Models of Learning and Optimization for Data Scientists
Models of Learning and Optimization for Data Scientists
A Python hands-on approach
About the Book
Data is nowadays ubiquitous, voluminous and puzzling. It is not surprise that scientists are so interested in analysing it, understanding it and discovering underlying complex patterns within it. And that’s the origin of what is known as Data Science. But as much as the scientific interest in this respect is growing, so it is practitioners curiosity about potential applications in real life and development of technological tools for Data Science in non-academic contexts.
This book has been designed to introduce newcomers to the essentials of Data Science using a hands-on approach rather than a theoretical perspective. For this aim, it addresses two of its most important branches: Machine Learning and Metaheuristics. The book presents many introductory examples as well as an assortment of challenges with varying levels of difficulty, for readers to solve them using the Python programming language, the current tool–of–choice adopted by the Data Science community. These challenges (nearly 90 programming exercises) will help readers to acquire skills that hopefully will foster their academic or industry interests involving data analysis for knowledge discovery.
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About the Author
Table of Contents
Contents
1 Introduction 5
2 Machine Learning Models 7
2.1 The big picture............................. 8
2.2 Exploratory data analysis....................... 10
2.3 Distance metrics............................ 15
2.4 Classification problems........................ 17
2.5 Majority vs Random class....................... 21
2.6 Nearest neighbour classifier ..................... 22
2.7 Classification trees .......................... 23
2.8 Choosing the best model ....................... 27
2.9 Perceptron learning .......................... 33
2.10 Clustering................................ 37
2.11 Suggested readings.......................... 40
3 Metaheuristic Methods ................... 41
3.1 The big picture............................. 42
3.2 Visual insights ............................. 44
3.3 Exhaustive search........................... 48
3.4 Random search ............................ 53
3.5 An object-oriented approach ..................... 54
3.6 Hill Climbing .............................. 59
3.7 Random Walk.............................. 70
3.8 Simulated Annealing.......................... 71
3.9 Genetic Algorithms .......................... 74
3.10 Fitness function ............................ 79
3.11Genetic operators ........................... 80
3.12 Metaheuristics benchmarks ..................... 88
3.13 Estimation of Distribution Algorithms ................ 92
3.14 Suggested readings.......................... 96
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