Adarsha (243 Ergebnisse)

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Paperback. Zustand: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.…

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Paperback. Zustand: New. This book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive  integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book,you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average),  ARMA (autoregressive moving-average) and ARIMA (autoregressive  integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.…

Paperback. Zustand: New. This book will teach you how to build recommender systems with machine learning algorithms using Python. Recommender systems have become an essential part of every internet-based business today.You'll start by learning basic concepts of recommender systems, with an overview of different types of recommender engines and how they function. Next, you will see how to build recommender systems with traditional algorithms such as market basket analysis and content- and knowledge-based recommender systems with NLP. The authors then demonstrate techniques such as collaborative filtering using matrix factorization and hybrid recommender systems that incorporate both content-based and collaborative filtering techniques. This is followed by a tutorial on building machine learning-based recommender systems using clustering and classification algorithms like K-means and random forest. The last chapters cover NLP, deep learning, and graph-based techniques to build a recommender engine. Each chapter includes data preparation, multiple ways to evaluate and optimize the recommender systems, supporting examples, and illustrations.By the end of this book, you will understand and be able to build recommender systems with various tools and techniques with machine learning, deep learning, and graph-based algorithms.What You Will LearnUnderstand and implement different recommender systems techniques with PythonEmploy popular methods like content- and knowledge-based, collaborative filtering, market basket analysis, and matrix factorization Build hybrid recommender systems that incorporate both content-based and collaborative filteringLeverage machine learning, NLP, and deep learning for building recommender systemsWho This Book Is ForData scientists, machine learning engineers, and Python programmers interested in building and implementing recommender systems to solve problems.…

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Paperback. Zustand: New. Gain a working knowledge of prescriptive AI, its history, and its current and future trends. This book will help you evaluate different AI-driven predictive analytics techniques and help you incorporate decision intelligence into your business workflow through real-world examples.The book kicks off with an introduction to decision intelligence and provides insight into prescriptive AI and how it can be woven into various business strategies and frameworks. You'll then be introduced to different decision intelligence methodologies and how to implement them, along with advantages and limitations of each. Digging deeper, the authors then walk you through how to perform simulations and interpret the results. A full chapter is devoted to embedding decision intelligence processes and outcomes into your business workflow using various applications. The book concludes by exploring different cognitive biases humans are prone to, and how those biasescan be eliminated by combining machine and human intelligence.Upon completing this book, you will understand prescriptive AI, tools, and techniques and will be ready to incorporate them into your business workflow.What You Will LearnImplement full-fledged decision intelligence applications using PythonLeverage the tools, techniques, and methodologies for prescriptive AIUnderstand how prescriptive AI can be used in different domains through practical examplesInterpret results and integrate them into your decision makingWho This Book Is ForData Scientists and Machine Learning Engineers, as well as business professionals who want to understand how AI-driven decision intelligence can help grow their business.…

paperback. Zustand: Acceptable. The innre rhinges have come unglued. The copy shows minor external wear, but is in otherwise very good condition.

paperback. Zustand: Good. Some shelf wear.

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