_id":46564465049845,"sku":"DADAX0262028182","price":72.09,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/41xwrAtgrAL.jpg?v=1744721622"},{"product_id":"supervised-machine-learning-optimization-framework-and-applications-with-sas-and-r-new","title":"Supervised Machine Learning: Optimization Framework And Applications With Sas And R,New","description":"\u003cp\u003eAi Framework Intended To Solve A Problem Of Biasvariance Tradeoff For Supervised Learning Methods In Reallife Applications. 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This Highly Anticipated Fourth Edition Of The Most Acclaimed Work On Data Mining And Machine Learning Teaches Readers Everything They Need To Know To Get Going, From Preparing Inputs, Interpreting Outputs, Evaluating Results, To The Algorithmic Methods At The Heart Of Successful Data Mining Approaches.Extensive Updates Reflect The Technical Changes And Modernizations That Have Taken Place In The Field Since The Last Edition, Including Substantial New Chapters On Probabilistic Methods And On Deep Learning. Accompanying The Book Is A New Version Of The Popular Weka Machine Learning Software From The University Of Waikato. Authors Witten, Frank, Hall, And Pal Include Today'S Techniques Coupled With The Methods At The Leading Edge Of Contemporary Research.Please Visit The Book Companion Website.It Contains Powerpoint Slides For Chapters 1 12. This Is A Very Comprehensive Teaching Resource, With Many Ppt Slides Covering Each Chapter Of The Book Online Appendix On The Weka Workbench; Again A Very Comprehensive Learning Aid For The Open Source Software That Goes With The Book Table Of Contents, Highlighting The Many New Sections In The 4Th Edition, Along With Reviews Of The 1St Edition, Errata, Etc. Provides A Thorough Grounding In Machine Learning Concepts, As Well As Practical Advice On Applying The Tools And Techniques To Data Mining Projects Presents Concrete Tips And Techniques For Performance Improvement That Work By Transforming The Input Or Output In Machine Learning Methods Includes A Downloadable Weka Software Toolkit, A Comprehensive Collection Of Machine Learning Algorithms For Data Mining Tasks In An Easy To Use Interactive Interface Includes Open Access Online Courses That Introduce Practical Applications Of The Material In The Book.\u003c\/p\u003e","brand":"Morgan Kaufmann","offers":[{"title":"Default Title","offer_id":46575347826933,"sku":"DADAX0128042915","price":64.91,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/71OKH1m3XhL.jpg?v=1745057099"},{"product_id":"machine-learning-for-multimedia-content-analysis-multimedia-systems-and-applications-30","title":"Machine Learning For Multimedia Content Analysis (Multimedia Systems And Applications, 30)","description":"\u003cp\u003eChallenges in complexity and variability of multimedia data have led to revolutions in machine learning techniques. Multimedia data, such as digital images, audio streams and motion video programs, exhibit richer structures than simple, isolated data items. A number of pixels in a digital image collectively conveys certain visual content to viewers. A TV video program consists of both audio and image streams that unfold the underlying story. To recognize the visual content of a digital image, or to understand the underlying story of a video program, we may need to label sets of pixels or groups of image and audio frames jointly.Machine Learning for Multimedia Content Analysis introduces machine learning techniques that are particularly powerful and effective for modeling spatial, temporal structures of multimedia data and for accomplishing common tasks of multimedia content analysis. This book systematically covers these techniques in an intuitive fashion and demonstrates their applications through case studies. This volume uses a large number of figures to illustrate and visualize complex concepts, and provides insights into the characteristics of many algorithms through examinations of their loss functions and straightforward comparisons.Machine Learning for Multimedia Content Analysis is designed for an academic and professional audience. Researchers will find this book an invaluable tool for applying machine learning techniques to multimedia content analysis. This volume is also suitable for practitioners in industry.\u003c\/p\u003e","brand":"Springer","offers":[{"title":"Default Title","offer_id":46596406739189,"sku":"DADAX0387699384","price":103.03,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/61p8R4LNAqL.jpg?v=1745875610"},{"product_id":"hbrs-10-must-reads-on-ai-with-bonus-article-how-to-win-with-machine-learning-by-ajay-agrawal-joshua-gans-and-avi-goldfarb-new","title":"Hbr'S 10 Must Reads On Ai (With Bonus Article 'How To Win With Machine Learning' By Ajay Agrawal, Joshua Gans, And Avi Goldfarb),New","description":"\u003cp\u003eThe next generation of AI is hereuse it to lead your business forward.If you read nothing else on artificial intelligence and machine learning, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand the future direction of AI, bring your AI initiatives to scale, and use AI to transform your organization.This book will inspire you to: Create a new AI strategy Learn to work with intelligent robots Get more from your marketing AI Be ready for ethical and regulatory challenges Understand how generative AI is game changing Stop tinkering with AI and go all inThis collection of articles includes 'Competing in the Age of AI,' by Marco Iansiti and Karim R. Lakhani; 'How to Win with Machine Learning,' by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; 'Developing a Digital Mindset,' by Tsedal Neeley and Paul