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Machine Learning: A Probabilistic Perspective (Adaptive Computation And Machine Learning Series) Boo [VERIFIED]


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Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.


One of the most popular machine learning books. It covers a great variety of algorithms (from supervised to unsupervised learning) and it does so by analyzing the concepts and the intuition behind them. It requires a strong statistical and mathematical background but if you want to learn how the algorithms work behind the scenes, then this is your book.


This best seller manages to reduce all of machine learning to 100 pages. The author tried to include only the most important concepts but on the same time to help you understand complex topics, pass your AI interview and start a business. A great introductory book in the world of Machine Learning.


If you ask an expert in Computer Vision to suggest you a book, it will be probably be that one. Not very machine learning focused, but necessary to learn the basic principles and concepts behind Computer Vision.


Another great Computer Vision read, which takes a more modern approach by exploring different machine learning techniques used in the field. It requires very little prerequisites and is suited for both practiotioners and researchers.


"This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach. The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package--PMTK (probabilistic modeling toolkit)--that is freely available online"--Back cover.


Artificial Intelligence (AI) and Machine Learning (ML) technologies have become key innovation accelerators for organizations looking for that extra edge. And with machine learning skills being in high demand, theres a surge in interest in this field. Machine Learning books are a great starting point for enthusiasts who want to transition to these in-demand roles. In this article we list down top machine learning books to get you started on ML journey.


The increased usage of machine learning in enterprises has driven up the need for skilled professionals. Machine learning models serve up Netflix recommendations, Facebooks News Feed leverages machine learning to drum up personalized content, and Twitter utilizes machine learning to rank tweets and boost engagements. Infact, anything that dishes out personalized feeds is driven by machine learning. The evolving field has given rise to new job roles such as machine learning engineers and data scientists.


According to SlashData, 45% of developers want to learn or improveOpens a new window their machine learning skills, and informative books and case studies serve us a good starting point for enhancing knowledge. We list down 15 books on machine learning that will help beginners, intermediate users, and advanced machine learning researchers broaden their understanding about the concept and learn from practical case studies.


In this book, Oliver Theobald introduces enthusiasts with no prior coding experience to the practical components and statistical concepts in machine learning. Core algorithms in the book are accompanied by plain-English explanations and visual examples and readers are also taught about concepts such as Cross Validation, Ensemble Modeling, Grid Search, Feature Engineering, and One-hot Encoding.


Who should read the book: While the book is aimed at beginners, with no coding experience, it is also a handy guide for machine learning researchers and engineers. The book will also help sharpen knowledge about Regression Analysis, how to create trend lines, data scrubbing techniques and using Decision Trees to decode classification. The book also covers clustering techniques, largely used to build machine learning models for price predictions using Python, as well as artificial neural networks.


A veteran of over half a dozen books on machine learning, Scott Chesterton brings together the basic aspects of machine learning in this book, such as popular machine learning frameworks being used, machine learning algorithms, evaluation systems, data mining, and other common applications of machine learning. The book features commentaries on machine learning software such as TensorFlow, Reptilian, Logstash, Elasticsearch, Installing Marvel, Bro, HDFS, HBASE, Syslog, SNMP, messaging layer and real-time processing layer.


One of the most-read books in Artificial Intelligence and machine learning space, this handy guide by Aurelein Geron is a must-read for data scientists and machine learning enthusiasts looking for practical examples on how to implement ML tools.


This book by Scott Chesterton is not a long read or may not contain advanced coding examples, but acts as a good theoretical resource on how to operationalize AI and ML projects, how ML tools and techniques can be best utilized to process big data, and how to visualize a predictive models analytical results. The book is aimed at intermediate-level users who are familiar with machine learning tools, frameworks, and techniques.


Who should read the book: This book will be most useful for machine learning engineers and analytics managers at organizations who are looking to develop new AI and ML projects to spur business growth or to build their enterprise strategy. Through this book, Chesterton introduces readers to machine learning projects and how they can be used to improve an organizations capabilities and competitiveness and how machine learning teams can prepare for new challenges when deploying machine learning at scale.


While the initial chapters of Brett Lantz book feature plenty of information for beginners on machine learning, understanding how machines learn, and conducting machine learning with R, the narrative soon graduates to more technical aspects with information on practical applications.


While not really a tutorial on machine learning algorithms, frameworks, or techniques, The Algorithmic Leader by well-known futuristic Mike Walsh is a handy guide for industry leaders, business heads, and IT decision-makers. The book contains 10 principles of attaining success in the Algorithmic Age and these lessons are drawn out from case studies and interviews with AI pioneers, experienced data scientists, and top business leaders.


Who should read the book: Business leaders and industry veterans can use the book to understand the evolution and future of concepts like decentralization, digital disruption, probabilistic thinking, ethics, and machine learning and learn how to use these concepts for purposes such as problem-solving and decision making. It can also be used as a handy guide for laying down roadmaps for next-gen technologies in organizations.


The winner of the 2014 Technometrics Ziegel Prize for Outstanding Book, Max Kuhns book is an essential part of graduate level predictive modeling courses, offering machine learning researchers and practitioners a way to gain an understanding about the overall predictive modeling process, including data preprocessing, data splitting, and model tuning.


Dubbed as the only comprehensive book on the subject by well-known machine learning academicians Ian Goodfellow, Yoshua Bengio and Aaron Courville, the book offers advanced machine learning scientists and developers a lowdown on widely-used deep learning techniques such as deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology.


Who should read the book: Aimed at data scientists, the book can help data scientists and ML practitioners sharpen their understanding of topics like linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning are also covered for the benefit of readers.


Who should read the book: The book has been written for people pursuing advanced courses in machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics and will give readers an in-depth understanding of several approximate inference algorithms such as variational Bayes and expectation propagation. It also contains exercises that cover all difficulty levels to help readers test their understanding of pattern recognition.


Who should read the book: The book also delves into concepts such as probability, optimization, and linear algebra to give readers an understanding of the underlying mathematics that powers the development of new ML tools and techniques. According to the author, the book offers a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach. The book serves as a handy guide to practitioners and business leaders on the latest developments in the field. 153554b96e






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