Thoughtful Machine Learning with Python. A Test-Driven Approach Matthew Kirk

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Autor:: Matthew Kirk
Wydawnictwo:: O'Reilly Media (Z chęcią przeczytam książkę w języku polskim)
Ocena:: Bądź pierwszym, który oceni tę książkę
Stron:: 220
Dostępne formaty:: ePub

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Thoughtful Machine Learning with Python. A Test-Driven Approach Matthew Kirk

Python w uczeniu maszynowym Matthew Kirk

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Gain the confidence you need to apply machine learning in your daily work. With this practical guide, author Matthew Kirk shows you how to integrate and test machine learning algorithms in your code, without the academic subtext.

Featuring graphs and highlighted code examples throughout, the book features tests with Python’s Numpy, Pandas, Scikit-Learn, and SciPy data science libraries. If you’re a software engineer or business analyst interested in data science, this book will help you:

Reference real-world examples to test each algorithm through engaging, hands-on exercises
Apply test-driven development (TDD) to write and run tests before you start coding
Explore techniques for improving your machine-learning models with data extraction and feature development
Watch out for the risks of machine learning, such as underfitting or overfitting data
Work with K-Nearest Neighbors, neural networks, clustering, and other algorithms

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Szczegóły książki

ISBN Ebooka:: 978-14-919-2408-2, 9781491924082
Data wydania ebooka :: 2017-01-16 Data wydania ebooka często jest dniem wprowadzenia tytułu do sprzedaży i może nie być równoznaczna z datą wydania książki papierowej. Dodatkowe informacje możesz znaleźć w darmowym fragmencie. Jeśli masz wątpliwości skontaktuj się z nami sklep@helion.pl.
Język publikacji:: angielski
Rozmiar pliku ePub:: 6.8MB
Rozmiar pliku Mobi:: 14.2MB

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Kategorie:
Programowanie » Python - Programowanie
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Spis treści książki

