(E-Book) Combining Machine Learning and Business: Data Science For Optimize, Automate and Accelerate Business Decisions

Description

Combining Machine Learning and Business: Data Science For Optimize, Automate and Accelerate Business Decisions

In today’s rapidly advancing digital era, the application of machine learning in various business aspects has become a crucial key in driving innovation and success. “Combining Machine Learning and Business” is designed to provide deep insights into how the integration of machine learning and business strategy can bring about significant transformation.

14 Chapter :

INTRODUCTION
Chapter 1: Introduction to Machine Learning and Business
Defining Machine Learning
Role of Machine Learning in Business
Benefits of Combining ML and Business
Challenges in Implementing ML in Business
Chapter 2: Understanding Business Data
Types of Business Data
Importance of Data in Business
Data Collection Methods
Data Privacy and Ethics
Chapter 3: Fundamentals of Machine Learning
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Deep Learning
Chapter 4: Applying Machine Learning in Business
Predictive Analysis
Customer Segmentation
Fraud Detection
Sales Forecasting
Chapter 5: Machine Learning Tools for Business
Python for Machine Learning
R for Machine Learning
TensorFlow and Keras
Scikit-Learn
Chapter 6: Preparing Business Data for Machine Learning
Data Cleaning
Data Transformation
Feature Engineering
Data Splitting
Chapter 7: Creating Machine Learning Models for Business
Selecting the Right Algorithm
Training the Model
Evaluating the Model
Improving the Model
Chapter 8: Implementing Machine Learning Models in Business
Model Deployment
Monitoring the Model
Updating the Model
Model Maintenance
Chapter 9: Case Studies: Machine Learning in Different Industries
Machine Learning in Retail
Machine Learning in Healthcare
Machine Learning in Finance
Machine Learning in Manufacturing
Chapter 10: Future of Machine Learning in Business
Trends in Machine Learning
Impact of AI on Business
Role of Big Data in Machine Learning
Challenges and Opportunities
Chapter 11: Building a Machine Learning Team
Roles in a Machine Learning Team
Skills Required for a Machine Learning Team
Hiring and Training a Machine Learning Team
Managing a Machine Learning Team
Chapter 12: Developing a Machine Learning Strategy for Business
Understanding Business Needs
Setting Goals and Objectives
Creating a Roadmap
Implementation and Evaluation
Chapter 13: Legal and Ethical Considerations in Machine Learning
Data Privacy Laws
Ethics in Machine Learning
Bias and Discrimination in M
Responsible AI
Chapter 14: Conclusion: Transforming Business with ML
Recap: Benefits of Machine Learning in Business
Steps for Successful Implementation
Future Outlook
Final Words
This book not only elaborates on concepts and theories but further takes the reader on an applied research journey that has been undertaken.The author of this book, Yusep Maulana, has spent months conducting in-depth research on the application of machine learning technology in business, with a particular focus on case studies in Switzerland and the Netherlands.

Through hands-on experience and direct collaboration with business practitioners in both countries, Yusep has managed to gather valuable data and insights on how machine learning can be integrated into business processes to enhance efficiency, innovation, and competitive advantage.

This book presents the results of that research in an accessible way to readers, whether they are academics, business practitioners, students, or anyone with an interest in the field of machine learning and its application in the business world. It is hoped that readers will gain a broader and more applied understanding of the potential and challenges in combining machine learning with business strategy.