Title Page Copyright and Credits Go Machine Learning Projects About Packt Why subscribe? Packt.com Contributors About the author About the reviewer 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 Conventions used Get in touch Reviews How to Solve All Machine Learning Problems What is a problem?  What is an algorithm?  What is machine learning?  Do you need machine learning? The general problem solving process What is a model? What is a good model? On writing and chapter organization  Why Go?  Quick start Functions Variables Values  Types  Methods  Interfaces Packages and imports Let's Go!  Linear Regression - House Price Prediction The project Exploratory data analysis Ingestion and indexing Janitorial work Encoding categorical data Handling bad numbers Final requirement Writing the code Further exploratory work The conditional expectation functions Skews Multicollinearity Standardization Linear regression The regression Cross-validation Running the regression Discussion and further work Summary Classification - Spam Email Detection The project  Exploratory data analysis  Tokenization Normalizing and lemmatizing Stopwords Ingesting the data Handling errors The classifier Naive Bayes TF-IDF  Conditional probability Features Bayes' theorem Implementating the classifier Class Alternative class design Classifier part II Putting it all together Summary Decomposing CO2 Trends Using Time Series Analysis Exploratory data analysis Downloading from non-HTTP sources Handling non-standard data Dealing with decimal dates Plotting Styling Decomposition STL LOESS The algorithm Using STL How to lie with statistics More plotting A primer on Gonum plots The residuals plotter Combining plots Forecasting Holt-Winters Summary References Clean Up Your Personal Twitter Timeline by Clustering Tweets The project  K-means  DBSCAN Data acquisition Exploratory data analysis Data massage The processor  Preprocessing a single word  Normalizing a string Preprocessing stopwords Preprocessing Twitter entities  Processing a single tweet  Clustering  Clustering with K-means  Clustering with DBSCAN  Clustering with DMMClust  Real data The program  Tweaking the program Tweaking distances  Tweaking the preprocessing step  Summary Neural Networks - MNIST Handwriting Recognition A neural network Emulating a neural network Linear algebra 101 Exploring activation functions Learning The project Gorgonia Getting the data Acceptable format From images to a matrix What is a tensor? From labels to one-hot vectors Visualization Preprocessing Building a neural network Feed forward Handling errors with maybe Explaining the feed forward function Costs Backpropagation Training the neural network Cross-validation Summary Convolutional Neural Networks - MNIST Handwriting Recognition Everything you know about neurons is wrong  Neural networks – a redux Gorgonia Why? Programming What is a tensor? – part 2 All expressions are graphs Describing a neural network One-hot vector The project Getting the data Other things from the previous chapter CNNs What are convolutions? How Instagram filters work Back to neural networks Max-pooling Dropout Describing a CNN Backpropagation Running the neural network Testing Accuracy Summary Basic Facial Detection What is a face?  Viola-Jones PICO  A note on learning  GoCV  API  Pigo Face detection program  Grabbing an image from the webcam  Displaying the image  Doodling on images  Face detection 1  Face detection 2 Putting it all together Evaluating algorithms Summary Hot Dog or Not Hot Dog - Using External Services MachineBox What is MachineBox? Signing in and up  Docker installation and setting up Using MachineBox in Go The project Training  Reading from the Webcam  Prettifying the results The results What does this all mean?  Why MachineBox? Summary What's Next? What should the reader focus on?  The practitioner  The researcher  The researcher, the practitioner, and their stakeholder What did this book not cover? Where can I learn more? Thank you Other Books You May Enjoy Leave a review - let other readers know what you think