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Intro -- Building Machine Learning Systems with Python Second Edition -- Table of Contents -- Building Machine Learning Systems with Python Second Edition -- 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 - a 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 nonexisting values -- Comparing the runtime -- Learning SciPy -- Our first (tiny) application of machine learning -- 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. Classifying with Real-world Examples -- The Iris dataset -- Visualization is a good first step -- 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 -- Classifying with scikit-learn -- Looking at the decision boundaries.
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 a similar number of common words -- Converting raw text into a bag of words -- Counting words -- Normalizing 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 -- K-means -- 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 -- Building a topic model -- Comparing documents by topics -- 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 kNN -- Engineering the features -- Training the classifier -- Measuring the classifier's performance -- Designing more features -- Deciding how to improve -- Bias-variance and their tradeoff -- 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 post classification 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 Naïve Bayes classifier.
Getting to know the Bayes' theorem -- Being naïve -- Using Naïve 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 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 -- Predicting house prices with regression -- Multidimensional regression -- Cross-validation for regression -- Penalized or regularized regression -- L1 and L2 penalties -- Using Lasso or ElasticNet in scikit-learn -- Visualizing the Lasso path -- P-greater-than-N scenarios -- An example based on text documents -- Setting hyperparameters in a principled way -- Summary -- 8. Recommendations -- Rating predictions and recommendations -- Splitting into training and testing -- Normalizing the training data -- A neighborhood approach to recommendations -- A regression approach to recommendations -- Combining multiple methods -- Basket analysis -- Obtaining useful predictions -- Analyzing supermarket shopping baskets -- Association rule mining -- More advanced basket analysis -- Summary -- 9. Classification - Music Genre Classification -- Sketching our roadmap -- Fetching the music data -- Converting into a WAV format -- Looking at music -- Decomposing music into sine wave components -- Using FFT to build our first classifier -- Increasing experimentation agility -- Training the classifier -- Using a confusion matrix to measure accuracy in multiclass problems -- An alternative way to measure classifier performance using receiver-operator characteristics -- Improving classification performance with Mel Frequency Cepstral Coefficients.
Summary -- 10. Computer Vision -- Introducing image processing -- Loading and displaying images -- Thresholding -- Gaussian blurring -- Putting the center in focus -- Basic image classification -- Computing features from images -- Writing your own features -- Using features to find similar images -- 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 -- Sketching PCA -- Applying PCA -- Limitations of PCA and how LDA can help -- Multidimensional scaling -- Summary -- 12. Bigger Data -- Learning about big data -- Using jug to break up your pipeline into tasks -- An introduction to tasks in jug -- Looking under the hood -- Using jug for data analysis -- Reusing partial results -- Using Amazon Web Services -- Creating your first virtual 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 Machine Learning -- Online courses -- Books -- Question and answer sites -- Blogs -- Data sources -- Getting competitive -- All that was left out -- Summary -- Index.
This book primarily targets Python developers who want to learn and use Python's machine learning capabilities and gain valuable insights from data to develop effective solutions for business problems.
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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.