Supervised Machine Learning with Python: Develop rich Python coding practices while exploring supervised machine learning
bTeach your machine to think for itself!/b h4Key Features/h4 ul liDelve into supervised learning and grasp how a machine learns from data /li liImplement popular machine learning algorithms from scratch, developing a deep understanding along the way /li liExplore some of the most popular scientific...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing Limited
2019
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| Ausgabe: | 1 |
| Schlagworte: | |
| Zusammenfassung: | bTeach your machine to think for itself!/b h4Key Features/h4 ul liDelve into supervised learning and grasp how a machine learns from data /li liImplement popular machine learning algorithms from scratch, developing a deep understanding along the way /li liExplore some of the most popular scientific and mathematical libraries in the Python language /li /ul h4Book Description/h4 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. h4What you will learn/h4 ul liCrack how a machine learns a concept and generalize its understanding to new data /li liUncover the fundamental differences between parametric and non-parametric models /li liImplement and grok several well-known supervised learning algorithms from scratch /li liWork with models in domains such as ecommerce and marketing /li liExpand your expertise and use various algorithms such as regression, decision trees, and clustering /li liBuild your own models capable of making predictions /li liDelve into the most popular approaches in deep learning such as transfer learning and neural networks /li /ul h4Who this book is for/h4 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 |
| Beschreibung: | 1 Online-Ressource (162 Seiten) |
| ISBN: | 9781838823061 |
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| 520 | |a bTeach your machine to think for itself!/b h4Key Features/h4 ul liDelve into supervised learning and grasp how a machine learns from data /li liImplement popular machine learning algorithms from scratch, developing a deep understanding along the way /li liExplore some of the most popular scientific and mathematical libraries in the Python language /li /ul h4Book Description/h4 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. | ||
| 520 | |a 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. | ||
| 520 | |a h4What you will learn/h4 ul liCrack how a machine learns a concept and generalize its understanding to new data /li liUncover the fundamental differences between parametric and non-parametric models /li liImplement and grok several well-known supervised learning algorithms from scratch /li liWork with models in domains such as ecommerce and marketing /li liExpand your expertise and use various algorithms such as regression, decision trees, and clustering /li liBuild your own models capable of making predictions /li liDelve into the most popular approaches in deep learning such as transfer learning and neural networks /li /ul h4Who this book is for/h4 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 | ||
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| isbn | 9781838823061 |
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| spelling | Smith, Taylor Verfasser aut Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning Smith, Taylor 1 Birmingham Packt Publishing Limited 2019 1 Online-Ressource (162 Seiten) txt rdacontent c rdamedia cr rdacarrier bTeach your machine to think for itself!/b h4Key Features/h4 ul liDelve into supervised learning and grasp how a machine learns from data /li liImplement popular machine learning algorithms from scratch, developing a deep understanding along the way /li liExplore some of the most popular scientific and mathematical libraries in the Python language /li /ul h4Book Description/h4 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. h4What you will learn/h4 ul liCrack how a machine learns a concept and generalize its understanding to new data /li liUncover the fundamental differences between parametric and non-parametric models /li liImplement and grok several well-known supervised learning algorithms from scratch /li liWork with models in domains such as ecommerce and marketing /li liExpand your expertise and use various algorithms such as regression, decision trees, and clustering /li liBuild your own models capable of making predictions /li liDelve into the most popular approaches in deep learning such as transfer learning and neural networks /li /ul h4Who this book is for/h4 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 COMPUTERS / Programming / Algorithms COMPUTERS / Data Processing |
| spellingShingle | Smith, Taylor Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning COMPUTERS / Programming / Algorithms COMPUTERS / Data Processing |
| title | Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning |
| title_auth | Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning |
| title_exact_search | Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning |
| title_exact_search_txtP | Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning |
| title_full | Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning Smith, Taylor |
| title_fullStr | Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning Smith, Taylor |
| title_full_unstemmed | Supervised Machine Learning with Python Develop rich Python coding practices while exploring supervised machine learning Smith, Taylor |
| title_short | Supervised Machine Learning with Python |
| title_sort | supervised machine learning with python develop rich python coding practices while exploring supervised machine learning |
| title_sub | Develop rich Python coding practices while exploring supervised machine learning |
| topic | COMPUTERS / Programming / Algorithms COMPUTERS / Data Processing |
| topic_facet | COMPUTERS / Programming / Algorithms COMPUTERS / Data Processing |
| work_keys_str_mv | AT smithtaylor supervisedmachinelearningwithpythondeveloprichpythoncodingpracticeswhileexploringsupervisedmachinelearning |