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Introduction:

AutoML (Automated Machine Learning) is a cutting-edge technology that aims to automate the process of model selection and tuning, making it easier and more efficient for data scientists to build high-performing machine learning models. By automating the tedious and time-consuming tasks involved in machine learning, AutoML allows researchers and practitioners to focus on higher-level tasks such as problem formulation and interpretation of results.

Table of Contents:

Chapter 1: Introduction
1.1 Background of AutoML
1.2 Objective of Study
1.3 Limitation of Study
1.4 Scope of Study

Chapter 2: Literature Review
2.1 Overview of Machine Learning
2.2 Evolution of AutoML
2.3 Techniques and Algorithms in AutoML
2.4 Applications of AutoML

Chapter 3: Research Methodology
3.1 Data Collection
3.2 Preprocessing
3.3 Model Selection and Tuning
3.4 Evaluation Metrics

Chapter 4: Discussion of Findings
4.1 Comparative Analysis of AutoML Techniques
4.2 Performance Evaluation of AutoML Models
4.3 Benefits and Challenges of Using AutoML
4.4 Future Directions for AutoML Research

Chapter 5: Conclusion and Summary
5.1 Summary of Findings
5.2 Contributions to the Field
5.3 Implications for Practice
5.4 Recommendations for Future Research

Thesis Overview:

AutoML for Automated Model Selection and Tuning is a comprehensive study that explores the latest advancements in automated machine learning techniques. The thesis begins with an introduction to AutoML, providing a background on its importance and relevance in the field of machine learning. The objective of the study is to evaluate the effectiveness of AutoML in improving the efficiency and accuracy of model selection and tuning processes. The limitations and scope of the study are also outlined to provide a clear understanding of the research focus.

The literature review in Chapter 2 covers key concepts in machine learning, the evolution of AutoML, different techniques and algorithms used in AutoML, and various applications of AutoML across different domains. Chapter 3 details the research methodology, including data collection, preprocessing techniques, model selection and tuning strategies, and evaluation metrics for assessing the performance of AutoML models.

Chapter 4 presents the discussion of findings, where a comparative analysis of AutoML techniques is conducted, the performance of AutoML models is evaluated, and the benefits and challenges of using AutoML are discussed. The chapter also highlights future research directions for AutoML and its potential impact on the field of machine learning.

Finally, Chapter 5 provides a conclusion and summary of the thesis, summarizing the key findings, contributions to the field, implications for practice, and recommendations for future research. Overall, the thesis offers a comprehensive overview of AutoML for Automated Model Selection and Tuning, highlighting its potential to revolutionize the way machine learning models are built and optimized.

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