Название: Machine Learning For Dummies, IBM Limited Edition
Автор: Judith Hurwitz, Daniel Kirsch
Издательство: Wiley
Год: 2018
Страниц: 74
Язык: английский
Формат: pdf (true), djvu
Размер: 13.2 MB

Intended for managers, and beginners, in this book, you discover types of machine learning techniques, models, and algorithms that can help achieve results for your company.

Machine Learning For Dummies, IBM Limited Edition, gives you insights into what machine learning is all about and how it can impact the way you can weaponize data to gain unimaginable insights. Your data is only as good as what you do with it and how you manage it. In this book, you discover types of machine learning techniques, models, and algorithms that can help achieve results for your company. This information helps both business and technical leaders learn how to apply machine learning to anticipate and predict the future.

Machine learning has become one of the most important topics within development organizations that are looking for innovative ways to leverage data assets to help the business gain a new level of understanding. Why add machine learning into the mix? With the appropriate machine learning models, organizations have the ability to continually predict changes in the business so that they are best able to predict what’s next. As data is constantly added, the machine learning models ensure that the solution is constantly updated. The value is straightforward: If you use the most appropriate and constantly changing data sources in the context of machine learning, you have the opportunity to predict the future.

Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process.

Machine learning uses a variety of algorithms that iteratively learn from data to improve, describe data, and predict outcomes. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output.








[related-news] [/related-news]