Machine learning data is an important ingredient in machine learning, reshaping the entire IT industry in the current era. Machine learning focuses on improving the experience without being supervised or assisted by human intelligence. It is trained using data to make predictions and understand patterns that are unforeseen.
Data plays a very important role in machine learning as it is the main resource machine learning models use to deliver the desired output. Here, let us learn more about the machine learning data and its importance.
Why Is Machine Learning Data Important?
To understand why data is important in machine learning, it is important to know the role that data plays in the entire process. Machine learning models use algorithms that can continuously improve themselves over time to time, better able to handle the unforeseen problems that might arise in the future.
The type of dataset used for machine learning needs to become really effective and deliver an effective and real output. Here, let us learn more about the data used in machine learning and how to extract meaningful and good-quality data for your machine learning models.
What Is a Dataset In Machine Learning?
A dataset is an advanced version of data, also known to be a collection of instances that have a common attribute. An instance here represents a single row of data.
It is important for the machines to understand how to train and what actions to take in the most frequent conditions. A dataset is important to provide to the machine learning algorithm. The more data is provided to the ML model, the faster it learns and improves. A dataset has two components.
- Features (X)
- Labels (Y)
There are different types of datasets used in machine learning. Check the three important parts in the datasets.
- Training Dataset: This dataset is used to train the model. Here, the model developed on various algorithms learns weights, patterns, and relationships from this data.
- Validation Dataset: This dataset is used to tune hyperparameters and avoid overfitting issues in the machine learning model.
- Test Dataset: This dataset is used to evaluate the performance of the machine learning model on unseen data.
Types of Data In a Dataset In Machine Learning
A machine learning data contains different types of data depending on the problem you want to solve. These data types determine the model structure, preprocessing steps, and how the models learns from data.
1. Structured Data
Structured data is considered to be one of the most organized forms of data, much easier to interpret and work with. It looks similar to what you’d find in Excel sheets or SQL tables, where everything is arranged in rows and columns. Each row represents a data record, and each column holds a specific feature such as age, salary, product price, or temperature.
Structured data is ideal for algorithms like linear regression, decision trees, or random forests because it is clean, numerical or categorical, and easy to convert into tensors or matrices. Since it follows a fixed schema, structured data is simple to store, query, and analyze using traditional tools as well as machine learning models.
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2. Unstructured Data
Unstructured data is a machine learning data type that does not have any predefined format or organized structure, which makes it more challenging for machines to interpret. This type of data typically requires specialized preprocessing techniques such as tokenization for text, spectrogram conversion for audio, or normalization and resizing for images.
Even after being unstructured or messy, these data contain a lot of information and can power advanced AI applications, such as language translation, image classification, speech recognition, and more. Some of the examples of unstructured data include images, videos, audio recordings, social media posts, emails, and free-form text.
3. Semi-Structured Data
The semi-structured data is a classification of data between structured and non-structured forms, a little different from the regular tabular formatting. It contains tags and markets that are used to define certain elements.
Some popular examples of semi-structured data include NoSQL database entries, XML Documents, JSON files, HTML pages, log files, and more.
What Type of Data Does Machine Learning Need?
Data can come in various formats, but machine learning models depend on various formats of data, especially machine learning data includes, numerical data, categorical data, time series data, and text data.
1. Numerical Data
Numerical data refers to information that can be measured and expressed in numbers. This type of data is used heavily in machine learning because models can directly perform mathematical operations on it.
Numerical data can easily be sorted, averaged, scaled, or normalized, making it perfect for algorithms that rely on patterns in magnitude or frequency. Numerical values fall into two categories:
- Discrete data: It consists of whole numbers, countable numbers. For example, the number of students in a class, and more.
- Continuous data: It stores values within a range. For example, temperature, weight, or interest rates
Numerical data is ideal for processes like regression, forecasting, clustering, and many other machine learning algorithms.
2. Categorical Data
Categorical data is used to represent labels or classifications rather than numeric values. These are data points that describe qualities or attributes, such as gender, country, job role, or product category. Pattern discovery, grouping similar items, classifying objects, and more can easily be performed using the categorical data.
Since these data cannot be added, averaged, or arranged like numerical data. These machine learning data is used for special techniques like one-hot encoding or label encoding to convert the categories into a machine-readable form.
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3. Time Series Data
Time series machine learning data consists of observations including readings, sales, stock prices, and more. This type of data generally consists of start and end time flags to make it suitable for pattern analysis and forecasting.
This machine learning data type has a clear sequence and depends on the passage of time. Each point has a timestamp, making trends, seasonality, and historical patterns extremely important.
4. Text Data
Text data includes data in Word format, including words, paragraphs, reviews, social media posts, articles, and more. Text is kept under an unstructured data type as it cannot be stored or arranged in rows and columns.
These text data are converted into numerical format using techniques like a bag of words, tokenization, word embeddings, and more. Various machine learning algorithms are powered using this type of data, such as sentiment analysis, spam detection, chatbots, document classification, large language models, and more.
Where to Get Machine Learning Data?
If you are a developer looking for an effective source to extract useful and quality machine learning data, then you can easily check these popular ML dataset resources.
1. AWS, Amazon Datasets
The Amazon Web Service (AWS) is one of the most in-demand platform cloud computing platforms in the world. This platform is used to store data coming from various sources and hence available for public through AWS resources.
This method of extracting data is one of he most popular ways of extracting machine learning data.
2. Google Dataset Research
This is a Google-based dataset search engine, often used as a machine learning data source since September 2018. You can easily use a simple keyword search to discover a range of repositories across the web. This platform can extract databases listed on various platforms in various categories.
Developers often prefer this platform to get quality machine learning data with source citation, description of the data, link to the data resource.
3. Microsoft Research Open Data
This machine learning data platform is managed and developed by Microsoft. This is a data repository that makes datasets available for people worldwide. The dataset contains Microsoft’s researchers, collaborators, developers, and other personnel, making it a rich source of data fetching platform.
It is a cloud-based platform and has been open to all since 2018, when the team announced its publishing.
4. Government Datasets
There are many platforms where you can easily get data from different official sources. These datasets can be used for advanced research, data visualizations, developing applications, and more.
The process of getting the extracted data from the government portals might become a lengthy process.
5. Kaggle
Kaggle is an online platform that provides datasets and is one of the biggest hubs for data scientists and machine learning experts. Users can easily collaborate on multiple projects, share code, web-based notebooks, and more.
This platform is owned by Google and offers a wide range of tools, datasets, and tutorials in various domains.
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Machine Learning Data FAQs
Q1. What is the importance of machine learning?
Ans: Machine learning is one of the most impactful technologies that can learn from data, identify patterns, and make decisions without human intervention. It can learn from data, making it highly valuable across industries.
Q2. What platforms are used in machine learning?
Ans: Many platforms like Kaggle, AWS, Google Dataset Research, Microsoft Research, and other platforms
Q3. What are the types of data?
Ans: Categorical data, numerical data, time series data, and text data are important classifications of data used for machine learning purposes.
Q4. What is time series data?
Ans: Time series data are often used to improve the performance of the machine learning model. Many machine learning models are trained for a specific time interval.
