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Author: Jürgen Weichenberger Publisher: BPB Publications ISBN: 9355519834 Category : Computers Languages : en Pages : 655
Book Description
DESCRIPTION Mastering AI and ML algorithms is essential for data scientists. This book covers a wide range of techniques, from supervised and unsupervised learning to deep learning and reinforcement learning. This book is a compass to the most important algorithms that every data scientist should have at their disposal when building a new AI/ML application. This book offers a thorough introduction to AI and ML, covering key concepts, data structures, and various algorithms like linear regression, decision trees, and neural networks. It explores learning techniques like supervised, unsupervised, and semi-supervised learning and applies them to real-world scenarios such as natural language processing and computer vision. With clear explanations, code examples, and detailed descriptions of 40 algorithms, including their mathematical foundations and practical applications, this resource is ideal for both beginners and experienced professionals looking to deepen their understanding of AI and ML. The final part of the book gives an outlook for more state-of-the-art algorithms that will have the potential to change the world of AI and ML fundamentals. KEY FEATURES ● Covers a wide range of AI and ML algorithms, from foundational concepts to advanced techniques. ● Includes real-world examples and code snippets to illustrate the application of algorithms. ● Explains complex topics in a clear and accessible manner, making it suitable for learners of all levels. WHAT YOU WILL LEARN ● Differences between supervised, unsupervised, and reinforcement learning. ● Gain expertise in data cleaning, feature engineering, and handling different data formats. ● Learn to implement and apply algorithms such as linear regression, decision trees, neural networks, and support vector machines. ● Creating intelligent systems and solving real-world problems. ● Learn to approach AI and ML challenges with a structured and analytical mindset. WHO THIS BOOK IS FOR This book is ideal for data scientists, ML engineers, and anyone interested in entering the world of AI. TABLE OF CONTENTS 1. Fundamentals 2. Typical Data Structures 3. 40 AI/ML Algorithms Overview 4. Basic Supervised Learning Algorithms 5. Advanced Supervised Learning Algorithms 6. Basic Unsupervised Learning Algorithms 7. Advanced Unsupervised Learning Algorithms 8. Basic Reinforcement Learning Algorithms 9. Advanced Reinforcement Learning Algorithms 10. Basic Semi-Supervised Learning Algorithms 11. Advanced Semi-Supervised Learning Algorithms 12. Natural Language Processing 13. Computer Vision 14. Large-Scale Algorithms 15. Outlook into the Future: Quantum Machine Learning
Author: Jürgen Weichenberger Publisher: BPB Publications ISBN: 9355519834 Category : Computers Languages : en Pages : 655
Book Description
DESCRIPTION Mastering AI and ML algorithms is essential for data scientists. This book covers a wide range of techniques, from supervised and unsupervised learning to deep learning and reinforcement learning. This book is a compass to the most important algorithms that every data scientist should have at their disposal when building a new AI/ML application. This book offers a thorough introduction to AI and ML, covering key concepts, data structures, and various algorithms like linear regression, decision trees, and neural networks. It explores learning techniques like supervised, unsupervised, and semi-supervised learning and applies them to real-world scenarios such as natural language processing and computer vision. With clear explanations, code examples, and detailed descriptions of 40 algorithms, including their mathematical foundations and practical applications, this resource is ideal for both beginners and experienced professionals looking to deepen their understanding of AI and ML. The final part of the book gives an outlook for more state-of-the-art algorithms that will have the potential to change the world of AI and ML fundamentals. KEY FEATURES ● Covers a wide range of AI and ML algorithms, from foundational concepts to advanced techniques. ● Includes real-world examples and code snippets to illustrate the application of algorithms. ● Explains complex topics in a clear and accessible manner, making it suitable for learners of all levels. WHAT YOU WILL LEARN ● Differences between supervised, unsupervised, and reinforcement learning. ● Gain expertise in data cleaning, feature engineering, and handling different data formats. ● Learn to implement and apply algorithms such as linear regression, decision trees, neural networks, and support vector machines. ● Creating intelligent systems and solving real-world problems. ● Learn to approach AI and ML challenges with a structured and analytical mindset. WHO THIS BOOK IS FOR This book is ideal for data scientists, ML engineers, and anyone interested in entering the world of AI. TABLE OF CONTENTS 1. Fundamentals 2. Typical Data Structures 3. 40 AI/ML Algorithms Overview 4. Basic Supervised Learning Algorithms 5. Advanced Supervised Learning Algorithms 6. Basic Unsupervised Learning Algorithms 7. Advanced Unsupervised Learning Algorithms 8. Basic Reinforcement Learning Algorithms 9. Advanced Reinforcement Learning Algorithms 10. Basic Semi-Supervised Learning Algorithms 11. Advanced Semi-Supervised Learning Algorithms 12. Natural Language Processing 13. Computer Vision 14. Large-Scale Algorithms 15. Outlook into the Future: Quantum Machine Learning
Author: Imran Ahmad Publisher: Packt Publishing Ltd ISBN: 178980986X Category : Computers Languages : en Pages : 374
Book Description
Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptography Key Features Learn the techniques you need to know to design algorithms for solving complex problems Become familiar with neural networks and deep learning techniques Explore different types of algorithms and choose the right data structures for their optimal implementation Book DescriptionAlgorithms have always played an important role in both the science and practice of computing. Beyond traditional computing, the ability to use algorithms to solve real-world problems is an important skill that any developer or programmer must have. This book will help you not only to develop the skills to select and use an algorithm to solve real-world problems but also to understand how it works. You’ll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, such as searching and sorting, with the help of practical examples. As you advance to a more complex set of algorithms, you'll learn about linear programming, page ranking, and graphs, and even work with machine learning algorithms, understanding the math and logic behind them. Further on, case studies such as weather prediction, tweet clustering, and movie recommendation engines will show you how to apply these algorithms optimally. Finally, you’ll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learn Explore existing data structures and algorithms found in Python libraries Implement graph algorithms for fraud detection using network analysis Work with machine learning algorithms to cluster similar tweets and process Twitter data in real time Predict the weather using supervised learning algorithms Use neural networks for object detection Create a recommendation engine that suggests relevant movies to subscribers Implement foolproof security using symmetric and asymmetric encryption on Google Cloud Platform (GCP) Who this book is for This book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most commonly used algorithms in a clear and concise way or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Although Python programming experience is a must, knowledge of data science will be helpful but not necessary.
