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Yoüll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. Yoüll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.
Finally, yoüll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.
What You'll Learn
Understand machine learning development and frameworks
Assess model diagnosis and tuning in machine learning
Examine text mining, natuarl language processing (NLP), and recommender systems
Review reinforcement learning and CNN
Who This Book Is For
Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
Yoüll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. Yoüll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data.
Finally, yoüll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.
What You'll Learn
Understand machine learning development and frameworks
Assess model diagnosis and tuning in machine learning
Examine text mining, natuarl language processing (NLP), and recommender systems
Review reinforcement learning and CNN
Who This Book Is For
Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area.
Compares different machine learning framework implementations for each topic
Covers Reinforcement Learning and Convolutional Neural Networks
Explains best practices for model tuning for better model accuracy
Chapter 1: Step 1 - Getting Started with Python.- Chapter 2 : Step 2 - Introduction to Machine Learning.- Chapter 3: Step 3 - Fundamentals of Machine Learning.- Chapter 4: Step 4 - Model Diagnosis and Tuning.- Chapter 5: Step 5 - Text Mining, NLP AND Recommender Systems.- Chapter 6: Step 6 - Deep and Reinforcement Learning.- Chapter 7 : Conclusion.
| Medium: | Taschenbuch |
|---|---|
| Seiten: | 476 |
| Inhalt: |
xvii
457 S. 184 s/w Illustr. 1 farbige Illustr. 457 p. 185 illus. 1 illus. in color. |
| ISBN-13: | 9781484249468 |
| ISBN-10: | 1484249461 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Swamynathan, Manohar |
| Auflage: | 2nd ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 254 x 178 x 26 mm |
| Von/Mit: | Manohar Swamynathan |
| Erscheinungsdatum: | 02.10.2019 |
| Gewicht: | 0,887 kg |
Compares different machine learning framework implementations for each topic
Covers Reinforcement Learning and Convolutional Neural Networks
Explains best practices for model tuning for better model accuracy
Chapter 1: Step 1 - Getting Started with Python.- Chapter 2 : Step 2 - Introduction to Machine Learning.- Chapter 3: Step 3 - Fundamentals of Machine Learning.- Chapter 4: Step 4 - Model Diagnosis and Tuning.- Chapter 5: Step 5 - Text Mining, NLP AND Recommender Systems.- Chapter 6: Step 6 - Deep and Reinforcement Learning.- Chapter 7 : Conclusion.
| Medium: | Taschenbuch |
|---|---|
| Seiten: | 476 |
| Inhalt: |
xvii
457 S. 184 s/w Illustr. 1 farbige Illustr. 457 p. 185 illus. 1 illus. in color. |
| ISBN-13: | 9781484249468 |
| ISBN-10: | 1484249461 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | Paperback |
| Einband: | Kartoniert / Broschiert |
| Autor: | Swamynathan, Manohar |
| Auflage: | 2nd ed. |
| Hersteller: |
Apress
Apress L.P. |
| Maße: | 254 x 178 x 26 mm |
| Von/Mit: | Manohar Swamynathan |
| Erscheinungsdatum: | 02.10.2019 |
| Gewicht: | 0,887 kg |
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