Whether you want to build a neural network model or use your intuition to make a decision, something that you absolutely need no matter what, is a dataset. Data is the new oil. Without it, nothing can run efficiently. So where do you get datasets for machine learning training? Well, in case you are a student and trying to sharpen your machine learning skills, there are loads of public repositories available that can provide you with various types of datasets, of data that are available in the public domain. One way to find datasets available in public domain is to use the Google Dataset Search.

How are datasets for machine learning training actually used?

Training a ML model can be simple enough, and is mostly done in languages like Python, or Java. Often datasets contain null or missing values, and these have to be addressed before you build a model. You need to make sure of the uniformity of the data. Once these things are taken care of, and you have divided the data into training and testing sets, you can decide which algorithm you want to use for creating the model. Depending on the type of data you have and the problem statement that you are trying to solve, there can be multiple algorithms which can be tried out.

What are some easy ways to begin training machine learning models with your own datasets?

Implementation of the algorithms are no more a hard task, thanks to libraries like scikit-learn and pandas. You can easily import these libraries into python and use their inbuilt functions to build and test your model. This way, you need not know how complex algorithms like random forest or dnn are written or the complex maths behind them. You just need to know how to use the high level APIs. But of course proficiency in at least Python would be required in order to go about doing all the steps such as data cleansing, re-organizing, restructuring and then finally building the model.

Where to download datasets for machine learning training?

When you require some dataset for your business, such as government domain data, or suppose competitor data, it might be very difficult to get the data in a consumable form even if the data is actually present in the public domain. The reason is that the data might be spread across millions of web-pages (in case of e-commerce websites) or the data might only be accessible to some companies with licenses.

When you’re trying to build your machine learning models and is in need of data, it’s recommended that you download the datasets from DataStock – our web  data repository with datasets from various industries.

With DataStock, you can simply browse and select the datasets you need and go ahead with the purchase. The datasets are available in ready-to-use formats such as csv, xml and json. This makes it easier for you to focus on building the right machine learning model that will help your business grow, by making better market predictions.