Building Machine Learning Systems With Python - Brossura

Richert, Willi; Coelho, Luis Pedro

Brossura

ISBN 10: 1782161406 ISBN 13: 9781782161400

Casa editrice: Packt Pub Ltd, 2013

Riassunto

Master Machine Learning using a broad set of Python libraries and start building your own Python-based ML systems
Covers classification, regression, feature engineering, and much more guided by practical examples
A scenario-based tutorial to get into the right mind-set of a machine learner (data exploration) and successfully implement this in your new or existing projects

Su questo libro

L'autore

Willi Richert

Willi Richert has a PhD in Machine Learning/Robotics and currently works for Microsoft in the Bing Core Relevance Team. He performs statistical machine translation.

Contenuti

Preface
Chapter 1: Getting Started with Python Machine Learning
Chapter 2: Learning How to Classify with Real-world Examples
Chapter 3: Clustering – Finding Related Posts
Chapter 4: Topic Modeling
Chapter 5: Classification – Detecting Poor Answers
Chapter 6: Classification II – Sentiment Analysis
Chapter 7: Regression – Recommendations
Chapter 8: Regression – Recommendations Improved
Chapter 9: Classification III – Music Genre Classification
Chapter 10: Computer Vision – Pattern Recognition
Chapter 11: Dimensionality Reduction
Chapter 12: Big(ger) Data
Appendix: Where to Learn More about Machine Learning
Index

Preface

Up

Chapter 1: Getting Started with Python Machine Learning
Machine learning and Python – the dream team
What the book will teach you (and what it will not)
What to do when you are stuck
Getting started
Introduction to NumPy, SciPy, and Matplotlib
Installing Python
Chewing data efficiently with NumPy and intelligently with SciPy
Learning NumPy
Indexing
Handling non-existing values
Comparing runtime behaviors
Learning SciPy
Our first (tiny) machine learning application
Reading in the data
Preprocessing and cleaning the data
Choosing the right model and learning algorithm
Before building our first model
Starting with a simple straight line
Towards some advanced stuff
Stepping back to go forward – another look at our data
Training and testing
Answering our initial question
Summary

Up

Chapter 2: Learning How to Classify with Real-world Examples
The Iris dataset
The first step is visualization
Building our first classification model
Evaluation – holding out data and cross-validation
Building more complex classifiers
A more complex dataset and a more complex classifier
Learning about the Seeds dataset
Features and feature engineering
Nearest neighbor classification
Binary and multiclass classification
Summary

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Chapter 3: Clustering – Finding Related Posts
Measuring the relatedness of posts
How not to do it
How to do it
Preprocessing – similarity measured as similar number of common words
Converting raw text into a bag-of-words
Counting words
Normalizing the word count vectors
Removing less important words
Stemming
Installing and using NLTK
Extending the vectorizer with NLTK's stemmer
Stop words on steroids
Our achievements and goals
Clustering
KMeans
Getting test data to evaluate our ideas on
Clustering posts
Solving our initial challenge
Another look at noise
Tweaking the parameters
Summary

Up

Chapter 4: Topic Modeling
Latent Dirichlet allocation (LDA)
Building a topic model
Comparing similarity in topic space
Modeling the whole of Wikipedia
Choosing the number of topics
Summary

Up

Chapter 5: Classification – Detecting Poor Answers
Sketching our roadmap
Learning to classify classy answers
Tuning the instance
Tuning the classifier
Fetching the data
Slimming the data down to chewable chunks
Preselection and processing of attributes
Defining what is a good answer
Creating our first classifier
Starting with the k-nearest neighbor (kNN) algorithm
Engineering the features
Training the classifier
Measuring the classifier's performance
Designing more features
Deciding how to improve
Bias-variance and its trade-off
Fixing high bias
Fixing high variance
High bias or low bias
Using logistic regression
A bit of math with a small example
Applying logistic regression to our postclassification problem
Looking behind accuracy – precision and recall
Slimming the classifier
Ship it!
Summary

Up

Chapter 6: Classification II – Sentiment Analysis
Sketching our roadmap
Fetching the Twitter data
Introducing the Naive Bayes classifier
Getting to know the Bayes theorem
Being naive
Using Naive Bayes to classify
Accounting for unseen words and other oddities
Accounting for arithmetic underflows
Creating our first classifier and tuning it
Solving an easy problem first
Using all the classes
Tuning the classifier's parameters
Cleaning tweets
Taking the word types into account
Determining the word types
Successfully cheating using SentiWordNet
Our first estimator
Putting everything together
Summary

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Chapter 7: Regression – Recommendations
Predicting house prices with regression
Multidimensional regression
Cross-validation for regression
Penalized regression
L1 and L2 penalties
Using Lasso or Elastic nets in scikit-learn
P greater than N scenarios
An example based on text
Setting hyperparameters in a smart way
Rating prediction and recommendations
Summary

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Chapter 8: Regression – Recommendations Improved
Improved recommendations
Using the binary matrix of recommendations
Looking at the movie neighbors
Combining multiple methods
Basket analysis
Obtaining useful predictions
Analyzing supermarket shopping baskets
Association rule mining
More advanced basket analysis
Summary

Up

Chapter 9: Classification III – Music Genre Classification
Sketching our roadmap
Fetching the music data
Converting into a wave format
Looking at music
Decomposing music into sine wave components
Using FFT to build our first classifier
Increasing experimentation agility
Training the classifier
Using the confusion matrix to measure accuracy in multiclass problems
An alternate way to measure classifier performance using receiver operator characteristic (ROC)
Improving classification performance with Mel Frequency Cepstral Coefficients
Summary

Up

Chapter 10: Computer Vision – Pattern Recognition
Introducing image processing
Loading and displaying images
Basic image processing
Thresholding
Gaussian blurring
Filtering for different effects
Adding salt and pepper noise
Putting the center in focus
Pattern recognition
Computing features from images
Writing your own features
Classifying a harder dataset
Local feature representations
Summary

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Chapter 11: Dimensionality Reduction
Sketching our roadmap
Selecting features
Detecting redundant features using filters
Correlation
Mutual information
Asking the model about the features using wrappers
Other feature selection methods
Feature extraction
About principal component analysis (PCA)
Sketching PCA
Applying PCA
Limitations of PCA and how LDA can help
Multidimensional scaling (MDS)
Summary

Up

Chapter 12: Big(ger) Data
Learning about big data
Using jug to break up your pipeline into tasks
About tasks
Reusing partial results
Looking under the hood
Using jug for data analysis
Using Amazon Web Services (AWS)
Creating your first machines
Installing Python packages on Amazon Linux
Running jug on our cloud machine
Automating the generation of clusters with starcluster
Summary

Up

Appendix: Where to Learn More about Machine Learning
Online courses
Books
Q&A sites
Blogs
Data sources
Getting competitive
What was left out
Summary

Dettagli dell?edizione

• Editore: Packt Pub Ltd
• Data di pubblicazione: 2013
• ISBN 10: 1782161406
• ISBN 13: 9781782161400
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 271