The key to success in the field of machine learning or to become a great data scientist is to practice with different types of datasets. But discovering a suitable dataset for each kind of machine learning project is a difficult task. So, in this blog, we will provide the detail of the sources from where you can easily get the dataset according to your project.

Before knowing the sources of the machine learning dataset, let’s discuss datasets.

What is a dataset?

A dataset is a collection of data in which data is arranged in some order. A dataset can contain any data from a series of an array to a database table. Below table shows an example of the dataset:

Country	Age	Salary	Purchased
India	38	48000	No
France	43	45000	Yes
Germany	30	54000	No
France	48	65000	No
Germany	40		Yes
India	35	58000	Yes

A tabular dataset can be understood as a database table or matrix, where each column corresponds to a particular variable, and each row corresponds to the fields of the dataset. The most supported file type for a tabular dataset is “Comma Separated File,” or CSV. But to store a “tree-like data,” we can use the JSON file more efficiently.

Types of data in datasets

Numerical data:Such as house price, temperature, etc.
Categorical data:Such as Yes/No, True/False, Blue/green, etc.
Ordinal data:These data are similar to categorical data but can be measured on the basis of comparison.

Note: A real-world dataset is of huge size, which is difficult to manage and process at the initial level. Therefore, to practice machine learning algorithms, we can use any dummy dataset.

Need of Dataset

To work with machine learning projects, we need a huge amount of data, because, without the data, one cannot train ML/AI models. Collecting and preparing the dataset is one of the most crucial parts while creating an ML/AI project.

The technology applied behind any ML projects cannot work properly if the dataset is not well prepared and pre-processed.

During the development of the ML project, the developers completely rely on the datasets. In building ML applications, datasets are divided into two parts:

Training dataset:
Test Dataset

Note: The datasets are of large size, so to download these datasets, you must have fast internet on your computer.

Popular sources for Machine Learning datasets

Below is the list of datasets which are freely available for the public to work on it:

1. Kaggle Datasets

Kaggle is one of the best sources for providing datasets for Data Scientists and Machine Learners. It allows users to find, download, and publish datasets in an easy way. It also provides the opportunity to work with other machine learning engineers and solve difficult Data Science related tasks.

Kaggle provides a high-quality dataset in different formats that we can easily find and download.

The link for the Kaggle dataset is https://www.kaggle.com/datasets.

2. UCI Machine Learning Repository

UCI Machine learning repository is one of the great sources of machine learning datasets. This repository contains databases, domain theories, and data generators that are widely used by the machine learning community for the analysis of ML algorithms.

Since the year 1987, it has been widely used by students, professors, researchers as a primary source of machine learning dataset.

It classifies the datasets as per the problems and tasks of machine learning such as Regression, Classification, Clustering, etc. It also contains some of the popular datasets such as the Iris dataset, Car Evaluation dataset, Poker Hand dataset, etc.

The link for the UCI machine learning repository is https://archive.ics.uci.edu/ml/index.php.

3. Datasets via AWS

We can search, download, access, and share the datasets that are publicly available via AWS resources. These datasets can be accessed through AWS resources but provided and maintained by different government organizations, researches, businesses, or individuals.

Anyone can analyze and build various services using shared data via AWS resources. The shared dataset on cloud helps users to spend more time on data analysis rather than on acquisitions of data.

This source provides the various types of datasets with examples and ways to use the dataset. It also provides the search box using which we can search for the required dataset. Anyone can add any dataset or example to the Registry of Open Data on AWS.

The link for the resource is https://registry.opendata.aws/.

4. Google’s Dataset Search Engine

Google dataset search engine is a search engine launched by Google on September 5, 2018. This source helps researchers to get online datasets that are freely available for use.

The link for the Google dataset search engine is https://toolbox.google.com/datasetsearch.

5. Microsoft Datasets

The Microsoft has launched the “Microsoft Research Open data” repository with the collection of free datasets in various areas such as natural language processing, computer vision, and domain-specific sciences.

Using this resource, we can download the datasets to use on the current device, or we can also directly use it on the cloud infrastructure.

The link to download or use the dataset from this resource is https://msropendata.com/.

6. Awesome Public Dataset Collection

Awesome public dataset collection provides high-quality datasets that are arranged in a well-organized manner within a list according to topics such as Agriculture, Biology, Climate, Complex networks, etc. Most of the datasets are available free, but some may not, so it is better to check the license before downloading the dataset.

