AI Project Cycle Stages Notes PDF

**AI Project Cycle** **AI project cycle stages** 1. List the different stages of the AI Project cycle. The different stages of AI Project cycle are: 1. Problem Scoping 2. Data Acquisition 3. Data Exploration 4. Modelling 5. Model Evaluation 2. Define the following a. **Prob...

**AI Project Cycle** **AI project cycle stages** 1. List the different stages of the AI Project cycle. The different stages of AI Project cycle are: 1. Problem Scoping 2. Data Acquisition 3. Data Exploration 4. Modelling 5. Model Evaluation 2. Define the following a. **Problem scoping:-** b. **Data Acquisition:-** - Data acquisition is by collecting data from various reliable and authentic sources. - Since the data you collect would be in large quantities, you can try to give it a visual image of different types of representations like graphs, databases, flow charts, maps, etc which makes it easier for you to interpret the patterns in which your acquired data follows. - Data needs to be accurate and reliable as it ensures the efficiency of your system. c. **Data Exploration** - At the data exploration stage you try to interpret some useful information out of the data you have acquired. - For this, you explore the data and try to put it uniformly for a better understanding. d. **Data Modelling** - After exploring the patterns, you can decide upon the type of model you would build to achieve the goal. - For this, you can research online and select various models which give a suitable output. - You can test the selected models and figure out which is the most efficient one. - The most efficient model is now the base of your AI project and you can develop your algorithm around it. e. **Evaluation** - Once the modelling is complete, you now need to test your model on some newly fetched data. The results will help you in evaluating your model and hence improving it. - Finally, after evaluation, the project cycle is now complete and what you get is your AI project.