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Scaling Up Machine Learning
This integrated collection covers a range of parallelization platforms, concurrent programming frameworks and machine learning settings, with case studies.
Data Science for Business
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 participa...
Analyzing Analytics
This book aims to achieve the following goals: (1) to provide a high-level survey of key analytics models and algorithms without going into mathematical details; (2) to analyze the usage patterns of these models; and (3) to discuss opportunities for accelerating analytics workloads using software, hardware, and system approaches. The book first describes 14 key analytics models (exemplars) that span data mining, machine learning, and data management domains. For each analytics exemplar, we summarize its computational and runtime patterns and apply the information to evaluate parallelization and acceleration alternatives for that exemplar. Using case studies from important application domains...
Machine Learning and Knowledge Discovery in Databases
This book constitutes the refereed proceedings of the joint conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2008, held in Antwerp, Belgium, in September 2008. The 100 papers presented in two volumes, together with 5 invited talks, were carefully reviewed and selected from 521 submissions. In addition to the regular papers the volume contains 14 abstracts of papers appearing in full version in the Machine Learning Journal and the Knowledge Discovery and Databases Journal of Springer. The conference intends to provide an international forum for the discussion of the latest high quality research results in all areas related to machine learning and knowledge discovery in databases. The topics addressed are application of machine learning and data mining methods to real-world problems, particularly exploratory research that describes novel learning and mining tasks and applications requiring non-standard techniques.
Data Clustering
Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabi...
Generative AI Fundamentals and Interview Guide
"Generative AI Fundamentals and Interview Preparation" is your gateway to mastering the exciting world of Generative AI and Large LanguageModels. A comprehensive guide for IT professionals, data scientists, software engineers, solution architects, and AI researchers, this book covers foundational to advanced Generative AI concepts while preparing you for interviews in this dynamic field. Explore key interview questions, delve into the mechanics of Transformers, BERT, Llama and GPT models, and learn to build cutting-edge LLM applications using Hugging Face. Gain insights into embeddings, vector databases, fine-tuning techniques, basic and advanced RAG, LangChain, LlamaIndex, Multimodal AI and evaluation metrics to master the transformative power of AI
High-Performance Big Data Computing
An in-depth overview of an emerging field that brings together high-performance computing, big data processing, and deep lLearning. Over the last decade, the exponential explosion of data known as big data has changed the way we understand and harness the power of data. The emerging field of high-performance big data computing, which brings together high-performance computing (HPC), big data processing, and deep learning, aims to meet the challenges posed by large-scale data processing. This book offers an in-depth overview of high-performance big data computing and the associated technical issues, approaches, and solutions. The book covers basic concepts and necessary background knowledge, ...
User Modeling, Adaptation, and Personalization
This book constitutes the proceedings of the First International Conference on User Modeling, Adaptation, and Personalization, held in Trento, Italy, on June 22-26, 2009. This annual conference was merged from the biennial conference series User Modeling, UM, and the conference on Adaptive Hypermedia and Adaptive Web-Based Systems, AH. The 53 papers presented together with 3 invited talks were carefully reviewed and selected from 125 submissions. The tutorials and workshops were organized in topical sections on constraint-based tutoring systems; new paradigms for adaptive interaction; adaption and personalization for Web 2.0; lifelong user modelling; personalization in mobile and pervasive computing; ubiquitous user modeling; user-centred design and evaluation of adaptive systems.
Data Scientist Diploma (master's level) - City of London College of Economics - 6 months - 100% online / self-paced
Overview This diploma course covers all aspects you need to know to become a successful Data Scientist. Content - Getting Started with Data Science - Data Analytic Thinking - Business Problems and Data Science Solutions - Introduction to Predictive Modeling: From Correlation to Supervised Segmentation - Fitting a Model to Data - Overfitting and Its Avoidance - Similarity, Neighbors, and Clusters Decision Analytic Thinking I: What Is a Good Model? - Visualizing Model Performance - Evidence and Probabilities - Representing and Mining Text - Decision Analytic Thinking II: Toward Analytical Engineering - Other Data Science Tasks and Techniques - Data Science and Business Strategy - Machine Learning: Learning from Data with Your Machine. - And much more Duration 6 months Assessment The assessment will take place on the basis of one assignment at the end of the course. Tell us when you feel ready to take the exam and we’ll send you the assignment questions. Study material The study material will be provided in separate files by email / download link.
Advances in Information Retrieval
This book constitutes the proceedings of the 34th European Conference on IR Research, ECIR 2012, held in Barcelona, Spain, in April 2012. The 37 full papers, 28 poster papers and 7 demonstrations presented in this volume were carefully reviewed and selected from 167 submissions. The contributions are organized in sections named: query representation; blogs and online-community search; semi-structured retrieval; evaluation; applications; retrieval models; image and video retrieval; text and content classification, categorisation, clustering; systems efficiency; industry track; and posters.
