github.com/petrostrak/machine-learning-with-go

Machine Learning with Go!

Gather, organize, and parse real-world data from a variety of sources. Also develop ML pipelines including predictive models, data visualizations, and statistical techniques.

This repo covers

• Gathering and Organizing Data, covers the gathering, organization, and parsing of data from local and remote sources. How to interact with data stored in various places and in various formats, how to parse and clean that data, and how to output that cleaned and parsed data.

• Matrices, Probability, and Statistics, also covers statistical measures and operations key to day-to-day data analysis work. How to perform solid summary data analysis, describe and visualize distributions, quantify hypotheses, and transform datasets.

• Evaluation and Validation, covers evaluation and validation, which are key to measuring the performance of machine applications and ensuring that they generalize.

• Regression, covers regression, a widely used technique to model continuous variables, and a basis for other models. Regression produces models that are immediately interpretable.

• Classification, covers classification, a machine learning technique distinct from regression in that the target variable is typically categorical or labeled.

• Clustering, covers clustering, an unsupervised machine learning technique used to form groupings of samples.

• Time Series and Anomaly Detection, introduces techniques utilized to model time series data, such as stock prices and user events. How to evaluate various terms in a time series, build up a model of the time series, and detect anomalies in a time series.

• Neural Networks, introduces techniques utilized to perform regression, classification, and image processing with neural networks.

• Deep Learning, introduces deep learning techniques, along with the motivation behind them.