Preface
- Conventions Used in This Book
- Using Code Examples
- OReilly Safari
- How to Contact Us
- Acknowledgments
1. Probably Approximately Correct Software
- Writing Software Right
  - SOLID
    - Single Responsibility Principle
    - Open/Closed Principle
    - Liskov Substitution Principle
    - Interface Segregation Principle
    - Dependency Inversion Principle
  - Testing or TDD
  - Refactoring
- Writing the Right Software
  - Writing the Right Software with Machine Learning
  - What Exactly Is Machine Learning?
  - The High Interest Credit Card Debt of Machine Learning
  - SOLID Applied to Machine Learning
    - SRP
    - OCP
    - LSP
    - ISP
    - DIP
  - Machine Learning Code Is Complex but Not Impossible
  - TDD: Scientific Method 2.0
  - Refactoring Our Way to Knowledge
- The Plan for the Book
2. A Quick Introduction to Machine Learning
- What Is Machine Learning?
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- What Can Machine Learning Accomplish?
- Mathematical Notation Used Throughout the Book
- Conclusion
3. K-Nearest Neighbors
- How Do You Determine Whether You Want to Buy a House?
- How Valuable Is That House?
- Hedonic Regression
- What Is a Neighborhood?
- K-Nearest Neighbors
- Mr. Ks Nearest Neighborhood
- Distances
  - Triangle Inequality
  - Geometrical Distance
    - Cosine similarity
  - Computational Distances
    - Manhattan distance
    - Levenshtein distance
  - Statistical Distances
    - Mahalanobis distance
    - Jaccard distance
- Curse of Dimensionality
- How Do We Pick K?
  - Guessing K
  - Heuristics for Picking K
    - Use coprime class and K combinations
    - Choose a K that is greater or equal to the number of classes plus one
    - Choose a K that is low enough to avoid noise
    - Algorithms for picking K
- Valuing Houses in Seattle
  - About the Data
  - General Strategy
  - Coding and Testing Design
  - KNN Regressor Construction
  - KNN Testing
- Conclusion
4. Naive Bayesian Classification
- Using Bayes Theorem to Find Fraudulent Orders
- Conditional Probabilities
- Probability Symbols
- Inverse Conditional Probability (aka Bayes Theorem)
- Naive Bayesian Classifier
  - The Chain Rule
- Naiveté in Bayesian Reasoning
- Pseudocount
- Spam Filter
  - Setup Notes
  - Coding and Testing Design
  - Data Source
  - Email Class
  - Tokenization and Context
  - SpamTrainer
    - Storing training data
    - Building the Bayesian classifier
    - Calculating a classification
  - Error Minimization Through Cross-Validation
    - Minimizing false positives
    - Building the two folds
    - Cross-validation and error measuring
- Conclusion
5. Decision Trees and Random Forests
- The Nuances of Mushrooms
- Classifying Mushrooms Using a Folk Theorem
- Finding an Optimal Switch Point
  - Information Gain
  - GINI Impurity
  - Variance Reduction
- Pruning Trees
  - Ensemble Learning
    - Bagging
    - Random forests
  - Writing a Mushroom Classifier
    - Coding and testing design
    - MushroomProblem
    - Testing
- Conclusion
6. Hidden Markov Models
- Tracking User Behavior Using State Machines
- Emissions/Observations of Underlying States
- Simplification Through the Markov Assumption
  - Using Markov Chains Instead of a Finite State Machine
- Hidden Markov Model
- Evaluation: Forward-Backward Algorithm
  - Mathematical Representation of the Forward-Backward Algorithm
  - Using User Behavior
- The Decoding Problem Through the Viterbi Algorithm
- The Learning Problem
- Part-of-Speech Tagging with the Brown Corpus
  - Setup Notes
  - Coding and Testing Design
  - The Seam of Our Part-of-Speech Tagger: CorpusParser
  - Writing the Part-of-Speech Tagger
  - Cross-Validating to Get Confidence in the Model
  - How to Make This Model Better
- Conclusion
7. Support Vector Machines
- Customer Happiness as a Function of What They Say
  - Sentiment Classification Using SVMs
- The Theory Behind SVMs
  - Decision Boundary
  - Maximizing Boundaries
  - Kernel Trick: Feature Transformation
  - Optimizing with Slack
- Sentiment Analyzer
  - Setup Notes
  - Coding and Testing Design
  - SVM Testing Strategies
  - Corpus Class
  - CorpusSet Class
  - Model Validation and the Sentiment Classifier
- Aggregating Sentiment
  - Exponentially Weighted Moving Average
- Mapping Sentiment to Bottom Line
- Conclusion
8. Neural Networks
- What Is a Neural Network?
- History of Neural Nets
- Boolean Logic
- Perceptrons
- How to Construct Feed-Forward Neural Nets
  - Input Layer
    - Standard inputs
    - Symmetric inputs
  - Hidden Layers
  - Neurons
  - Activation Functions
  - Output Layer
  - Training Algorithms
  - The Delta Rule
  - Back Propagation
  - QuickProp
  - RProp
- Building Neural Networks
  - How Many Hidden Layers?
  - How Many Neurons for Each Layer?
  - Tolerance for Error and Max Epochs
- Using a Neural Network to Classify a Language
  - Setup Notes
  - Coding and Testing Design
  - The Data
  - Writing the Seam Test for Language
  - Cross-Validating Our Way to a Network Class
  - Tuning the Neural Network
  - Precision and Recall for Neural Networks
  - Wrap-Up of Example
  - Conclusion
9. Clustering
- Studying Data Without Any Bias
- User Cohorts
- Testing Cluster Mappings
  - Fitness of a Cluster
  - Silhouette Coefficient
  - Comparing Results to Ground Truth
- K-Means Clustering
  - The K-Means Algorithm
  - Downside of K-Means Clustering
- EM Clustering
  - Algorithm
    - Expectation
    - Maximization
- The Impossibility Theorem
- Example: Categorizing Music
  - Setup Notes
  - Gathering the Data
  - Coding Design
  - Analyzing the Data with K-Means
  - EM Clustering Our Data
  - The Results from the EM Jazz Clustering
- Conclusion
10. Improving Models and Data Extraction
- Debate Club
- Picking Better Data
  - Feature Selection
  - Exhaustive Search
  - Random Feature Selection
  - A Better Feature Selection Algorithm
  - Minimum Redundancy Maximum Relevance Feature Selection
- Feature Transformation and Matrix Factorization
  - Principal Component Analysis
  - Independent Component Analysis
- Ensemble Learning
  - Bagging
  - Boosting
- Conclusion
11. Putting It Together: Conclusion
- Machine Learning Algorithms Revisited
- How to Use This Information to Solve Problems
- Whats Next for You?
Index