John D. Kelleher, Brian Mac Namee, Aoife D'Arcy

Fundamentals of Machine Learning for Predictive Data Analytics, second edition (eBook, ePUB)

Algorithms, Worked Examples, and Case Studies

• Format: ePub

Produktbeschreibung

The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

---

Dieser Download kann aus rechtlichen Gründen nur mit Rechnungsadresse in A, B, BG, CY, CZ, D, DK, EW, E, FIN, F, GR, HR, H, IRL, I, LT, L, LR, M, NL, PL, P, R, S, SLO, SK ausgeliefert werden.

Produktdetails

• Verlag: MIT Press
• Seitenzahl: 856
• Erscheinungstermin: 20. Oktober 2020
• Englisch
• ISBN-13: 9780262361101
• Artikelnr.: 58671638

Autorenporträt

• Prof. John D. Kelleher, Academic Leader of the Information, Communication and Entertainment Research Institute, Technological University Dublin (Ireland) • Brian MacNamee, Lecturer, School of Computer Science, University College Dublin • Aoife D'Arcy, Managing Director, The Analytics Store, Dublin, Ireland<

Inhaltsangabe

I Introduction to Machine Learning and Data Analytics
1 Machine Learning for Predictive Data Analytics
2 Data to Insights to Decisions
3 Data Exploration
II Predictive Data Analytics
4 Information-Based Learning
5 Similarity-Based Learning
6 Probability-Based Learning
7 Error-Based Learning
8 Deep Learning
9 Evaluation
III Beyond Prediction
10 Beyond Prediction: Unsupervised Learning
11 Beyond Prediction: Reinforcement Learning
IV Case Studies and Conclusions
12 Case Study: Customer Churn
13 Case Study: Galaxy Classification
14 The Art of Machine Learning for Predictive Data Analytics
V Appendices
A Descriptive Statistics and Data Visualization for Machine Learning
B Introduction to Probability for Machine Learning
C Differentiation Techniques for Machine Learning
D Introduction to Linear Algebra
Bibliography
Index