AI data collection, collect data, collect information, dataset, Dataset in Machine Learning

Machine Learning and examples for Best Datasets

Introduction to Machine Learning Datasets

The accompanying article gives a layout to machine learning Datasets. AI dataset is characterized as the assortment of information that is expected to prepare the model and make forecasts. These datasets are named organized and unstructured datasets, where the organized datasets are in plain organization in which the line of the dataset relates to record and segment compares to the highlights, and unstructured datasets relates to the pictures, text, discourse, sound, and so on which is gained through Information Obtaining, Information Fighting and Information Investigation, during the educational experience these datasets are separated as preparing, approval and test sets for the preparation and estimating the precision of the mode.

Following are the three main steps needed in data analysis:

Data Acquisition
Data Wrangling or Data Pre-Processing
Data Exploration

As an output of data analysis, we will be having a relevant dataset that can be used in the training of the model.

Types of Datasets

In Machine Learning while training a model we often encounter the problem of over-fitting and underfitting.

In order to overcome the situation, we need to divide our dataset into 3 different parts:

Training Dataset
Validation Dataset
Test Dataset

1. Training Dataset

This data set is used to train the model i.e. these datasets are used to update the weight of the model.

2. Validation Dataset

These types of a dataset are used to reduce overfitting. It is used to verify that the increase in the accuracy of the training dataset is actually increased if we test the model with the data that is not used in the training.
If the accuracy over the training dataset increase while the accuracy over the validation dataset decrease, then this results in the case of high variance i.e. overfitting.

3. Test Dataset

Most of the time when we try to make changes to the model based upon the output of the validation set then unintentionally we make the model peek into our validation set and as a result, our model might get overfit on the validation set as well.
To overcome this issue we have a test dataset that is only used to test the final output of the model in order to confirm the accuracy.

Dataset structure and properties are defined by the various characteristics, like the attributes or features. Dataset is generally created by manual observation or might sometimes be created with the help of the algorithm for some application testing. Data available in the dataset can be numerical, categorical, text, or time series. For example, in predicting the car price the values will be numerical. In the dataset, each row corresponds to an observation or a sample.

Types of Data

Let’s see the type of data available in the datasets from the perspective of machine learning.

1. Numerical Data

Any data points which are numbers are termed numerical data. Numerical data can be discrete or continuous. Continuous data has any value within a given range while discrete data is supposed to have a distinct value. For example, the number of doors of cars will be discrete i.e. either two, four, six, etc. and the price of the car will be continuous that is might be 1000$ or 1250.5$. The data type of numerical data is int64 or float64.

2. Categorical Data

Categorical data are used to represent the characteristics. For example car color, date of manufacture, etc. It can also be a numerical value provided the numerical value is indicating a class. For example, 1 can be used to denote a gas car and 0 for a diesel car. We can use categorical data to forms groups but cannot perform any mathematical operations on them. Its data type is an object.

3. Time Series Data

It is the collection of a sequence of numbers collected at a regular interval over a certain period of time. It is very important, like in the field of the stock market where we need the price of a stock after a constant interval of time. The type of data has a temporal field attached to it so that the timestamp of the data can be easily monitored.

4. Text Data

Text data is nothing but literals. The first step of handling test data is to convert them into numbers as or model is mathematical and needs data to inform of numbers. So to do so we might use functions as a bag of word formulation.

Various Sources of Dataset

It is quite often hard to find the dataset for the machine learning application.

1. Google Dataset Search Engine

Link: https://datasetsearch.research.google.com/

Google has its own search engine for the dataset. Their objective was to unify almost all the available dataset repositories and make them discoverable. One can easily search for the dataset based upon the application of their learning model.

2. Microsoft Dataset

Link: https://msropendata.com/

Microsoft has Microsoft Research Open Data. It is a data repository that makes the dataset created by the researchers at Microsoft available to the data scientists. Over here one can get a bunch of curated datasets.

3. Computer Vision Dataset

Link: https://visualdata.io/

This source provides a dataset of images. If you plan to work on image processing, deep learning or computer vision you can use this source. There are great visual datasets that are available to build computer vision models.

4. Kaggle Dataset

Link: https://www.kaggle.com/datasets

It contains numerous amounts of data with different shapes and sizes. Most of the available dataset has kernels associated with them, where many data scientist has provided their notebooks to analyze the dataset.

5. Amazon Dataset

Link: https://registry.opendata.aws/

It contains a dataset from the field of public transport, satellite images, etc. These datasets are available on the Amazon Web Service resource like Amazon S3. It becomes handy if you plan to use AWS for machine learning experimentation and development.

Conclusion – Machine Learning Datasets

In this article, we understood the machine learning database and the importance of data analysis. We have also seen the different types of datasets and data available from the perspective of machine learning. In the end, you have a various sources which can be used to avail the dataset for the experimentation and development of machine learning models.