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MACHINE LEARNING FOUNDTNS: SPRVSD UNSPRVSD ADV LEARNING

Information Technology

Computing & Information Technology

Gt Product Group

Binding Format

Marketing Imprint Name

B2B GT

CL Carton Count

Imprint Name

B2B & B2C

<P>This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. The book provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; outlines the computation paradigm for solving classification, regression, and clustering; and features essential techniques for building the a new generation of machine learning.</P>

Machine Learning Foundations

Duration

Duration Unit Of Measure

Transaction Type

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
Preface.
Contents.
Foundation.
Introduction.
1: Numerical Vectors.
2: Data Encoding.
3: Simple Machine Learning Algorithms.
4: Supervised Learning.
5: Instance Based Learning.
6: Probabilistic Learning.
7: Decision Tree.
8: Support Vector Machine.
9: Unsupervised Learning.
10: Simple Clustering Algorithms.
11: K Means Algorithm.
12: EM Algorithm.
13: Advanced Clustering.
14: Advanced Topics.
15: Ensemble Learning.
16: Semi-supervised Learning.
17: Temporal Learning.
18: Reinforcement Learning.
Index.

Machine Learning Foundations.css-1p8rvzy{position:absolute;padding-left:8px;}

Machine Learning Foundations