Applied Machine Learning

1. Introduction 
2. Supervised Learning: Rationale and Basics
3. Statistical Learning
4. Learning with Support Vector Machines (SVMs)
5. Learning with Neural Networks (NNs)
6. Decision Tree Learning and Tree-Based Ensembles
7. Data Clustering
8. Data Transformations
9. Understanding Machine Learning by Application
Appendix A: The Basic Genetic Algorithm (GA) for Search Optimization
Appendix B: Basic Linear Algebra in Machine Learning Techniques
Problems 
References
Index

Overview


Now in its second edition, Applied Machine Learning continues to explore the theoretical
underpinnings of learning and equips the readers with the knowledge needed to apply powerful
machine learning techniques to solve challenging real-world problems. This book shows in a
step-by-step manner how to conceptualize problems, accurately represent data, select and tune
algorithms, interpret and analyze results, and make informed strategic decisions. Presented in a
non-rigorous mathematical style, the book covers a broad array of machine learning methods that
have been profitably employed. It also provides a platform for hands-on experience through case
studies and exercise experiments.


Key Features


• Simple and lucid explanation of all basic concepts and techniques of machine learning, well supported with case studies and exercise experiments
• New sections on widely used techniques, such as log loss (cross entropy) metric for classification, imbalanced data problems and solutions, Ridge and LASSO regression, multiclass logistic regression, softmax function, and many more
• New chapter on ‘Understanding Machine Learning by Application’ provides a platform for gentle start on hands-on experience through case studies on real-life problems in diverse and important application areas
• Datasets in Excel files for Case studies and Exercise Experiments available online (refer to the complete list on the back inner cover)

• Applied Machine Learning, 2e