Building Machine Learning Systems with Python. Expand your Python knowledge and learn all about machine-learning libraries in this user-friendly manual. ML is the next big breakthrough in technology and this book will give you the head-start you need

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• Building Machine Learning Systems with Python
  • Table of Contents
  • Building Machine Learning Systems with Python
  • Credits
  • About the Authors
  • About the Reviewers
  • www.PacktPub.com
    • Support files, eBooks, discount offers and more
      • Why Subscribe?
      • Free Access for Packt account holders
  • Preface
    • What this book covers
    • What you need for this book
    • Who this book is for
    • Conventions
    • Reader feedback
    • Customer support
      • Downloading the example code
      • Errata
      • Piracy
      • Questions
  • 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
  • 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
  • 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 NLTKs 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • A. Where to Learn More about Machine Learning
    • Online courses
    • Books
      • Q&A sites
      • Blogs
      • Data sources
      • Getting competitive
    • What was left out
    • Summary
  • Index

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