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 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 -- 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.