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Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques / Sunila Gollapudi

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Community experience distilledPublisher: Birmingham, UK : Packt Publishing, 2016Description: 1 online resource (1 volume)ISBN:
  • 9781784394011
Subject(s): Additional physical formats: 9781784399689 | Erscheint auch als: Practical machine learning. Birmingham : Packt Publishing, 2016. xvi, 433 SeitenDDC classification:
  • 006.31
RVK: RVK: ST 300 | ST 302LOC classification:
  • Q325.5
Online resources: Summary: Cover -- Copyright -- Credits -- Foreword -- About the Author -- Acknowledgments -- About the Reviewers -- www.PacktPub.com -- Preface -- Chapter 1: Introduction to Machine learning -- Machine learning -- Definition -- Core Concepts and Terminology -- What is learning? -- Data -- Labeled and unlabeled data -- Tasks -- Algorithms -- Models -- Data and inconsistencies in Machine learning -- Under-fitting -- Over-fitting -- Data instability -- Unpredictable data formats -- Practical Machine learning examples -- Types of learning problems -- Classification -- Clustering -- Forecasting, prediction or regression -- Simulation -- Optimization -- Supervised learning -- Unsupervised learning -- Semi-supervised learning -- Reinforcement learning -- Deep learning -- Performance measures -- Is the solution good? -- Mean squared error (MSE) -- Mean absolute error (MAE) -- Normalized MSE and MAE (NMSE and NMAE) -- Solving the errors: bias and variance -- Some complementing fields of Machine learning -- Data mining -- Artificial intelligence (AI) -- Statistical learning -- Data science -- Machine learning process lifecycle and solution architecture -- Machine learning algorithms -- Decision tree based algorithms -- Bayesian method based algorithms -- Kernel method based algorithms -- Clustering methods -- Artificial neural networks (ANN) -- Dimensionality reduction -- Ensemble methods -- Instance based learning algorithms -- Regression analysis based algorithms -- Association rule based learning algorithms -- Machine learning tools and frameworks -- Summary -- Chapter 2: Machine learning and Large-scale datasets -- Big data and the context of large-scale Machine learning -- Functional versus Structural - A methodological mismatch -- Commoditizing information -- Theoretical limitations of RDBMS -- Scaling-up versus Scaling-out storage.PPN: PPN: 1657549860Package identifier: Produktsigel: ZDB-26-MYL | ZDB-30-PQE
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