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A Practical Guide to Artificial Intelligence and Data Analytics

Author : Rayan Wali

Publisher : Rayan Wali

Page : 605 pages

File Size : 10,36 MB

Release : 2021-06-12

Category : Computers

ISBN :

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Book Description

Whether you are looking to prepare for AI/ML/Data Science job interviews or you are a beginner in the field of Data Science and AI, this book is designed for engineers and AI enthusiasts like you at all skill levels. Taking a different approach from a traditional textbook style of instruction, A Practical Guide to AI and Data Analytics touches on all of the fundamental topics you will need to understand deeper into machine learning and artificial intelligence research, literature, and practical applications with its four parts: Part I: Concept Instruction Part II: 8 Full-Length Case Studies Part III: 50+ Mixed Exercises Part IV: A Full-Length Assessment With an illustrative approach to instruction, worked examples, and case studies, this easy-to-understand book simplifies many of the AI and Data Analytics key concepts, leading to an improvement of AI/ML system design skills.



A Practical Guide to AI and Data Analytics

Author : Rayan Wali

Publisher :

Page : 423 pages

File Size : 34,95 MB

Release : 2022-01-05

Category :

ISBN :

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Book Description

Whether you are looking to prepare for AI/ML/Data Science job interviews or you are a beginner in the field of Data Science and AI, this book is designed for engineers and AI enthusiasts like you at all skill levels. Taking a different approach from a traditional textbook style of instruction, A Practical Guide to AI and Data Analytics touches on all of the fundamental topics you will need to understand deeper into machine learning and artificial intelligence research, literature, and practical applications with its three parts: Part I: A Conceptual (and Visual) Illustration [topics including, but not limited to, are listed below] Fundamentals of Data Science The Data and Machine Learning Pipelines Data Preprocessing + Worked Data Preprocessing Strategy Data Visualization Python for Data Analysis Calculus & Linear Algebra Fundamentals Data Structures and Algorithms Exercises Machine Learning Models & Algorithms (kNN, Neural Networks, Hidden Markov Models, Ensemble Methods, etc.) Deep Learning for Computer Vision & NLP (CNNs, RNNs, etc.) Data Mining Model Deployment Time Series Data Analysis AI Systems in the Real-World Applications of Data Analysis Exercises Database Systems & Cloud Computing (with practical example) Functional Programming for Data Analytics Part II: 10 Full-Length Case Studies Case Study I: Sports Web Scraping Case Study II: NLP Textual Analysis Case Study III: Emergency Response Duration Analysis Case Study IV: MNIST Image Classification Case Study V: COVID-19 Statistical Data Analysis Case Study VI: COVID-19 Chest X-Ray Screening Case Study VII: Signal Strength Geospatial Analysis Case Study VIII: NYC Crash Accidents Data Analysis Case Study IX: Sales Forecasting Case Study X: Meteorite Landings Analysis Part III: A Full-Length Data Science and Analytics Skills Assessment (DSSA) With exercises that span a wide range of AI problems from different domains, from the economics and finance to transportation and medical industries, the DSSA aims to provide a comprehensive assessment to measure your understanding through cleverly-designed AI reasoning, problem-solving, and scenario-based exercises, whether you use it to enhance your understanding in the AI and Data Analytics field or use it to prepare for your AI/Data Analytics problem solving and system design interviews. Section I: 60 Multiple-Choice and Short-Answer Exercises Section II: 5 AI & Data Analytics Problem Solving and Coding Exercises Solutions to Sections I and II are included With an illustrative approach to instruction, worked examples, and case studies, this easy-to-understand book simplifies many of the AI and Data Analytics key concepts, leading to an improvement of AI/ML system design skills.



