Fun with Machine Learning: Simplify the Data Science process by automating repetitive and complex tasks using AutoML

Cover Page
Title Page
Copyright Page
Dedication Page
About the Authors
About the Reviewer
Acknowledgements
Preface
Errata
Table of Contents
1. Significance of Machine Learning in Today’s Business
    Structure
    Objectives
        Hype behind machine learning and data science
            Supervised Learning
            Unsupervised learning
            Reinforcement learning
        Benefits of machine learning in business
        Introducing data
        Types of data in business context
        Challenges with data
        Citizen data science
        Data science for leaders
    Conclusion
    Points to remember
    Multiple choice question
    Answers
2. Know Your Data
    Structure
    Objectives
    Most common data types you will encounter
    Data preparation and understanding the criticality of the process
    Data science journey and impact of clean data
    Mathematical concepts one must remember
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
3. Up and Running With Analytical Tools
    Structure
    Objectives
    Analytical tools that matter and their hardware requirements
    Python workbook and auto ML libraries
    Steps to use analytical tools
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
4. Machine Learning in a Nutshell
    Structure
    Objectives
    Machine learning life cycle and its impact on the business outcomes
    Understanding business need
    Couple business need with data
    Understand and finalize the Mathematics
    Choose the right algorithm
    Break the myth; gone are the days of intuition-based decision-making processes
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
5. Regression Analysis
    Structure
    Objectives
    Types of Machine Learning
        Supervised learning
        Semi-Supervised Learning
        Unsupervised Learning
        Reinforcement Learning
    Basics of Regression Analysis
    Regression process flow
    EDA and statistics for Regression
    Summary of Regression and useful handouts
    Linear Regression using Orange – No Code
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
6. Classification
    Structure
    Objectives
    Get started with classification
    Process flow of classification
    EDA and Statistics of Classification
    Classification using Orange
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
7. Clustering and Association
    Structure
    Objectives
    Get started with Clustering and Association
        Density- based clustering
            Density- Based Spatial Clustering of Applications with Noise (DBSCAN)
    Ordering Points to Identify Clustering Structure
    Hierarchical density- Based spatial clustering applications with Noise
    Hierarchical clustering
    Fuzzy clustering
    Partitioning clustering
    Grid-based clustering
    Association
    Process flow of clustering
    EDA and evaluation metric for clustering
    Clustering using Orange
    Clustering cheat sheet
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
8. Time Series Forecasting
    Structure
    Objectives
    Get started with time series forecasting
    Aspects of time series forecasting
    Types of time series methods
    Autoregressive (AR) model
    Moving average model
    Autoregressive Moving Average (ARMA) Model
    Autoregressive Integrated Moving Average (ARIMA) Model
    Seasonal Autoregressive Integrated Moving Average (SARIMA) Model
    Vector Autoregressive (VAR) Model
    Vector Error Correction Model (VECM)
    Process Flow of Time Series Forecasting
    EDA and Statistics of time series forecasting
    Time series forecasting using Orange
    Time series cheat sheet
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
9. Image Analysis
    Structure
    Objectives
    Get started with Deep Learning
    Image analysis
    What is an Image
    Image processing
    Sources of digital images
        Types of digital images
        Levels of digital image processing
        Applications of digital image processing
    Process flow of image processing
    EDA and Statistics of image processing
    Image analysis using Orange
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
10. Tips and Tricks
    Structure
    Objectives
    Data management tips
        Data Governance
        Data Fallacies
    EDA Tips
        Data observation
        Missing value and outlier treatment
        Correlation Analysis
    Data presentation tips
        Context
        Audience
        Visual
        Focus
        Tell a story
    Machine learning cheat sheet
    Conclusion
    Points to remember
    Multiple choice questions
    Answers
Index