Learn how to use AutoML to leverage Machine Learning for solving business problems
Key Features
● Get familiar with the common machine learning problems and understand how to solve them.
● Understand the importance of different types of data and how to work with them effectively.
● Learn how to use machine learning and AutoML tools to solve real-world problems.
Description
“Fun with Machine Learning” is an essential guide for anyone looking to learn about machine learning and how it can be used to make informed business decisions.
The book covers the basics of machine learning, providing an overview of key concepts and terminology. To fully understand machine learning, it is important to have a basic understanding of statistics and mathematics. The book provides a simple introduction to these topics, making it easy for you to understand the core concepts. One of the key features of the book is its focus on AutoML tools. It introduces you to different AutoML tools and explains how to use them to simplify the data science processes. The book also shows how machine learning can be used to solve real-world business problems, such as predicting customer churn, detecting fraud, and optimizing marketing campaigns.
By the end of the book, you will be able to transform raw data into actionable insights with machine learning.
What you will learn
● Get a clear understanding of what machine learning is and how it works.
● Learn how to perform regression analysis using Orange.
● Understand how to implement classification In machine learning.
● Get to know more about the clustering and association algorithms.
● Analyze, visualize, manipulate, and forecast time series data with Orange.
Who this book is for
This book is for Machine Learning engineers, Machine Learning enthusiasts, Data Scientists, beginners, and students who are looking to implement machine learning techniques to solve real-life business problems. It is also a great resource for business leaders who are responsible for making data-driven decisions.
Author(s): Arockia Liborious; Dr. Rik Das
Publisher: BPB Publications
Year: 2023
Language: English
Pages: 188
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
