Table of Content

1.0 Executive Summary
1.1 Research Scope
1.2 Research Methodology

2.0 Unsupervised Learning – Introduction
2.1 Unsupervised Learning Lays the Framework for Truly Automated Machine Learning Where Human Intervention Is Minimal
2.2 Unsupervised Learning Works Accurately with Large Datasets Which Cannot be Labeled Manually
2.3 Clustering of Data into Groups Makes Them More Suitable and Understandable for Further Analysis
2.4 A Variety of Data Clustering Methods Based on Unsupervised Learning can be Used Based on the Type of Dataset and the Objectives
2.5 Dimensionality Reduction Techniques Play a Key Role in Prepping up Large Datasets for Analysis
2.6 Autonomy and Minimal Human Intervention in Unsupervised Learning Systems Create Ambiguity in Output

3.0 Innovations and Companies to Action
3.1 Unsupervised Learning Will Empower a Higher Level of Autonomy Among Self-driving Cars
3.2 Unsupervised Learning can Help NLP Systems Learn More Easily and Rapidly with Unknown Languages and Accents
3.3 Unique Financial Frauds with no Precedent can Be More Accurately Identified with Unsupervised Learning Methods
3.4 Identifying Outliers From Datasets Is a Key Strength of UL Systems, Making Them Fit for Detecting Malicious Behavior
3.5 Cybersecurity is Emerging as a Key Area of Innovation for Unsupervised Learning

4.0 Growth Opportunity
4.1 Pursuit of Greater Degree of Autonomy Among Self-driving Cars Is Facilitating the Adoption of Unsupervised Learning Techniques
4.2 The Accuracy of Artificial Intelligence System Is Highly Dependent on the Quality of Training Data Used to Train Algorithms
4.3 Industry-academia Collaborations can Accelerate the Pace of Commercial Adoption of Unsupervised Learning

5.0 Industry Contacts
5.1 Key Contacts
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