In today's data-driven world, the ability to make sense of complex, high-dimensional datasets is crucial for economists and data scientists. Traditional quantitative methods, while powerful, often struggle to keep up with the complexities of modern economic challenges. This book bridges this gap, integrating cutting-edge machine learning techniques with established economic analysis to provide new, more accurate insights. The book offers a comprehensive approach to understanding and applying neural networks and deep learning models in the context of conducting economic research. It starts by laying the groundwork with essential quantitative methods such as cluster analysis, regression, and factor analysis, then demonstrates how these can be enhanced with deep learning techniques like recurrent neural networks (RNNs), convolutional neural networks (CNNs), and transformers. By guiding readers through real-world examples, complete with Python code and access to datasets, it showcases the practical benefits of neural networks in solving complex economic problems, such as fraud detection, sentiment analysis, stock price forecasting, and inflation factor analysis. Importantly, the book also addresses critical concerns about the "black box" nature of deep learning, offering interpretability techniques like Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP) to demystify model predictions. The book is essential reading for economists, data scientists, and professionals looking to deepen their understanding of AI's role in economic modeling. It is also an accessible resource for non-experts interested in how machine learning is transforming economic analysis.