- Data Integration for machine learning projects
- Data processing for machine learning projects
- Develop a full appreciation for neural networks and deep learning
- Learn to choose between machine learning libraries
- Use distributed machine learning^ e.g.Spark MLib^ when appropriate
When a developer applies machine learning in the real world^ he needs how machine learning projects are conducted from soup to nuts^ from the moment data have to be prepared for machine learning projects^ up to the possibilities presented by deep learning libraries. Selections of machine learning algorithms are usually presented in beginners books^ but then the context in which they are being used tends to be missing. This book is meant as a follow-up to introductory books on machine learning^ and it will fill gaps like the preparation of machine learning data for ML projects^ the variety and strengths of machine learning libraries^ and how projects using neural networks and deep learning algorithms are actually executed. In other words^ this book embeds what has been learned in theory and in small projects^ in the real-world.