Designing machine learning systems with Python : design efficient machine learning systems that give you more accurate results /


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Bibliographic Details
Main Author: Julian, David (Author)
Format: eBook
Published: Birmingham, UK : Packt Publishing, 2016.
Series:Community experience distilled.
Online Access:CONNECT
Table of Contents:
  • Cover; Copyright; Credits; About the Author; About the Reviewer;; Table of Contents; Preface; Chapter 1: Thinking in Machine Learning; The human interface; Design principles; Types of questions; Are you asking the right question?; Tasks; Classification; Regression; Clustering; Dimensionality reduction; Errors; Optimization; Linear programming; Models; Features; Unified modeling language; Class diagrams; Object diagrams; Activity diagrams; State diagrams; Summary; Chapter 2: Tools and Techniques; Python for machine learning; IPython console; Installing the SciPy stack; NumPY
  • Constructing and transforming arraysMathematical operations; Matplotlib; Pandas; SciPy; Scikit-learn; Summary; Chapter 3: Turning Data into Information; What is data?; Big data; Challenges of big data; Data volume; Data velocity; Data variety; Data models; Data distributions; Data from databases; Data from the Web; Data from natural language; Data from images; Data from application programming interfaces; Signals; Data from sound; Cleaning data; Visualizing data; Summary; Chapter 4: Models
  • Learning from Information; Logical models; Generality ordering; Version space; Coverage space
  • PAC learning and computational complexityTree models; Purity; Rule models; The ordered list approach; Set-based rule models; Summary; Chapter 5: Linear Models; Introducing least squares; Gradient descent; The normal equation; Logistic regression; The Cost function for logistic regression; Multiclass classification; Regularization; Summary; Chapter 6: Neural Networks; Getting started with neural networks; Logistic units; Cost function; Minimizing the cost function; Implementing a neural network; Gradient checking; Other neural net architectures; Summary
  • Chapter 7: Features
  • How Algorithms See the WorldFeature types; Quantitative features; Ordinal features; Categorical features; Operations and statistics; Structured features; Transforming features; Discretization; Normalization; Calibration; Principle component analysis; Summary; Chapter 8: Learning with Ensembles; Ensemble types; Bagging; Random forests; Extra trees; Boosting; Adaboost; Gradient boosting; Ensemble strategies; Other methods; Summary; Chapter 9: Design Strategies and Case Studies; Evaluating model performance; Model selection; Gridsearch; Learning curves
  • Real-world case studiesBuilding a recommender system; Content-based filtering; Collaborative filtering; Reviewing the case study; Insect detection in greenhouses; Reviewing the case study; Machine learning at a glance; Summary; Index