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Introduction

Automated machine learning (AutoML) platforms have emerged as a popular tool in the field of machine learning, enabling users to build machine learning models without the need for extensive programming or data science expertise. These platforms automate the process of selecting the best machine learning algorithms, feature engineering, hyperparameter optimization, and model evaluation, thereby making machine learning more accessible to a wider audience. This thesis aims to provide a comprehensive overview of AutoML platforms, including their capabilities, limitations, and applications.

Chapter 1: Introduction
1.1 Introduction
1.2 Background of study
1.3 Problem Statement
1.4 Objective of study
1.5 Limitation of study
1.6 Scope of study
1.7 Significance of study
1.8 Structure of the Thesis
1.9 Definition of Terms

Chapter 2: Literature Review
2.1 Overview of AutoML platforms
2.2 Evolution of AutoML technologies
2.3 Comparison of AutoML platforms
2.4 Challenges in AutoML
2.5 Applications of AutoML
2.6 Ethical considerations in AutoML
2.7 AutoML in industry
2.8 AutoML in research
2.9 Future trends in AutoML
2.10 Conclusion

Chapter 3: Research Methodology
3.1 Research design
3.2 Data collection
3.3 Data preprocessing
3.4 Model selection
3.5 Hyperparameter tuning
3.6 Model evaluation
3.7 Performance metrics
3.8 Experimental setup

Chapter 4: Discussion of Findings
4.1 Overview of findings
4.2 Performance comparison of AutoML platforms
4.3 Impact of feature engineering on model performance
4.4 Interpretability of AutoML models
4.5 Scalability of AutoML platforms
4.6 Limitations of AutoML platforms
4.7 Integration of AutoML in existing workflows
4.8 Recommendations for future research

Chapter 5: Conclusion
5.1 Summary of the project
5.2 Contributions to the field of AutoML
5.3 Implications for practice
5.4 Future research directions
5.5 Conclusion

Thesis Overview on Automated Machine Learning (AutoML) Platforms

Automated machine learning (AutoML) platforms have revolutionized the field of machine learning by automating the process of building and deploying machine learning models. These platforms have enabled users with varying levels of expertise to leverage the power of machine learning for various applications, from predictive analytics to image recognition. This thesis provides a comprehensive overview of AutoML platforms, including their functionalities, advantages, limitations, and applications.

Chapter 1 gives an introduction to the thesis, providing background information on AutoML platforms, defining the problem statement, stating the objectives of the study, discussing the limitations and scope of the study, highlighting the significance of the study, and outlining the structure of the thesis. Chapter 2 delves into a literature review of AutoML platforms, discussing their evolution, comparison, challenges, applications, ethical considerations, industry and research applications, and future trends.

Chapter 3 details the research methodology used in the study, including the research design, data collection, preprocessing, model selection, hyperparameter tuning, model evaluation, performance metrics, and experimental setup. Chapter 4 presents a comprehensive discussion of the findings, analyzing the performance comparison of AutoML platforms, the impact of feature engineering, interpretability of models, scalability, limitations, and recommendations for future research.

Chapter 5 concludes the thesis by summarizing the key findings, contributions to the field of AutoML, implications for practice, future research directions, and a final conclusion. The thesis aims to provide a thorough understanding of AutoML platforms and their role in advancing the field of machine learning.

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