Machine learning is a subfield of artificial intelligence, broadly defined as a machine's capability to imitate intelligent human behavior. Like humans, machines become capable of making intelligent decisions by learning from their past experiences. Machine learning is being employed in many applications, including fraud detection and prevention, self-driving cars, recommendation systems, facial recognition technology, and intelligent computing. This book helps beginners learn the art and science of machine learning. It presens real-world examples that leverage the popular Python machine learning ecosystem,
The topics covered in this book include machine learning basics: supervised and unsupervised learning, linear regression and logistic regression, Support Vector Machines (SVMs). It also delves into special topics such as neural networks, theory of generalisation, and bias and fairness in machine learning. After reading this book, computer science and engineering students - at college and university levels - will receive a complete understanding of machine learning fundamentals and will be able to implement neural network solutions in information systems, and also extend them to their advantage.
Author(s): Chopra, Deepti;Khurana, Roopal;
Edition: 1
Publisher: Bentham Science Publishers
Year: 2023
Language: English
Pages: 139
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General:
FOREWORD
PREFACE CONSENT FOR PUBLICATION
CONFLICT OF INTEREST
ACKNOWLEDGEMENT
Introduction to Python Abstract
INTRODUCTION Web Development
Game Development
Artificial Intelligence and Machine Learning
Desktop GUI
SETTING UP PYTHON ENVIRONMENT Steps Involved In Installing Python On Windows Include The Following:
Steps involved in installing Python on Macintosh include the following
Setting Up Path
Setting Up Path In The Unix/linux
WHY PYTHON FOR DATA SCIENCE?
ECOSYSTEM FOR PYTHON MACHINE LEARNING
ESSENTIAL TOOLS AND LIBRARIES Jupyter Notebook Pip Install Jupiter
NumPy
Pandas
Scikit-learn
SciPy
Matplotlib
Mglearn
PYTHON CODES
CONCLUSION
Exercises
REFERENCES
Introduction To Machine Learning Abstract
INTRODUCTION
DESIGN A LEARNING SYSTEM Selection Of Training Set
Selection Of Target Function
Selection Of A Function Approximation Algorithm
PERSPECTIVE AND ISSUES IN MACHINE LEARNING Issues In Machine Learning Quality of Data
Improve the Quality of Training
Overfitting the Training Data
Machine Learning Involves A Complex Process
Insufficient training data
Feasibility of Learning An Unknown Target Function Collection of Data
Pre-processing of Data Dealing with Null Values
Standardization
Dealing with Categorical Variables
Feature Scaling
Splitting the Data
Finding The Model That Will Be Best For The Data
Training and Testing Of The Developed Model Evaluation
In Sample Error and Out of Sample Error
Applications of Machine Learning
Virtual Personal Assistants
Traffic Prediction
Online Transportation Networks
Video Surveillance System
Social Media Services People you May Know
Face Recognition
Similar Pins
Sentiment Analysis
Email Spam and Malware Filtering
Online Customer Support
Result Refinement of a Search Engine
Product Recommendations
Online Fraud Detection
Online Gaming
Financial Services
Healthcare
Oil and Gas
Self-driving Cars
Automatic Text Translation
Dynamic Pricing
Classification of News
Information Retrieval
Robot Control
CONCLUSION
Exercises
REFERENCES
Linear Regression and Logistic Regression Abstract
INTRODUCTION
LINEAR REGRESSION Linear Regression In One Variable
Linear Regression In Multiple Variables
Overfitting and Regularization In Linear Regression
GRADIENT DESCENT
POLYNOMIAL REGRESSION Features of Polynomial Regression
LOGISTIC REGRESSION Overfitting and Regularisation in Logistic Regression
BINARY CLASSIFICATION AND MULTI-CLASS CLASSIFICATION Binary Classification Tests Classification Accuracy
Error Rate
Sensitivity
Specificity
PYTHON CODES
CONCLUSION
Exercises
REFERENCES
Support Vector Machine Abstract:
INTRODUCTION
SUPPORT VECTOR CLASSIFICATION The Maximal Margin Classifier
Soft Margin Optimization
Linear Programming Support Vector Machines
SUPPORT VECTOR REGRESSION Kernel Ridge Regression
Gaussian Processes
APPLICATIONS OF SUPPORT VECTOR MACHINE Text Categorisation
Image Recognition
Bioinformatics
PYTHON CODE
CONCLUSION
Exercises
REFERENCES
Decision Trees
Abstract:
INTRODUCTION
REGRESSION TREES
STOPPING CRITERION AND PRUNING LOSS FUNCTIONS IN DECISION TREE
CATEGORICAL ATTRIBUTES, MULTIWAY SPLITS AND MISSING VALUES IN DECISION TREES
ISSUES IN DECISION TREE LEARNING Preventing Overfitting of Data
Incorporating Continuous Valued Attributes
Other Measures for Attributes Selection
Handling Missing Values
Handling of Attributes with Differing Costs
INSTABILITY IN DECISION TREES
PYTHON CODE
CONCLUSION
Exercises
REFERENCES
Neural Network Abstract :
INTRODUCTION
EARLY MODELS
PERCEPTRON LEARNING
BACKPROPAGATION
AN ILLUSTRATIVE EXAMPLE: FACE RECOGNITION
STOCHASTIC GRADIENT DESCENT
ADVANCED TOPICS IN ARTIFICIAL NEURAL NETWORK Alternative Error Functions
Alternative Error Minimization Mechanism
Recurrent Networks
Dynamically Modifying Network Structures
PYTHON CODES
CONCLUSION
Exercises
REFERENCES
Supervised Learning Abstract:
INTRODUCTION
USING STATISTICAL DECISION THEORY Gaussian or Normal Distribution
Conditionally Independent Binary Components
LEARNING BELIEF NETWORKS
NEAREST-NEIGHBOUR METHODS
CONCLUSION
Exercises
REFERENCES
Unsupervised Learning Abstract:
INTRODUCTION
CLUSTERING K-means Clustering
Hierarchical Clustering
Principal Component Analysis (PCA)
PYTHON CODE
CONCLUSION
Exercises
REFERENCES
Theory of Generalisation Abstract
INTRODUCTION
BOUNDING THE TESTING ERROR
VAPNIK CHERVONENKIS INEQUALITY
PROOF OF VC INEQUALITY
CONCLUSION
Exercises
REFERENCES
Bias and Fairness in Ml Abstract
INTRODUCTION
HOW TO DETECT BIAS?
HOW TO FIX BIASES OR ACHIEVE FAIRNESS IN ML?
CONFIDENCE INTERVALS
HYPOTHESIS TESTING
COMPARING LEARNING ALGORITHMS
CONCLUSION
Exercises
REFERENCES
APPENDIX CONCLUSION
