Preface
[ 2 ]
Chapter 2, Matrices, Probability, and Statistics, covers the organization of data into
matrices and matrix operations. Once the reader is done with this material, they
will understand how to form matrices within Go programs and how to utilize
these matrices to perform various types of matrix operations. This chapter also
covers statistical measures and operations key to day-to-day data analysis work.
Once the reader is done with this chapter, they will understand how to perform
solid summary data analysis, describe and visualize distributions, quantify
hypotheses, and transform datasets with, for example, dimensionality reductions.
Chapter 3, Evaluation and Validation, covers evaluation and validation, which are
key to measuring the performance of machine applications and ensuring that
they generalize. Once the reader is done with this chapter, they will understand
various metrics to gauge the performance of models (in other words, evaluate the
model) as well as various techniques to validate the model more generally.
Machine learning techniques:
Chapter 4, Regression, explains regression, a widely used technique to model
continuous variables and a basis for other models. Regression produces models
that are immediately interpretable. Thus, it can provide an excellent starting
point when introducing predictive capabilities in a organization.
Chapter 5, Classification, covers classification, a machine learning technique
distinct from regression in that the target variable is typically categorical or
labeled. For example, a classification model may classify emails into spam and
not spam categories or classify network traffic as fraudulent or not fraudulent.
Chapter 6, Clustering, covers clustering, an unsupervised machine learning
technique used to form groupings of samples. At the end of this chapter, readers
will be able to automatically form groupings of data points to better understand
their structure.
Chapter 7, Time Series and Anomaly Detection, introduces techniques utilized to
model time series data, such as stock prices, user events, and so on. After reading
this chapter, the reader will understand how to evaluate various terms in a time
series, build up a model of the time series, and detect anomalies in a time series.
Taking machine learning to the next level:
Chapter 8, Neural Networks and Deep Learning, introduces techniques utilized to
perform regression, classification, and image processing with neural networks.
After reading this chapter, the reader will understand how and when to apply
these more complicated modeling techniques.
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