What datasets are best for beginner AI projects?
For early stage AI projects, it's best to select clean, well-documented, and easy to comprehend datasets. Some of the most common datasets include the Iris dataset, used in classification tasks, the MNIST dataset in handwritten digit representation, and the Titanic dataset in binary classification based on passengers. The advantage of using these datasets is they are small in terms of size, require very little preprocessing, and come with complete community support. Some additional useful datasets include the Boston Housing dataset for regression-based problems, and COCO or CIFAR-10 if you want to specifically tackle image-based problems. Using these datasets, beginners can learn fundamental machine learning and deep learning processes, and won't be overwhelmed.
By registering for an Artificial Intelligence Course in Pune, learners will be able to explore these beginner datasets in a course-like curriculum where they can also have hands-on labs where they can practice all the methods using these datasets to build and train models.
Furthermore, Artificial Intelligence Training in Pune offers projects and experience whereby, learners can use real-world data, which helps them understand the importance of preparing the data, how to evaluate a model, and how to improve it. Quality training and projects, along with mentorship from qualified teachers, help learners develop the comfort needed to take on growingly complex AI problems. With these beginner datasets and quality training, there is a gradual learning curve for anyone new to AI.
