How to train and test an AI model with real datasets?
To train and test an AI model with real datasets, you must complete the key tasks: First, gather a dataset from one of the public datasets that are available (such as Kaggle, UCI Machine Learning Repository or company databases), and then preprocess the dataset; in preprocessing, notice and complete all the missing values, encode any categorical variables, and scale all the features. After the dataset is cleaned, you may divide the training set and test cases, using an 80/20 split. It is the portion of your dataset that is used to test the AI model after it has been trained, so the percentage may vary by project requirement. Choose an appropriate algorithm for the task based on the type of problem you are trying to solve (ex: classification versus regression), and then use the training set to train the model. You will want to evaluate the model's performance on the test set with metrics that are appropriate for your project, such as accuracy, precision, or recall in classification problems, or root mean squared error in regression. If necessary, retraining and tuning the parameters of the AI model for better accuracy can be performed over periods of time and repeated iterations until you can achieve the project metrics.
A course on Artificial Intelligence Course in Pune is a good way to gain real experience with handling your own datasets with AI. The courses usually provide practical labs where you will go through the process of preprocessing, training, testing AI models, step by step, in a best practice setting.
In addition, the Artificial Intelligence Training in Pune, you will obtain a large amount of knowledge about the tools associated with Artificial Intelligence, such as Python, Pandas, Scikit-learn, and TensorFlow. By taking the course, you will gather hands-on knowledge and practical skills to use these tools for data in any real settings, ensuring that your AI models are both accurate and robust for real life application.
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