Applied Machine Learning with Python
Nonfiction, Computers, Advanced Computing, Artificial Intelligence, Programming, Programming Languages, Application Software
| Author: | Hamidreza Sattari | ISBN: | 9781788297523 |
| Publisher: | Packt Publishing | Publication: | January 11, 2021 |
| Imprint: | Packt Publishing | Language: | English |
| Author: | Hamidreza Sattari |
| ISBN: | 9781788297523 |
| Publisher: | Packt Publishing |
| Publication: | January 11, 2021 |
| Imprint: | Packt Publishing |
| Language: | English |
To write the machine learning and deep learning applications that create your business edge
About This Book
- 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
Who This Book Is For
Everyone competent enough in Python, who has read an introductory book in machine learning can understand and profit from Applied Machine Learning in Python. The book expects the reader to engage with machine learning projects, and be prepared for the vicissitudes of data integration and data preprocessing. Knowledge of Python and basic machine learning algorithms is required.
What You Will 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
In Detail
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.
To write the machine learning and deep learning applications that create your business edge
About This Book
- 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
Who This Book Is For
Everyone competent enough in Python, who has read an introductory book in machine learning can understand and profit from Applied Machine Learning in Python. The book expects the reader to engage with machine learning projects, and be prepared for the vicissitudes of data integration and data preprocessing. Knowledge of Python and basic machine learning algorithms is required.
What You Will 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
In Detail
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.