Intelligent Systems for Engineers and Scientists

Author : Adrian A. Hopgood

Publisher : CRC Press

Page : 455 pages

File Size : 12,73 MB

Release : 2012-02-02

Category : Computers

ISBN : 1466516178

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Book Description

The third edition of this bestseller examines the principles of artificial intelligence and their application to engineering and science, as well as techniques for developing intelligent systems to solve practical problems. Covering the full spectrum of intelligent systems techniques, it incorporates knowledge-based systems, computational intelligence, and their hybrids. Using clear and concise language, Intelligent Systems for Engineers and Scientists, Third Edition features updates and improvements throughout all chapters. It includes expanded and separated chapters on genetic algorithms and single-candidate optimization techniques, while the chapter on neural networks now covers spiking networks and a range of recurrent networks. The book also provides extended coverage of fuzzy logic, including type-2 and fuzzy control systems. Example programs using rules and uncertainty are presented in an industry-standard format, so that you can run them yourself. The first part of the book describes key techniques of artificial intelligence—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), frames, objects, agents, symbolic learning, case-based reasoning, genetic algorithms, optimization algorithms, neural networks, hybrids, and the Lisp and Prolog languages. The second part describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. The author provides sufficient detail to help you develop your own intelligent systems for real applications. Whether you are building intelligent systems or you simply want to know more about them, this book provides you with detailed and up-to-date guidance. Check out the significantly expanded set of free web-based resources that support the book at: http://www.adrianhopgood.com/aitoolkit/



Data Analytics for Absolute Beginners: a Deconstructed Guide to Data Literacy

Author : Oliver Theobald

Publisher :

Page : 88 pages

File Size : 30,74 MB

Release : 2019-07-21

Category :

ISBN : 9781081762469

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Book Description

While exposure to data has become more or less a daily ritual for the rank-and-file knowledge worker, true understanding-treated in this book as data literacy-resides in knowing what lies behind the data. Everything from the data's source to the specific choice of input variables, algorithmic transformations, and visual representation shape the accuracy, relevance, and value of the data and mark its journey from raw data to business insight. It's also important to grasp the terminology and basic concepts of data analytics as much as it is to have the financial literacy to be successful as a decisionmaker in the business world. In this book, we make sense of data analytics without the assumption that you understand specific data science terminology or advanced programming languages to set you on your path. Topics covered in this book: Data Mining Big Data Machine Learning Alternative Data Data Management Web Scraping Regression Analysis Clustering Analysis Association Analysis Data Visualization Business Intelligence



A Practical Guide to Data Mining for Business and Industry

Author : Andrea Ahlemeyer-Stubbe

Publisher : John Wiley & Sons

Page : 323 pages

File Size : 10,53 MB

Release : 2014-03-31

Category : Mathematics

ISBN : 1118763378

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Book Description

Data mining is well on its way to becoming a recognized discipline in the overlapping areas of IT, statistics, machine learning, and AI. Practical Data Mining for Business presents a user-friendly approach to data mining methods, covering the typical uses to which it is applied. The methodology is complemented by case studies to create a versatile reference book, allowing readers to look for specific methods as well as for specific applications. The book is formatted to allow statisticians, computer scientists, and economists to cross-reference from a particular application or method to sectors of interest.



Artificial Intelligence for Big Data

Author : Anand Deshpande

Publisher : Packt Publishing Ltd

Page : 371 pages

File Size : 27,68 MB

Release : 2018-05-22

Category : Computers

ISBN : 1788476018

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Book Description

Build next-generation Artificial Intelligence systems with Java Key Features Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Book Description In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. What you will learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms Who this book is for This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus.



Deep Learning Illustrated

Author : Jon Krohn

Publisher : Addison-Wesley Professional

Page : 725 pages

File Size : 42,20 MB

Release : 2019-08-05

Category : Computers

ISBN : 0135121728

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Book Description

"The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.



Python for Data Science

Author : Erick Thompson

Publisher :

Page : 266 pages

File Size : 19,2 MB

Release : 2020-10-30

Category : Computers

ISBN : 9781801547994

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Making Sense of Data I

Author : Glenn J. Myatt

Publisher : John Wiley & Sons

Page : 262 pages

File Size : 25,96 MB

Release : 2014-07-02

Category : Mathematics

ISBN : 1118422104

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Book Description

Praise for the First Edition “...a well-written book on data analysis and data mining that provides an excellent foundation...” —CHOICE “This is a must-read book for learning practical statistics and data analysis...” —Computing Reviews.com A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study. In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features: Updated exercises for both manual and computer-aided implementation with accompanying worked examples New appendices with coverage on the freely available TraceisTM software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches Additional real-world examples of data preparation to establish a practical background for making decisions from data Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.



Data Mining

Author : Ian H. Witten

Publisher : Elsevier

Page : 665 pages

File Size : 37,10 MB

Release : 2011-02-03

Category : Computers

ISBN : 0080890369

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Book Description

Data 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 real-world 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, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true 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&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level 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 tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization