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Procedure
                 Trained technicians capture high-quality images of patients' eyes using specialised cameras.

                 The AI system analyses these digital images to identify the presence of Diabetic Retinopathy.

                 This approach accelerates the detection process and ensures timely diagnosis.
                 Technicians can operate the AI system without the need for a skilled doctor, making it accessible in rural areas.

                 Benefits
                 The AI-driven solution significantly benefits rural populations by enabling early detection and treatment of Diabetic
                 Retinopathy. This reduces the risk of severe vision loss.


                                                                                 #Digital Literacy

                             Video Session
                       Watch the video given below "Deploy and fine-tune large AI models with your data".

                       Visit:  https://www.youtube.com/watch?v=5y0xiHUKBW4  or  scan  the  QR  code  and
                       understand how the AI Project is deployed and fine-tuned.

                       Scan the QR code or visit the given link to watch the video, "Deploy and fine-tune large
                       AI models with your data"

                       https://www.youtube.com/watch?v=5y0xiHUKBW4

                       Scan  the  QR  code  or  visit  the  given  link  to  watch  the  video,  "AI-Powered Pest
                       Management for Cotton Farmers"
                       https://www.youtube.com/watch?v=LP_A4jydmz4





                         AI Project Cycle Mapping Template


                 AI Project Cycle Mapping Template presents how different stages are related to each other and how the functions
                 performed in every phase forms an input for the next phase.
                 The performed task at one stage forms the root for the next stage.

                 AI Project: Customer churn prediction (identifying at-risk customers who are likely to cancel their subscriptions
                 or close/abandon their accounts.)
                    • Problem Scoping
                      ✶ Identify the problem: The telecommunications company wants to reduce customer churn rates.
                      ✶ Define objectives: Develop a predictive model to identify customers at risk of churning.
                    • Data Acquisition

                      ✶ Gather data sources: Collect customer demographics, usage patterns, service history, and churn status data
                      from the company's databases.
                      ✶ Ensure data quality: Clean the data, handle missing values, and remove duplicates.
                    • Data Exploration
                      ✶ Explore the data: Analyse customer demographics, usage patterns, and churn rates through visualisations
                      and statistical summaries.

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