Datasets are the repository of information that is required to solve a particular type of problem. Also called data storage areas, they help users to understand the essential insights about the information they represent. Datasets play a crucial role and are at the heart of all Machine Learning models.
Machine Learning without data sets will not exist because ML depends on data sets to bring out relevant insights and solve real-world problems. Machine learning uses algorithms that comb through data sets and continuously improve the machine learning model.
Quality data is therefore important to ensure the efficacy of a machine learning model. Datasets are often related to a particular type of problem and machine learning models can be built to solve those problems by learning from the data. Datasets help users uncover insights before actually applying the machine learning model to it.
Many datasets are available online for learners who are starting off on building machine learning models. Alternatively, we also can make our own datasets.
Every problem statement we are dealing with comprises of data, which helps us better understand the problem and draw better insights from data by applying ML methods. In the real world, datasets are huge. So, you may have tons and tons of data that represents a particular problem. Datasets may also be confidential as they may contain sensitive information pertaining to a product, organization or government.
Data is not available in a specific format. Dataset files may be in the form of excel sheets containing rows and columns, bunch of images, videos and audios, in the form of Text like words, sentences and paragraphs, in the form of numbers or values, messages, chats, statuses and in the form of different files like word, txt, pdf, xml and so on. Data can be related to sales of a company, weather reports, income of a company, types of manufacturing products, salary paid to each employee, customers count for a particular item, monthly savings of an employee, frequent visits of a person to a particular place, statistics of any type of industry, quality performance check of a particular item, type of projects a company deals with, etc. Data is defined according to the problem it represents.
In Machine Learning, a dataset plays a key role in understanding the problem statement given by a user. A dataset is a repository of information, a collection of instances that help a user to better understand something. A dataset is used to draw better insights and get a clear picture of a particular problem statement. In Machine learning, a dataset is used as input for the machine learning model that has been developed to offer predictions based on the data. The more data we feed a machine learning model, the better it works and more accurate it gets. If you are a beginner, there are many data sets available that you can make use of to enhance your machine learning skills. Open-source repositories like Kaggle, UCI, Google etc. can help users to get started with Machine Learning.
To solve any problem in data science, be it in the field of Machine Learning, Deep Learning, or Artificial Intelligence, one needs a dataset that can be input into the model to derive insights. A technology has no significance without data. In the real world, data is not open source, as it is confidential and may contain very sensitive information related to an item, user or product. But raw data is available as open source for beginners and learners who wish to learn technologies associated with data. This raw data may or may not be the exact match of the real-time data. But it is a great resource for users/learners to get better connected with the data and draw insights from it by applying different types of algorithms on it. The commonly used sites from where learners can access datasets to practice their machine learning skills include:
Data Science, a field that encompasses machine learning, artificial intelligence, deep learning, data mining and more, has seen an unprecedented growth in the past decade. The sole reason for this growth has been the explosion of data that we have seen in the past few years. Tons and tons of data are being generated each day and organizations have realized the vast potential that this data holds in terms of fueling innovation and predicting market trends and customer preferences. Data science and its associated fields use algorithms, processes, and other modern tools and techniques to draw insights from vast amounts of structured and unstructured data. Data science has been consistently rated as being among the hottest job trends that is both lucrative and allows growth opportunities. If you are a learner or an experienced IT professional wanting to learn about data science, then there are several resources available online that help you get access to datasets and polish your machine learning skills. These include:
Beginners of machine learning are often advised to work on Regression and Classification Problems. To make a career in data science and to know more about Machine Learning models or algorithm functionality, it is important to have a grasp of the basics of Math concepts like Statistics, Probability, Linear Algebra, and Calculus. A background of Mathematics also helps users to implement algorithms on their own. It helps to better understand about the different types of implementation of complex strategies of the model and problems in the field of Data Science.
Natural Language Processing is a branch of artificial intelligence and among the fastest-growing fields in machine learning. NLP has found applications across fields like Text Classification, Speech Recognition, Language Modelling, Summarization, Image Captioning, Sentiment Analysis, Question Answering, and more. Some popular examples of NLP applications include Amazon “Alexa”, Google Assistant, and Apple’s “Siri”. The main use of NLP is smart search, summarization, classification etc., which majorly solves most of the users' problems. NLP requires a lot of data to function well. Given below are some datasets that can be used for NLP use cases. These are classified based on different types of domain areas and are as follows.
The above are the basic datasets to get started with the Natural Language Processing. Learners and beginners can explore these datasets and use them to build their NLP practice projects.
