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## Book Description

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. Ideal for software engineers and business analysts interested in data science.

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