The link to download the dataset from Awesome public dataset collection is https://github.com/awesomedata/awesome-public-datasets.

7. Government Datasets

There are different sources to get government-related data. Various countries publish government data for public use collected by them from different departments.

The goal of providing these datasets is to increase transparency of government work among the people and to use the data in an innovative approach. Below are some links of government datasets:

Indian Government dataset
US Government Dataset
Northern Ireland Public Sector Datasets
European Union Open Data Portal

8. Computer Vision Datasets

Visual data provides multiple numbers of the great dataset that are specific to computer visions such as Image Classification, Video classification, Image Segmentation, etc. Therefore, if you want to build a project on deep learning or image processing, then you can refer to this source.

The link for downloading the dataset from this source is https://www.visualdata.io/.

9. Scikit-learn dataset

Scikit-learn is a great source for machine learning enthusiasts. This source provides both toy and real-world datasets. These datasets can be obtained from sklearn.datasets package and using general dataset API.

The toy dataset available on scikit-learn can be loaded using some predefined functions such as, load_boston([return_X_y]), load_iris([return_X_y]), etc, rather than importing any file from external sources. But these datasets are not suitable for real-world projects.

The link to download datasets from this source is https://scikit-learn.org/stable/datasets/index.html.

So, this brings us to the end of blog. This Tecklearn ‘How to get datasets for Machine Learning’ blog helps you with commonly asked questions if you are looking out for a job in Machine Learning. If you wish to learn Machine Learning and build a career in Data Science or Machine Learning domain, then check out our interactive, Machine Learning Training, that comes with 24*7 support to guide you throughout your learning period. Please find the link for course details:

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Introduction to Machine Learning

Need of Machine Learning
Types of Machine Learning – Supervised, Unsupervised and Reinforcement Learning
Applications of Machine Learning

Concept of Supervised Learning and Linear Regression

Concept of Supervised learning
Types of Supervised learning: Classification and Regression
Overview of Regression
Types of Regression: Simple Linear Regression and Multiple Linear Regression
Assumptions in Linear Regression and Mathematical Concepts behind Linear Regression
Hands On

Concept of Classification and Logistic Regression

Overview of the Concept of Classification
Comparison of Linear regression with Logistic regression
Mathematics behind Logistic Regression: Detailed Formulas and Functions
Concept of Confusion matrix and Accuracy Measurement
True positives rate, False positives rate
Threshold evaluation with ROCR
Hands on

Concept of Decision Trees and Random Forest

Overview of Tree Based Classification
Concept of Decision trees, Impurity function and Entropy
Concept of Impurity function and Information gain for the right split of node and
Concept of Gini index and right split of node using Gini Index
Overfitting and Pruning Techniques
Stages of Pruning: Pre-Pruning, Post Pruning and cost-complexity pruning
Introduction to ensemble techniques and Concept of Bagging
Concept of random forests
Evaluation of Correct number of trees in a random forest
Hands on

Naive Bayes and Support Vector Machine

Introduction to probabilistic classifiers
Understanding Naive Bayes Theorem and mathematics behind the Bayes theorem
Concept of Support vector machines (SVM)
Mathematics behind SVM and Kernel functions in SVM
Hands on

Concept of Unsupervised Learning

Overview of Unsupervised Learning
Types of Unsupervised Learning: Dimensionality Reduction and Clustering
Types of Clustering
Concept of K-Means Clustering
Mathematics behind K-Means Clustering
Concept of Dimensionality Reduction using Principal Component Analysis (PCA)
Hands on

Natural Language Processing and Text Mining Concepts

Overview of Concept of Natural Language Processing (NLP)
Concepts of Text mining with Importance and applications of text mining
Working of NLP with text mining
Reading and Writing to word files and OS modules
Text mining using Natural Language Toolkit (NLTK) environment: Cleaning of Text, Pre-Processing of Text and Text classification
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Introduction to Deep Learning

Overview of Deep Learning with neural networks
Biological neural network Versus Artificial neural network (ANN)
Concept of Perceptron learning algorithm
Deep Learning frameworks and Tensor Flow constants
Hands on

Time Series Analysis

Concept of Time series analysis, its techniques and applications
Time series components
Concepts of Moving average and smoothing techniques such as exponential smoothing
Univariate time series models
Multivariate time series analysis and the ARIMA model
Time series in Python
Sentiment analysis using Python (Twitter sentiment analysis Use Case) and Text analysis
Hands on

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