Librería Portfolio

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



Chapter 1Probably Approximately Correct Software
Writing Software Right
Writing the Right Software
The Plan for the Book
Chapter 2A 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
Chapter 3K-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. K's Nearest Neighborhood
Distances
Curse of Dimensionality
How Do We Pick K?
Valuing Houses in Seattle
Conclusion
Chapter 4Naive Bayesian Classification
Using Bayes' Theorem to Find Fraudulent Orders
Conditional Probabilities
Probability Symbols
Inverse Conditional Probability (aka Bayes' Theorem)
Naive Bayesian Classifier
Naiveté in Bayesian Reasoning
Pseudocount
Spam Filter
Conclusion
Chapter 5Decision Trees and Random Forests
The Nuances of Mushrooms
Classifying Mushrooms Using a Folk Theorem
Finding an Optimal Switch Point
Pruning Trees
Conclusion
Chapter 6Hidden Markov Models
Tracking User Behavior Using State Machines
Emissions/Observations of Underlying States
Simplification Through the Markov Assumption
Hidden Markov Model
Evaluation: Forward-Backward Algorithm
The Decoding Problem Through the Viterbi Algorithm
The Learning Problem
Part-of-Speech Tagging with the Brown Corpus
Conclusion
Chapter 7Support Vector Machines
Customer Happiness as a Function of What They Say
The Theory Behind SVMs
Sentiment Analyzer
Aggregating Sentiment
Mapping Sentiment to Bottom Line
Conclusion
Chapter 8Neural Networks
What Is a Neural Network?
History of Neural Nets
Boolean Logic
Perceptrons
How to Construct Feed-Forward Neural Nets
Building Neural Networks
Using a Neural Network to Classify a Language
Chapter 9Clustering
Studying Data Without Any Bias
User Cohorts
Testing Cluster Mappings
K-Means Clustering
EM Clustering
The Impossibility Theorem
Example: Categorizing Music
Conclusion
Chapter 10Improving Models and Data Extraction
Debate Club
Picking Better Data
Feature Transformation and Matrix Factorization
Ensemble Learning
Conclusion
Chapter 11Putting It Together: Conclusion
Machine Learning Algorithms Revisited
How to Use This Information to Solve Problems
What's Next for You?