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    STEP - 2   Training  the  model:  Once the  data  is  ready,  the  tool  uses  it  to  train a  machine
                                  learning model. During training, the model learns to recognise patterns and features
                                  in the data so it can predict the correct category when it sees new input.

                      STEP - 3   Testing the model: After training, you can test your model in real time. This allows
                                  you to see how accurately it recognises new images, sounds or poses. You can also
                                  improve the model’s accuracy by adding more data or adjusting categories.

                      STEP - 4   Using the model: Once you are satisfied with the model’s performance, it can be
                                  exported and integrated into websites, apps or other digital projects. This allows you
                                  to apply your AI model in real-world applications such as games, educational tools
                                  or assistive technologies.

                          Ask AIRO

                          Create a Rock, Paper, Scissors Model using Google Teachable Machine.

                     How to Improve Your Model?

                     To make  your model  smarter and more  accurate,  follow these  steps  to  refine and enhance
                     its overall performance:
                        Add more data: The more examples you provide, the better your model will learn. Try to
                        collect a variety of data covering different scenarios, so the model can recognise patterns
                        more accurately.

                        Use clear and varied data: Make sure your data is clean, clear and high in quality. Include
                        different examples  so your model  can handle different situations,  like  changes in lighting
                        or angles.
                        Test the model: Regularly test how well your model performs with new data. This helps you see
                        what needs improvement and where it is doing well.

                        Adjust  settings: You can adjust  the settings in Google  Teachable Machine, like  training
                        time  and  learning parameters.  Fine-tuning  these  settings  can help the  model  learn
                        more effectively.
                        Retrain the model: After adding data or adjusting the settings, retrain the model. This helps it
                        learn from the new information and improve its predictions.

                     CREATING A PROJECT USING GOOGLE TEACHABLE MACHINE

                     To create a new project in Google Teachable Machine, follow the given steps:

                      STEP - 1   Open the Web browser.

                      STEP - 2   Type the URL https://teachablemachine.withgoogle.com in the address
                                  bar and press the Enter key or scan the QR code.

                                                                        Introduction to Google Teachable Machine
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