Applied Machine Learning

By (author) Gopal M.
Description
Publisher''s Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product.
Cutting-edge machine learning principles, practices, and applications
This comprehensive textbook explores the theoretical under¬pinnings of learning and equips readers with the knowledge needed to apply powerful machine learning techniques to solve challenging real-world problems. Applied Machine Learning shows, step by step, 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 topics with special emphasis on methods that have been profitably employed.
Coverage includes:
•Supervised learning•Statistical learning•Learning with support vector machines (SVM)•Learning with neural networks (NN)•Fuzzy inference systems•Data clustering•Data transformations•Decision tree learning•Business intelligence•Data mining•And much more

Table of contents
Dedication
Contents
Preface
Acknowledgements
1. Introduction
1.1 Towards Intelligent Machines
1.2 Well-Posed Machine Learning Problems
1.3 Examples of Applications in Diverse Fields
1.4 Data Representation
1.4.1 Time Series Forecasting
1.4.2 Datasets for Toy (Unreastically Simple) and Realistic Problems
1.5 Domain Knowledge for Productive use of Machine Learning
1.6 Diversity of Data: Structured/Unstructured
1.7 Forms of Learning
1.7.1 Supervised/Directed Learning
1.7.2 Unsupervised/Undirected Learning
1.7.3 Reinforcement Learning
1.7.4 Learning Based on Natural Processes: Evolution, Swarming, and Immune Systems
1.8 Machine Learning and Data Mining
1.9 Basic Linear Algebra in Machine Learning Techniques
1.10 Relevant Resources for Machine Learning
2. Supervised Learning: Rationale and Basics
2.1 Learning from Observations
2.2 Bias and Variance
2.3 Why Learning Works: Computational Learning Theory
2.4 Occam’s Razor Principle and Overfitting Avoidance
2.5 Heuristic Search in Inductive Learning
2.5.1 Search through Hypothesis Space
2.5.2 Ensemble Learning
2.5.3 Evaluation of a Learning System
2.6 Estimating Generalization Errors
2.6.1 Holdout Method and Random Subsampling
2.6.2 Cross-validation
2.6.3 Bootstrapping
2.7 Metrics for Assessing Regression (Numeric Prediction) Accuracy
2.7.1 Mean Square Error
2.7.2 Mean Absolute Error
2.8 Metrics for Assessing Classification (Pattern Recognition) Accuracy
2.8.1 Misclassification Error
2.8.2 Confusion Matrix
2.8.3 Comparing Classifiers Based on ROC Curves
2.9 An Overview of the Design Cycle and Issues in Machine Learning
3. Statistical Learning
3.1 Machine Learning and Inferential Statistical Analysis
3.2 Descriptive Statistics in Learning Techniques
3.2.1 Representing Uncertainties in Data: Probability Distributions
3.2.2 Descriptive Measures of Probability Distributions
3.2.3 Descriptive Measures from Data Sample
3.2.4 Normal Distributions
3.2.5 Data Similarity
3.3 Bayesian Reasoning: A Probabilistic Approach to Inference
3.3.1 Bayes Theorem
3.3.2 Naive Bayes Classifier
3.3.3 Bayesian Belief Networks
3.4 k-Nearest Neighbor (k-NN) Classifier
3.5 Discriminant Functions and Regression Functions
3.5.1 Classification and Discriminant Functions
3.5.2 Numeric Prediction and Regression Functions
3.5.3 Practical Hypothesis Functions
3.6 Linear Regression with Le