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AI in Financial Modeling: Advanced Predictive Techniques Course
Original price was: INR ₹10,998.00.INR ₹5,499.00Current price is: INR ₹5,499.00.
Course Overview
AI in Financial Modeling: Advanced Predictive Techniques is a 12-week advanced course designed for M.Tech and M.Sc students in Computer Science and IT, as well as professionals in the BFSI, analytics, and fintech sectors. The course covers the application of AI in financial modeling, including machine learning algorithms, neural networks, and natural language processing to interpret and predict financial outcomes. Participants will engage in video lectures, hands-on labs, and live expert sessions to learn how to develop and implement AI models that drive financial decisions and strategies.
AI in Financial Modeling: Advanced Predictive Techniques
AI, Financial Modeling, Predictive Analysis, Machine Learning, Neural Networks, Natural Language Processing, Risk Assessment, Investment Strategies, Data Management, Feature Engineering, Time-Series Forecasting, Ethics, Regulatory Compliance
Course Overview
AI in Financial Modeling: Advanced Predictive Techniques is a 12-week advanced course designed for M.Tech and M.Sc students in Computer Science and IT, as well as professionals in the BFSI, analytics, and fintech sectors. The course covers the application of AI in financial modeling, including machine learning algorithms, neural networks, and natural language processing to interpret and predict financial outcomes. Participants will engage in video lectures, hands-on labs, and live expert sessions to learn how to develop and implement AI models that drive financial decisions and strategies.
Course Goals
This course aims to provide participants with an in-depth understanding of how artificial intelligence can revolutionize financial modeling through enhanced decision-making and predictive analysis. The program is designed to equip participants with the skills needed to apply AI in analyzing stock markets, assessing risks, and optimizing investment strategies.
Program Objectives
- Understand AI in Financial Contexts: Grasp the role of AI in financial modeling and decision-making processes.
- Develop Predictive Models: Learn to create and use various AI-driven predictive models for financial analysis.
- Practical AI Application: Gain hands-on experience applying AI tools like Python, R, and TensorFlow to financial datasets.
- Ethical and Regulatory Insights: Navigate the ethical and regulatory challenges of implementing AI in financial environments.
Program Structure
- Introduction to AI in Financial Modeling: Learn the foundations of financial modeling and how AI enhances financial decision-making. Gain insights into AI technologies like machine learning and deep learning, and their applications in finance.
- Data Management and Preprocessing: Understand the sources and management of financial data crucial for AI modeling. Explore data quality, preprocessing, and feature engineering techniques to prepare data for effective AI analysis.
- Advanced Predictive Modeling Techniques: Dive into time-series forecasting using models like ARIMA and LSTM networks to predict market trends. Study risk management models, including credit risk modeling and market risk prediction using AI.
- Implementing AI Models: Learn about integrating AI models with financial systems, including APIs and real-time data feeds. Address the challenges and solutions in deploying AI models within financial environments.
- Ethics and Regulation: Discuss ethical considerations and regulatory compliance issues relevant to AI in finance.
Eligibility
- Students: M.Tech, M.Sc in Computer Science, IT, and related fields.
- Professionals: E0 & E1 level professionals in BFSI, analytics services, and fintech IT services, looking to enhance their skills in AI-driven financial strategies.
Learning Outcomes
- Mastery of AI in Finance: Gain a deep understanding of how AI can be applied in various financial contexts.
- Analytical Skills Enhancement: Improve your ability to analyze data and predict financial outcomes using AI.
- Practical Model Development: Develop skills in creating and implementing AI models for real-world financial problems.
- Readiness for AI Challenges: Be prepared to address ethical and regulatory issues in AI applications within the financial sector.
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