Fun with Machine Learning: Simplify the Data Science process by automating repetitive and complex tasks using AutoML
- Length: 250 pages
- Edition: 1
- Language: English
- Publisher: BPB Publications
- Publication Date: 2023-03-23
- ISBN-10: 9355517858
- ISBN-13: 9789355517852
- Sales Rank: #0 (See Top 100 Books)
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.
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
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