Title Page Copyright and Credits Building Machine Learning Systems with Python Third Edition Packt Upsell Why subscribe? PacktPub.com Contributors About the authors About the reviewers Packt is searching for authors like you Preface Who this book is for What this book covers To get the most out of this book Download the example code files Download the color images Conventions used Get in touch Reviews Getting Started with Python Machine Learning Machine learning and Python – a dream team What the book will teach you – and what it will not How to best read this book What to do when you are stuck Getting started Introduction to NumPy, SciPy, Matplotlib, and TensorFlow Installing Python Chewing data efficiently with NumPy and intelligently with SciPy Learning NumPy Indexing Handling nonexistent values Comparing the runtime Learning SciPy Fundamentals of machine learning Asking a question Getting answers Our first (tiny) application of machine learning Reading in the data Preprocessing and cleaning the data Choosing the right model and learning algorithm Before we build our first model Starting with a simple straight line Toward more complex models Stepping back to go forward - another look at our data Training and testing Answering our initial question Summary Classifying with Real-World Examples The Iris dataset Visualization is a good first step Classifying with scikit-learn Building our first classification model Evaluation – holding out data and cross-validation How to measure and compare classifiers A more complex dataset and the nearest-neighbor classifier Learning about the seeds dataset Features and feature engineering Nearest neighbor classification Looking at the decision boundaries Which classifier to use Summary 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 Regression with TensorFlow Summary Classification I – 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 Preselecting and processing attributes Defining what a good answer is Creating our first classifier Engineering the features Training the classifier Measuring the classifier's performance Designing more features Deciding how to improve the performance Bias, variance and their trade-off Fixing high bias Fixing high variance High 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! Classification using Tensorflow Summary 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 projection Principal component analysis Sketching PCA Applying PCA Limitations of PCA and how LDA can help Multidimensional scaling Autoencoders, or neural networks for dimensionality reduction Summary 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 Clustering posts Solving our initial challenge Another look at noise Tweaking the parameters Summary 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 Artificial Neural Networks and Deep Learning Using TensorFlow TensorFlow API Graphs Sessions Useful operations Saving and restoring neural networks Training neural networks Convolutional neural networks Recurrent neural networks LSTM for predicting text LSTM for image processing Summary 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 Topic Modeling Latent Dirichlet allocation Building a topic model Comparing documents by topic Modeling the whole of Wikipedia Choosing the number of topics Summary Classification III – Music Genre Classification Sketching our roadmap Fetching the music data Converting into 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 Music classification using Tensorflow Summary 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 Image generation with adversarial networks Summary Reinforcement Learning Types of reinforcement learning Policy and value network Q-network Excelling at games A small example Using Tensorflow for the text game Playing breakout Summary 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 cfncluster Summary Where to Learn More About Machine Learning Online courses Books Blogs Data sources Getting competitive All that was left out Summary Other Books You May Enjoy Leave a review - let other readers know what you think