Mastering Machine Learning with Python in Six Steps
Apress (Verlag)
978-1-4842-4946-8 (ISBN)
You’ll 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. You’ll 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, you’ll 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.
Manohar Swamynathan is a data science practitioner and an avid programmer, with over 14+ years of experience in various data science related areas that include data warehousing, Business Intelligence (BI), analytical tool development, ad-hoc analysis, predictive modeling, data science product development, consulting, formulating strategy and executing analytics program. He's had a career covering life cycle of data across different domains such as US mortgage banking, retail/e-commerce, insurance, and industrial IoT. He has a bachelor's degree with a specialization in physics, mathematics, computers, and a master's degree in project management. He's currently living in Bengaluru, the silicon valley of India.
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.
| Erscheinungsdatum | 16.10.2019 |
|---|---|
| Zusatzinfo | 1 Illustrations, color; 184 Illustrations, black and white; XVII, 457 p. 185 illus., 1 illus. in color. |
| Verlagsort | Berkley |
| Sprache | englisch |
| Maße | 178 x 254 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Deep learning • machine learning • Model Tuning • Neural networks • Python • recommendation system • Reinforcement Learning • scikit-learn • Text Mining |
| ISBN-10 | 1-4842-4946-1 / 1484249461 |
| ISBN-13 | 978-1-4842-4946-8 / 9781484249468 |
| Zustand | Neuware |
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