Specialized Book: Guidelines for Data Analysis and Introduction to AI and Machine Learning

Explaining the practical know-how for utilizing data required for accelerating DX (Digital Transformation) and IoT implementation!

★【The Key to Project Success!】 Practical Techniques for Data Preprocessing Explained ⇒ Methods for feature generation according to data types, techniques for data augmentation and transfer learning, considerations for handling missing values and methods for imputation, removal, and replacement, which preprocessing techniques to use and how to determine that, strategies to prevent leakage (unwanted data contamination), a list of convenient tools for processing, and more... explained by active data scientists! ★ How to Approach Data Analysis and Examples by Field! ⇒ Techniques useful in practice, including common issues that engineers on the ground can relate to and how to address them, are mentioned throughout! ★ How to correctly handle "technologies and methods," including AI (artificial intelligence), by working backward from customer and company objectives? ★ Steps and considerations for efficiently acquiring high-quality data ★ Which metrics should be used to evaluate machine learning models? What is the recent focus on explainable AI (XAI)? ★ What are the factors behind the failure of many companies in implementing AI and machine learning? What are the commonalities of successful projects? ★ How should organizations be structured for AI implementation, what know-how is needed for operating developed products, and how should business profitability be evaluated?

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