Generative AI is a game-changing technology, and now is the time to take maximum advantage of it. In our previous course, we introduced Generative AI and helped attendees learn how to implement its models in real-world problems. This time, we’re going deeper and presenting how to utilize Generative AI at the enterprise level.
In this course, we will build industry-relevant use cases and generate high-level data to train advanced AI models that can solve complex business problems like customer analytics, personalized marketing, fraud detection, risk management, and more. Finally, we will use Generative AI models for underwriting and other documentation purposes. Overall, This course will demonstrate how Generative AI can solve complex business problems more accurately and with less effort.
- Knowledge of developing business-relevant solutions using Generative AI at the application level
- In-depth understanding of applying Generative AI models to solve complex business problems
- Exposure to implementing Generative AI models at the enterprise level for the tasks such as training data generation, customer-based predictions, underwriting, etc.
- Introduction and Overviews
- Generative AI business use cases in BFSI, retail and CPG.
- Building a use case to be solved during the workshop
- Data generation for model training
- Finding relevant data using Generative AI based on the use case
- Generating required data to train machine learning model
- Training the predictive models using synthetic data
- Finding actionable results such as customer segmentation, fraud detection, risk management, etc.
- Personalized offers and recommendations
- Building advanced recommendation model
- Making personalized recommendations and offers to customers
- Optimizing the marketing plan
- Underwriting and documentation
- Utilizing the insights and results from the models and preparing documents
- Creating analytics reports including customers, sales, revenue, etc.
- Generative AI for marketing communications
- Basic understanding of Generative AI, artificial intelligence, machine learning and deep learning.
- Good knowledge of Mathematics and Python programming language.
- Familiarity with Jupyter/Colab Notebook environment.
- Jupyter Notebook / Google Colab
- High-Speed Internet connectivity