Supervised Machine Learning with Python | Develop rich Python coding practices while exploring supervised machine learning
Lingua: inglese
Editore: Packt Publishing, 2019
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Supervised Machine Learning with Python | Develop rich Python coding practices while exploring supervised machine learning | Taylor Smith | Taschenbuch | Kartoniert / Broschiert | Englisch | 2019 | Packt Publishing | EAN 9781838825669 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Codice articolo 116796890
- Titolo
- Supervised Machine Learning with Python | Develop rich Python coding practices while exploring supervised machine learning
- Autore
- Taylor Smith
- Editore
- Packt Publishing
- Anno di pubblicazione
- 2019
- Condizione
- Neu
- Rilegatura
- Taschenbuch
- Lingua
- inglese
- ISBN 10
- 1838825665
- ISBN 13
- 9781838825669
- Peso dell'articolo
- 316 grammi
- Dimensioni
- 235 x 191 x 9 mm
- Cataloghi dei venditori
- Bücher
Teach your machine to think for itself!
Key Features
- Delve into supervised learning and grasp how a machine learns from data
- Implement popular machine learning algorithms from scratch, developing a deep understanding along the way
- Explore some of the most popular scientific and mathematical libraries in the Python language
Book Description
Supervised machine learning is used in a wide range of sectors (such as finance, online advertising, and analytics) because it allows you to train your system to make pricing predictions, campaign adjustments, customer recommendations, and much more while the system self-adjusts and makes decisions on its own. As a result, it's crucial to know how a machine learns under the hood.
This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms while facilitating a deep understanding along the way. You'll embark on this journey with a quick overview and see how supervised machine learning differs from unsupervised learning. Next, we explore parametric models such as linear and logistic regression, non-parametric methods such as decision trees, and various clustering techniques to facilitate decision-making and predictions. As we proceed, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you'll wrap up with a brief foray into neural networks and transfer learning.
By the end of this book, you'll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and powerfully apply algorithms to new problems.
What you will learn
- Crack how a machine learns a concept and generalize its understanding to new data
- Uncover the fundamental differences between parametric and non-parametric models
- Implement and grok several well-known supervised learning algorithms from scratch
- Work with models in domains such as ecommerce and marketing
- Expand your expertise and use various algorithms such as regression, decision trees, and clustering
- Build your own models capable of making predictions
- Delve into the most popular approaches in deep learning such as transfer learning and neural networks
Who this book is for
This book is for aspiring machine learning developers who want to get started with supervised learning. Intermediate knowledge of Python programming?and some fundamental knowledge of supervised learning?are expected.
Table of Contents
- First step towards supervised learning
- Implementing parametric models
- Working with non-parametric models
- Advanced topics in supervised machine learning
"Riassunto" può appartenere a un’altra edizione di questo titolo.
Informazioni sull’autore
Taylor Smith is a machine learning enthusiast with over five years of experience who loves to apply interesting computational solutions to challenging business problems. Currently working as a principal data scientist, Taylor is also an active open source contributor and staunch Pythonista.
"Descrizione articolo" può appartenere a un’altra edizione di questo titolo.
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