Introduction to Machine Learning with Python

Welcome
Table of Content
Title
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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