Author: Rafael A. Irizarry Publisher: CRC Press ISBN: 1000708039 Category : Mathematics Languages : en Pages : 836
Book Description
Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert.
Author: Dirk P. Kroese Publisher: CRC Press ISBN: 1000730778 Category : Business & Economics Languages : en Pages : 538
Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author: Avrim Blum Publisher: Cambridge University Press ISBN: 1108617360 Category : Computers Languages : en Pages : 433
Book Description
This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Author: Pedro Domingos Publisher: Basic Books ISBN: 0465061923 Category : Computers Languages : en Pages : 354
Book Description
Recommended by Bill Gates A thought-provoking and wide-ranging exploration of machine learning and the race to build computer intelligences as flexible as our own In the world's top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Master Algorithm, Pedro Domingos lifts the veil to give us a peek inside the learning machines that power Google, Amazon, and your smartphone. He assembles a blueprint for the future universal learner--the Master Algorithm--and discusses what it will mean for business, science, and society. If data-ism is today's philosophy, this book is its bible.
Author: Joel Grus Publisher: "O'Reilly Media, Inc." ISBN: 1491904399 Category : Computers Languages : en Pages : 336
Book Description
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases
Author: John W. Foreman Publisher: John Wiley & Sons ISBN: 1118839862 Category : Business & Economics Languages : en Pages : 432
Book Description
Data Science gets thrown around in the press like it'smagic. Major retailers are predicting everything from when theircustomers are pregnant to when they want a new pair of ChuckTaylors. It's a brave new world where seemingly meaningless datacan be transformed into valuable insight to drive smart businessdecisions. But how does one exactly do data science? Do you have to hireone of these priests of the dark arts, the "data scientist," toextract this gold from your data? Nope. Data science is little more than using straight-forward steps toprocess raw data into actionable insight. And in DataSmart, author and data scientist John Foreman will show you howthat's done within the familiar environment of aspreadsheet. Why a spreadsheet? It's comfortable! You get to look at the dataevery step of the way, building confidence as you learn the tricksof the trade. Plus, spreadsheets are a vendor-neutral place tolearn data science without the hype. But don't let the Excel sheets fool you. This is a book forthose serious about learning the analytic techniques, the math andthe magic, behind big data. Each chapter will cover a different technique in aspreadsheet so you can follow along: Mathematical optimization, including non-linear programming andgenetic algorithms Clustering via k-means, spherical k-means, and graphmodularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, andbag-of-words models Forecasting, seasonal adjustments, and prediction intervalsthrough monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through eachtechnique. But never fear, the topics are readily applicable andthe author laces humor throughout. You'll even learnwhat a dead squirrel has to do with optimization modeling, whichyou no doubt are dying to know.
Author: John Paul Mueller Publisher: John Wiley & Sons ISBN: 1119330491 Category : Computers Languages : en Pages : 434
Book Description
Discover how algorithms shape and impact our digital world All data, big or small, starts with algorithms. Algorithms are mathematical equations that determine what we see—based on our likes, dislikes, queries, views, interests, relationships, and more—online. They are, in a sense, the electronic gatekeepers to our digital, as well as our physical, world. This book demystifies the subject of algorithms so you can understand how important they are business and scientific decision making. Algorithms for Dummies is a clear and concise primer for everyday people who are interested in algorithms and how they impact our digital lives. Based on the fact that we already live in a world where algorithms are behind most of the technology we use, this book offers eye-opening information on the pervasiveness and importance of this mathematical science—how it plays out in our everyday digestion of news and entertainment, as well as in its influence on our social interactions and consumerism. Readers even learn how to program an algorithm using Python! Become well-versed in the major areas comprising algorithms Examine the incredible history behind algorithms Get familiar with real-world applications of problem-solving procedures Experience hands-on development of an algorithm from start to finish with Python If you have a nagging curiosity about why an ad for that hammock you checked out on Amazon is appearing on your Facebook page, you'll find Algorithm for Dummies to be an enlightening introduction to this integral realm of math, science, and business.
Author: Foster Provost Publisher: "O'Reilly Media, Inc." ISBN: 144937428X Category : Computers Languages : en Pages : 506
Book Description
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making. Understand how data science fits in your organization—and how you can use it for competitive advantage Treat data as a business asset that requires careful investment if you’re to gain real value Approach business problems data-analytically, using the data-mining process to gather good data in the most appropriate way Learn general concepts for actually extracting knowledge from data Apply data science principles when interviewing data science job candidates
Author: Jürgen Weichenberger
Author: Jürgen Weichenberger
Author: Imran Ahmad
Author: Rafael A. Irizarry
Author: Dirk P. Kroese
Author: Avrim Blum
Author: Pedro Domingos
Author: Joel Grus
Author: John W. Foreman
Author: John Paul Mueller
Author: Foster Provost