While mastering technical skills, it is very critical to do hands-on practice. Projects are the best gateways to achieve that. It is recommended that you visit multiple data science platforms, such as Kaggle, UCI Machine Learning Repo, OpenML, etc. Get the datasets from there, understand the...
Kaggle HuggingFace Twitter: @CatalystCoopAbout Example Jupyter notebooks hosted on Kaggle that demonstrate how to work with US energy data from PUDL. www.kaggle.com/datasets/catalystcooperative/pudl-project Topics python data-science data tutorial energy jupyter sqlite example jupyter-notebook kaggle...
Computer programming skills serve as the backbone of AI development. Simple computer skills and understanding of data structures are significant to implement AI solutions. Knowing how to work with and analyze data becomes vital since AI projects often involve large datasets. Choosing the Right Programmi...
And to work on real-world projects, you need to find the relevant data to explore. For this, there are various online platforms that you can refer to like: Kaggle– A community platform for data science discovery and collaboration that includes datasets, contests, and tools. UCI Machine Learn...
5.Uploading to Kaggle: Once your dataset is ready, you can upload it to Kaggle in the Datasets section. Make sure to provide a descriptive title, clear description, and relevant tags to make it easily discoverable by others. 6.Sharing and Collaboration: Share your dataset with the Kaggle com...
Practicing your skills and solving mock or real-world problems will give you a solid basis for your future work experience. At this stage, having access to some real, clean datasets and preselected ideas to explore will help maintain your interest in learning and avoid the distractions of ...
To avoid reinventing the wheels and get inspired on how to preprocess, engineer and model the data, it's worth spend 1/10 to 1/5 of the project time just researching how people previously dealt with similar problems/datasets. Some good places to start are: No Free Hunch: the official ...
By finding all the answers, you're sure to learn some interesting things along the way.How to Work With Missing Data in Polars When you’re dealing with missing data in Polars, there are several things you can do: Recover it Remove it Replace it Ignore it By far the best plan of ...
Deep neural networks (DNNs) have demonstrated exceptional performance across various image segmentation tasks. However, the process of preparing datasets f
Go to Kaggle’s website. Either go to ‘Datasets’ (on the menu at the top of the screen) or ‘Notebooks’ (same place). Find something that looks interesting. Either read it carefully or duplicate it entirely. This gives you two ways of tracking down learning materials. Either you ...