Computer vision (CV) is called the other “Human eye” and focuses on enabling computers to classify images the way humans do. Machines are trained with Computer vision and Image Processing techniques and used in interpreting real-world images and videos. CV helps in the visual interpretation of images and videos and is among the most widely used applications in the world of machine learning. Computer vision applications have applications right from classifying MNIST dataset of numbers to the real-world applications like Self Driving Cars. This technology is used in various industries like Medical, Automobile, robotics, etc. It can detect the objects at any given point of time and can be used in the application of CCTVs. Computer vision technology is used in mobile applications to detect a person’s images and label them further. The basic datasets required by a user to get started with Computer Vision and Image Processing are as follows.
The above datasets are a great resource to better understand about Computer Vision and Image Processing.
Deep Learning is a core part of Machine Learning, which deals with complex problems that deal with vast amounts of data. It has been developed to mimic the neural networks of the human brain. Deep learning uses neural networks consisting of many layers to solve problems like decision making and problem solving. Generally, machine learning has two layers. One is the Input layer-- to take input from the user and the output layer-- used to show the given problem statement's end results after processing it with a ML model. But in the case of Deep Learning there are 3 layers--called Input Layer, Hidden Layer and Output Layer. Deep learning finds applications in many industries and is used to tackle many difficult problems. The datasets for Deep Learning are as follows.
The datasets for Deep Learning include the datasets for Computer Vision, Natural Language Processing etc., because these are all the applications and core areas of Deep Learning.
We can say that the technology of Machine Learning is a boon for the Finance and Economics sector, as ML applications are widely used in these two areas. ML is used in these fields as a tool for predictions of sales forecasting, business growth, goods sold, manufacturing etc. ML is also expected to predict behavior of the consumer, which is turn will help develop economic models for the growth of the company. The basic datasets in this field are as follows.
The application of Machine Learning in the fields of Finance and Economics can be further used in stock market predictions, trading in an algorithmic way, for fraud detections etc.,
These datasets are used by the government in making economic decisions beneficial for the citizens of the nation. The Machine Learning models train the public data that can help the government policy makers to identify the trends, population growth or decline, migration and ageing. The datasets for the public Government are as follows.
Given above are the basic datasets to get started with applying Machine Learning models in context to Government data, to best analyze the trends and needs of the people of a nation.
It is a part of Natural Language Processing used to analyze text for polarity, from positive to negative. This process is used in detecting the emotions in the text of the users. We can detect the different behaviors of the author/user. We can tell how the writer's article or blog is either Humorous, Depressed, Insightful, etc. The following are the basic datasets for sentiment analysis.
Sentiment analysis is mostly used in the area of classification of tweets, chats, text etc., to know the users’ behavior at that particular context of time.
The application of Autonomous driving is a widely used application by many of the automobile industry at present, and most possibly in the future too. It is a sophisticated application, and it includes many of the technologies incorporated in it for better functioning of the system. It comprises of the latest technologies like Computer Vision, Natural Language Processing, Deep Learning, Machine Learning etc., in order to implement the complete functioning of the system. Autonomous driving application is used in self-driving cars at present, and it can be further extended to airplanes, ships etc., to provide a better experience to the user of moving from one place to the other without driving on their own. The following are the datasets of Autonomous Driving.
This technology is a boon for the Automotive industry to best deal with problems like rash driving, road accidents, harmful emissions, decreased lane capacity etc. and provide users with a better and more sophisticated way to travel.
The use of Machine Learning has extended its wings into Healthcare to solve the urgent needs and requirements of many people. ML has the capability to analyze huge patient related data sets and aid doctors in coming up with faster, better and low-cost approach to providing treatments. ML techniques in the medical field can help in identifying cancerous tumors, rare conditions, and abnormalities and help physicians make quick decisions by providing real time data on patients. The following are some of the Clinical Datasets that beginners can use to build their machine learning models.
ML can change the way healthcare is approached. It can lead to low-cost affordable care that everyone can access.
Recommender systems help us remember the history of previously browsed sites or necessary applications in the system in a particular site. This application has found use on e-commerce and streaming sites like Flipkart, Amazon, Netflix etc., to help users search for a particular item on the site or a movie in their play list. The recommender system is built based on the user’s preferences or choices based on a particular item. It helps the user by providing smart search to display ads on frequently visited sites. Google search Engine is the biggest Recommender system is very beneficial to the users and understands user behavior in the site search. The following are some of the datasets related to Recommender systems.
The above discussion is all about datasets, their significance in machine learning and the associated fields of machine learning including Deep Learning, Computer vision, and Natural Language Processing. ML is revolutionizing the way we live. It has found applications in all facets of our lives from healthcare to automobiles to banking and finance. And the crux of all Machine Learning innovations are datasets. The size and quality of the dataset affects the efficiency of the machine learning model. Machine learning models with the right datasets can provide solutions to a whole range of business challenges. Knowing how to work with and implementing datasets is a must for professionals who plan to work with machine learning and data science.
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