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Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.
Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.
MICHAEL BOWLES teaches machine learning at UC Berkeley, University of New Haven and Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as semi conductor inspection, drug design and optimization and trading in the financial markets. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses are always popular and receive great feedback from participants.
Chapter 1 The Two Essential Algorithms for Making Predictions 1
Chapter 2 Understand the Problem by Understanding the Data 23
Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data 77
Chapter 4 Penalized Linear Regression 129
Chapter 5 Building Predictive Models Using Penalized Linear Methods 169
Chapter 6 Ensemble Methods 221
Chapter 7 Building Ensemble Models with Python 265
Index 329
| Erscheinungsjahr: | 2019 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 368 |
| Inhalt: | 368 S. |
| ISBN-13: | 9781119561934 |
| ISBN-10: | 1119561930 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Bowles, Michael |
| Auflage: | 2nd edition |
| Hersteller: | Wiley |
| Maße: | 233 x 189 x 22 mm |
| Von/Mit: | Michael Bowles |
| Erscheinungsdatum: | 05.11.2019 |
| Gewicht: | 0,631 kg |
MICHAEL BOWLES teaches machine learning at UC Berkeley, University of New Haven and Hacker Dojo in Silicon Valley, consults on machine learning projects, and is involved in a number of startups in such areas as semi conductor inspection, drug design and optimization and trading in the financial markets. Following an assistant professorship at MIT, Michael went on to found and run two Silicon Valley startups, both of which went public. His courses are always popular and receive great feedback from participants.
Chapter 1 The Two Essential Algorithms for Making Predictions 1
Chapter 2 Understand the Problem by Understanding the Data 23
Chapter 3 Predictive Model Building: Balancing Performance, Complexity, and Big Data 77
Chapter 4 Penalized Linear Regression 129
Chapter 5 Building Predictive Models Using Penalized Linear Methods 169
Chapter 6 Ensemble Methods 221
Chapter 7 Building Ensemble Models with Python 265
Index 329
| Erscheinungsjahr: | 2019 |
|---|---|
| Genre: | Informatik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Taschenbuch |
| Seiten: | 368 |
| Inhalt: | 368 S. |
| ISBN-13: | 9781119561934 |
| ISBN-10: | 1119561930 |
| Sprache: | Englisch |
| Einband: | Kartoniert / Broschiert |
| Autor: | Bowles, Michael |
| Auflage: | 2nd edition |
| Hersteller: | Wiley |
| Maße: | 233 x 189 x 22 mm |
| Von/Mit: | Michael Bowles |
| Erscheinungsdatum: | 05.11.2019 |
| Gewicht: | 0,631 kg |
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