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      Smartwatches and fitness bands: AI in wearables tracks
                        steps,  heart rate, sleep  quality  and daily  movement. It
                        studies  these  patterns  to  understand  the  user’s  habits
                        and overall wellbeing. Based  on this  data,  it offers
                        personalised  suggestions to  improve  fitness  and rest.

                        Over time, the device becomes more accurate as it learns
                        how the user behaves each day.

                     HOW DOES AI LEARN?

                     The effectiveness of Artificial  Intelligence  largely depends  on data.  Artificial  Intelligence  and
                     Machine  Learning systems  learn by  analysing  large amounts  of information  and identifying
                     meaningful patterns. The more data these systems receive, the better they become at performing
                     tasks and making decisions.

                     AI systems use data to recognise trends, understand relationships and generate useful insights.
                     This learning process  helps machines  improve  their accuracy  and efficiency  over time.  With
                     continuous exposure to new data, AI systems become more capable of handling complex tasks
                     and adapting to changing environments.

                     For example, AI-powered systems such as spam filters, online shopping recommendations and
                     weather prediction applications appear intelligent because they are trained using vast datasets.
                     Similarly, AI in education platforms suggests learning materials based on student performance,
                     while banking systems detect unusual transactions to prevent fraud. These systems continuously
                     learn from new data and improve their performance.
                     Therefore, the  accuracy, reliability  and performance  of AI-driven applications  depend  on the

                     quality and quantity of data used for training. This is why data quality is extremely important in AI
                     development. Poor data can lead to incorrect predictions. Therefore, proper data collection and
                     preparation are essential.

                     AI PROJECT

                     Creating an AI application may sound complex, but it follows a clear and structured process.
                     An AI project is a planned way of developing a smart system that can learn from data and solve
                     real-life problems. Each step is carried out carefully so that the system works properly and gives
                     correct results.

                     You can understand this with a simple daily life example. Suppose you want to organise your
                     school library. First, you decide your goal, which is to arrange books so they are easy to find. In
                     the same way, every AI project begins with a clear goal, such as recommending songs, sorting
                     photos, recognising faces or detecting spam emails.
                     After setting the goal, the project moves step by step. First, the problem is clearly identified. Then,

                     useful data is collected from different sources. Next, the data is cleaned and organised to make
                     it accurate and easy to use.

                                                                                               AI Project Lifecycle    11

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