Leonardi; 'Learning to Work with Intelligent Machines,' by Matt Beane; 'Getting AI to Scale,' by Tim Fountaine, Brian McCarthy, and Tamim Saleh; 'Why You Aren't Getting More from Your Marketing AI,' by Eva Ascarza, Michael Ross, and Bruce G. S. Hardie; 'The Pitfalls of Pricing Algorithms,' by Marco Bertini and Oded Koenigsberg; 'A Smarter Strategy for Using Robots,' by Ben Armstrong and Julie Shah; 'Why You Need an AI Ethics Committee,' by Reid Blackman; 'Robots Need Us More Than We Need Them,' by H. James Wilson and Paul R. Daugherty; 'Stop Tinkering with AI,' by Thomas H. Davenport and Nitin Mittal; and 'ChatGPT Is a Tipping Point for AI,' by Ethan Mollick.HBR's 10 Must Reads paperback series is the definitive collection of books for new and experienced leaders alike. Leaders looking for the inspiration that big ideas provide, both to accelerate their own growth and that of their companies, should look no further. HBR's 10 Must Reads series focuses on the core topics that every ambitious manager needs to know: leadership, strategy, change, managing people, and managing yourself. Harvard Business Review has sorted through hundreds of articles and selected only the most essential reading on each topic. Each title includes timeless advice that will be relevant regardless of an everchanging business environment.\u003c\/p\u003e","brand":"Harvard Business Review Press","offers":[{"title":"Default Title","offer_id":46616322375925,"sku":"DADAX1647825849","price":26.42,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/71y3rteWShL.jpg?v=1746531799"},{"product_id":"hbrs-10-must-reads-on-ai-with-bonus-article-how-to-win-with-machine-learning-by-ajay-agrawal-joshua-gans-and-avi-goldfarb-used","title":"Hbr'S 10 Must Reads On Ai (With Bonus Article 'How To Win With Machine Learning' By Ajay Agrawal, Joshua Gans, And Avi Goldfarb),Used","description":"\u003cp\u003eThe next generation of AI is hereuse it to lead your business forward.If you read nothing else on artificial intelligence and machine learning, read these 10 articles. We've combed through hundreds of Harvard Business Review articles and selected the most important ones to help you understand the future direction of AI, bring your AI initiatives to scale, and use AI to transform your organization.This book will inspire you to: Create a new AI strategy Learn to work with intelligent robots Get more from your marketing AI Be ready for ethical and regulatory challenges Understand how generative AI is game changing Stop tinkering with AI and go all inThis collection of articles includes 'Competing in the Age of AI,' by Marco Iansiti and Karim R. Lakhani; 'How to Win with Machine Learning,' by Ajay Agrawal, Joshua Gans, and Avi Goldfarb; 'Developing a Digital Mindset,' by Tsedal Neeley and Paul Leonardi; 'Learning to Work with Intelligent Machines,' by Matt Beane; 'Getting AI to Scale,' by Tim Fountaine, Brian McCarthy, and Tamim Saleh; 'Why You Aren't Getting More from Your Marketing AI,' by Eva Ascarza, Michael Ross, and Bruce G. S. Hardie; 'The Pitfalls of Pricing Algorithms,' by Marco Bertini and Oded Koenigsberg; 'A Smarter Strategy for Using Robots,' by Ben Armstrong and Julie Shah; 'Why You Need an AI Ethics Committee,' by Reid Blackman; 'Robots Need Us More Than We Need Them,' by H. James Wilson and Paul R. Daugherty; 'Stop Tinkering with AI,' by Thomas H. Davenport and Nitin Mittal; and 'ChatGPT Is a Tipping Point for AI,' by Ethan Mollick.HBR's 10 Must Reads paperback series is the definitive collection of books for new and experienced leaders alike. Leaders looking for the inspiration that big ideas provide, both to accelerate their own growth and that of their companies, should look no further. HBR's 10 Must Reads series focuses on the core topics that every ambitious manager needs to know: leadership, strategy, change, managing people, and managing yourself. Harvard Business Review has sorted through hundreds of articles and selected only the most essential reading on each topic. Each title includes timeless advice that will be relevant regardless of an everchanging business environment.\u003c\/p\u003e","brand":"Harvard Business Review Press","offers":[{"title":"Default Title","offer_id":46616322408693,"sku":"SONG1647825849","price":12.08,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/71y3rteWShL_11784de2-987f-464b-8d66-74c7df42f3a2.jpg?v=1759842354"},{"product_id":"the-ai-playbook-mastering-the-rare-art-of-machine-learning-deployment-management-on-the-cutting-edge-new","title":"The Ai Playbook: Mastering The Rare Art Of Machine Learning Deployment (Management On The Cutting Edge),New","description":"\u003cp\u003eIn his bestselling first book, Eric Siegel explained how machine learning works. Now, in The AI Playbook, he shows how to capitalize on it.'Eric Siegel delivers a robust primer on machine learning, the key mechanism in AI. 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With over 300 review questions, 50 handson exercises, templates, and hundreds of best practice tips to guide you through every step of the way, this book is a mustread for anyone seeking to accelerate AI transformation across their enterprise.