Applied Machine Learning with Python

More Information
Learn
  • - 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
About

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.

Features
  • - Develop a full appreciation of the big topics in Machine Learning, like when supervised or unsupervised learning is appropriate,
  • - Stay away from partisanship with regard to libraries and learn to evaluate libraries solely according to their usefulness in a real-world context.
  • - Show practical uses of deep learning
  • – when can you use machine learning algorithms and when are deep learning algorithms appropriate- machine learning for business, not Kaggle competitions-
Page Count 212
Course Length 6 hours 21 minutes
ISBN 9781788297066
Date Of Publication 3 Dec 2020

Authors

Hamidreza Sattari

Hamidreza Sattari started software development in 2002. He has been involved in several areas of Software Engineering--programming to architecture to management. His area of interest has been integration among enterprise applications and SOA. Hamidreza Sattari earned his MSc in Software Engineering in 2008 from Herriot Watt University, UK and his Bachelor's Degree in 1994 in Electrical Engineering (Electronics) from Tehran Azad University, Iran. In recent years his research area of interest has been scientific data mining, using algorithms and statistical techniques in pattern recognition, estimation and machine learning.