What this book covers

Chapter 1, Unsupervised Machine Learning, shows you how to apply unsupervised learning techniques to identify patterns and structure within datasets.

Chapter 2, Deep Belief Networks, explains how the RBM and DBN algorithms work; you'll know how to use them and will feel confident in your ability to improve the quality of the results that you get out of them.

Chapter 3, Stacked Denoising Autoencoders, continues to build our skill with deep architectures by applying stacked denoising autoencoders to learn feature representations for high-dimensional input data.

Chapter 4, Convolutional Neural Networks, shows you how to apply the convolutional neural network (or Convnet).

Chapter 5, Semi-Supervised Learning, explains how to apply several semi-supervised learning techniques, including CPLE, self-learning, and S3VM.

Chapter 6, Text Feature Engineering, discusses data preparation skills that significantly increase the effectiveness of all the models that we've previously discussed.

Chapter 7, Feature Engineering Part II, shows you how to interrogate the data to weed out or mitigate quality issues, transform it into forms that are conducive to machine learning, and creatively enhance that data.

Chapter 8, Ensemble Methods, looks at building more sophisticated model ensembles and methods of building robustness into your model solutions.

Chapter 9, Additional Python Machine Learning Tools, reviews some of the best in recent tools available to data scientists, identifies the benefits that they offer, and discusses how to apply them alongside tools and techniques discussed earlier in this book, within a consistent working process.

Appendix A, Chapter Code Requirements, discusses tool requirements for the book, identifying required libraries for each chapter.