Machine learning
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Importance of Datasets in Machine Learning and AI Research

 Datasets in Machine Learning

The majority of us these days are centered around building machine learning models and taking care of issues with the current datasets. However, we want to initially comprehend what a dataset is, its significance, and its part in building powerful AI arrangements. Today we have an overflow of open-source datasets to do explore on or construct an application to tackle true issues in many fields.

In any case, the absence of value and quantitative datasets are a reason for concern. Information has developed colossally and will keep on developing at a higher speed from now on. All in all, how would we involve the enormous volumes of information in artificial intelligence research? Here we will examine approaches to shrewdly use the current dataset or create the right datasets for the given necessities.

What is a Dataset in Machine Learning?

Dataset is a collection of various types of data stored in a digital format. Data is the key component of any Machine Learning project. Datasets primarily consist of images, texts, audio, videos, numerical data points, etc., for solving various Artificial Intelligence challenges such as

  • Image or video classification
  • Object detection
  • Face recognition
  • Emotion classification
  • Speech analytics
  • Sentiment analysis
  • Stock market prediction, etc.

Why is Dataset Important?

We can not have a Man-made brainpower framework with information. Profound Learning models are information hungry and require a ton of information to make the best model or a framework with high constancy. The nature of information is essentially as significant as the amount regardless of whether you have executed incredible calculations for machine learning models. The accompanying statement best makes sense of the working of an machine learning model.

Trash In Trash Out (GIGO): In the event that we feed bad quality information to machine learning Model it will convey a comparative outcome.

As indicated by The Province of Information Science 2020 report, information readiness and understanding is one of the most significant and tedious undertakings of the AI project lifecycle. Overview shows that most Information Researchers and artificial intelligence engineers invest almost 70% of their energy dissecting datasets. The leftover time is spent on different cycles like model choice, preparing, testing, and organization.

Limitation of Datasets

Finding a quality dataset is an essential prerequisite to construct the groundwork of any genuine computer based intelligence application. Notwithstanding, this present reality datasets are intricate, more chaotic, and unstructured. The exhibition of any AI or Profound Learning model relies upon the amount, quality, and importance of the dataset. It’s anything but a simple undertaking to track down the right equilibrium.

Machine Learning Datasets

We are special to have an enormous corpus of open-source datasets somewhat recently which has spurred the simulated intelligence local area and scientists to do best in class exploration and work on artificial intelligence empowered items. In spite of the overflow of datasets, it is consistently a test to tackle another issue explanation. Coming up next are the noticeable difficulties of datasets that limit information researchers from building better computer based intelligence applications.

  • Insufficient Data – Non-availability of large samples of data points required by Machine Learning algorithms.
  • Bias and Human Error – Most tools used for data collection lead to either human error or bias towards one aspect.
  • Quality – The real-world datasets are unorganized and complex. They are of low quality almost by default.
  • Privacy and Compliance – Most sources do not share their data due to some privacy and compliance regulations. For example medical, national security, etc.
  • Data Annotations Process – Generally human interventions are used to manually label datasets for quality, which results in an error. It is time-consuming and expensive.

How to Build Datasets for Your Machine Learning Projects?

An Artificial Intelligence application flow is depicted in the diagram below. The first two components are the dataset acquisitions & data annotation section which are crucial to understanding for building a good Machine Learning application.

These days, we have adequate assets where we can get datasets on the web either open-source or paid. As you probably are aware information assortment and planning is the essence of any machine learning project, and a large portion of our valuable time is spent on this stage.

To take care of the issue articulations utilizing AI, we have two options. Possibly we utilize the current datasets or make another one. For an exceptionally unambiguous issue explanation, you need to make a dataset for a space, clean it, envision it, and comprehend the importance to obtain the outcome. In any case, assuming the issue articulation is normal, you can utilize the accompanying dataset stages for examination and accumulate information that best suits your necessities.

Best Dataset Search Engine Platforms for a Machine Learning Challenge

Below is the list of a few dataset platforms, that allow us to search and download data for Machine Learning projects and experiments. Most of the datasets are already cleaned and segregated for ML and AI project pipeline. However, we have to filter and utilize them according to our specifications.

  • Google Dataset Search Engine
  • Kaggle Datasets
  • ZDataset Free -Dataset
  • UCI Machine Learning Repository
  • ICPSR Datasets
  • Data World
  • gesisDataSearch
  • UK Dataservice

Custom Dataset can be created by collecting multiple datasets. For example, if we want to build an app to detect kitchen equipment, we need to collect and label images of relevant kitchen equipment. For labeling the images, we can run a campaign to collect data by encouraging users to submit or label images on a platform. They can be paid or rewarded for the task. Here are a few options that can be used to get data quickly for your requirements.

  • Generate real-world datasets by creating a mobile app to capture images or use an existing app.
  • Create a web app, and a single page, and plug it into your website. Ask users to annotate data for rewards. (open-source frameworks, for instance, audio collection for ASR applink /code.)
  • Build an in-house team to compile a dataset.
  • The Amazon Mechanical Turk is also a great option for crowdsourcing tasks for minimal charges.
  • Hire research community students or volunteers to take part in data collection.
  • Sign an agreement with data providers for the acquisitions of sensitive datasets like Medical health records (EHR datasets), X-rays or MRIs, etc. Generally, hospitals tie-up with research institutes for such projects.

A synthetic dataset is created using computer algorithms that mimic real-world datasets. This type of dataset has shown promising results in the experiments conducted to build Deep Learning models to create more generalized AI systems. Different techniques can be leveraged to generate a dataset.

What are Features in Machine Learning and Why it is Important?

Nowadays, researchers and developers utilize game technology to render realistic scenarios. Game framework unity is used to create datasets of particular interest and then used in the production of real-world data. Unity report shows that the synthesized dataset can be used to improve models’ performance. For instance,  computer vision models use synthetic images to iterate fast experiments and enhance accuracy.

Generative Adversarial Networks (GANs) are also used to create synthetic datasets. These are neural network-based model architectures used for generating realistic datasets. Most use case requires data privacy and confidentiality. Hence, these networks are utilized to generate a sensitive dataset that is hard to acquire or collect from public sources.

Data Augmentation is widely used by altering the existing dataset with minor changes to its pixels or orientations. It’s helpful when we are out of data to feed our Neural Network. However, we cannot apply the augmentation technique to every use case as it may alter the real result output. For instance, in the medical domain dataset, we cannot augment more data from the raw source as it’s case sensitive and may end up generating irrelevant data. This will hamper our model and cause more trouble. Some widely used augmentation techniques are :

  • Padding
  • Random rotating
  • Re-scaling
  • Vertical and horizontal flipping translation
  • Cropping
  • Zooming
  • Darkening & brightening/color, etc.


Data has come along a long way in the past few years, from countable numbers to now sitting on countless data points. Data is generated at a faster pace than ever. But, we can control the quality of data points, which will lead to the success of our AI models.

Datasets are, after all, the core part of any Machine Learning project. Understanding and choosing the right dataset is fundamental for the success of an AI project.

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