Thoughtful Machine Learning with Python

Thoughtful Machine Learning with Python - Opis

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 exercisesApply test-driven development (TDD) to write and run tests before you start codingExplore techniques for improving your machine-learning models with data extraction and feature developmentWatch out for the risks of machine learning, such as underfitting or overfitting dataWork with K-Nearest Neighbors, neural networks, clustering, and other algorithms Spis treści:
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 (...) więcej 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 mniej