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":46620386263285,"sku":"DADAX1394213050","price":68.14,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/81PgjtcV9SL.jpg?v=1746616382"},{"product_id":"enterprise-ai-in-the-cloud-a-practical-guide-to-deploying-endtoend-machine-learning-and-chatgpt-solutions-tech-today-used","title":"Enterprise Ai In The Cloud: A Practical Guide To Deploying Endtoend Machine Learning And Chatgpt Solutions (Tech Today),Used","description":"\u003cp\u003eEmbrace emerging AI trends and integrate your operations with cuttingedge solutionsEnterprise AI in the Cloud: A Practical Guide to Deploying EndtoEnd Machine Learning and ChatGPT Solutions is an indispensable resource for professionals and companies who want to bring new AI technologies like generative AI, ChatGPT, and machine learning (ML) into their suite of cloudbased solutions. If you want to set up AI platforms in the cloud quickly and confidently and drive your business forward with the power of AI, this book is the ultimate goto guide. The author shows you how to start an enterprisewide AI transformation effort, taking you all the way through to implementation, with clearly defined processes, numerous examples, and handson exercises. 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Flach provides case studies of increasing complexity and variety with wellchosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and stateoftheart topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. 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The book describes algorithms with code examples backed up by a website that provides working implementations in Python. The author uses data from a variety of applications to demonstrate the methods and includes practical problems for students to solve.Highlights a Range of Disciplines and ApplicationsDrawing from computer science, statistics, mathematics, and engineering, the multidisciplinary nature of machine learning is underscored by its applicability to areas ranging from finance to biology and medicine to physics and chemistry. 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For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. 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It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes all the way to the frontiers of the subject such as infinite mixture models, GPs, and MCMC.'Devdatt Dubhashi, Professor, Department of Computer Science and Engineering, Chalmers University, Sweden'This textbook manages to be easier to read than other comparable books in the subject while retaining all the rigorous treatment needed. The new chapters put it at the forefront of the field by covering topics that have become mainstream in machine learning over the last decade.'Daniel Barbara, George Mason University, Fairfax, Virginia, USA'The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing just in time the essential background on linear algebra, calculus, and probability theory that the reader needs to understand these concepts.'Daniel OrtizArroyo, Associate Professor, Aalborg University Esbjerg, Denmark'I was impressed by how closely the material aligns with the needs of an introductory course on machine learning, which is its greatest strengthOverall, this is a pragmatic and helpful book, which is wellaligned to the needs of an introductory course and one that I will be looking at for my own students in coming months.'David Clifton, University of Oxford, UK'The first edition of this book was already an excellent introductory text on machine learning for an advanced undergraduate or taught masters level course, or indeed for anybody who wants to learn about an interesting and important field of computer science. The additional chapters of advanced material on Gaussian process, MCMC and mixture modeling provide an ideal basis for practical projects, without disturbing the very clear and readable exposition of the basics contained in the first part of the book.'Gavin Cawley, Senior Lecturer, School of Computing Sciences, University of East Anglia, UK'This book could be used for junior\/senior undergraduate students or firstyear graduate students, as well as individuals who want to explore the field of machine learningThe book introduces not only the concepts but the underlying ideas on algorithm implementation from a critical thinking perspective.'Guangzhi Qu, Oakland University, Rochester, Michigan, USA\u003c\/p\u003e","brand":"CRC Press","offers":[{"title":"Default Title","offer_id":46649993527541,"sku":"SONG0367574640","price":44.43,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0737\/5804\/8501\/files\/81X9B0G4PLL_df9b8028-0359-456c-a5fb-a3d952237400.jpg?v=1747564023"},{"product_id":"machine-learning-and-knowledge-discovery-in-databases-european-conference-ecml-pkdd-2010-athens-greece-september-59-2011-used","title":"Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2010, Athens, Greece, September 59, 2011,,Used","description":"\u003cp\u003eThis threevolume set LNAI 6911, LNAI 6912, and LNAI 6913 constitutes the refereed proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2011, held in Athens, Greece, in September 2011. 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Noah Gift demystifies all the concepts and tools you need to get resultseven if you dont have a strong background in math or data science.Gift illuminates powerful offtheshelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, youll gain a more intuitive understanding of what you can achieve with them and how to maximize their value.Building on these fundamentals, youll walk stepbystep through building cloudbased AI\/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether youre a business professional, decisionmaker, student, or programmer, Gifts expert guidance and wideranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools youll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six realworld AI applications, from start to finishRegister your book for convenient access to downloads, updates, and\/or corrections as they become available. 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