Insightful Interpretation of Machine Learning Datasets

It is possible to simulate human intelligence in machines with artificial intelligence (AI) and machine learning (ML). These simulations allow them to complete a variety of tasks without much human assistance – Companies need precise training data if they are to develop AI and ML models that are more efficient and newer. It is possible to gain a better understanding of a given problem through the use of training datasets which can subsequently be enriched through data annotation and labelling for further use as artificial intelligence (AI) training data.  

What is Machine Learning?

The goal of machine learning is to imitate humans’ learning process through the use of data and algorithms. It gradually improves the accuracy of its predictions. Statistical methods allow algorithms to be trained to make classifications or predictions within data mining projects using machine learning — this provides key insights into the data. 

Ideally, data mining improves business and application decision-making, influencing key growth metrics through these insights. Increasing demand for data scientists will result from the continued growth and development of big data, which requires them to identify the most pertinent business questions and the data that will be required to answer the questions.

Types of Machine Learning

An algorithm learns to improve its accuracy by applying supervised, unsupervised, semi-supervised, and reinforcement learning approaches. These four basic approaches are classified according to how an algorithm learns. Data scientists choose which algorithm and machine learning type depending on the data they wish to analyze.

  1. Supervised Learning 

These types of machine learning algorithms require labeled training data and variables data scientists want the algorithm to evaluate for correlations. Here, the input and output of the algorithm are both specified by the data scientists.

  1. Unsupervised Learning

It involves algorithms that learn from unlabeled data, where an algorithm scans data sets to identify meaningful connections. All predictions or recommendations are predetermined by the data that the algorithms train on.

  1. Semi-supervised Learning

There are two approaches to machine learning in this approach, the model is fed mostly labeled training data by a data scientist, but it is free to explore the data on its own and develop its own insights about it.

  1. Reinforcement Learning

As part of reinforcement learning, data scientists teach a machine how to complete a multistep process governed by clearly defined rules. For the most part, an algorithm decides how to complete a task on its own, but data scientists program it to complete it and give it positive or negative cues as it works out how to accomplish it.

Real-world Machine Learning Use Cases

You might encounter machine learning every day in the following ways:

  1. Speech Recognition 

Alternatively called automatic speech recognition (ASR), computer speech recognition, or speech-to-text, this technology converts human speech into the written form using natural language processing (NLP). A number of mobile devices include speech recognition in their systems so that users can conduct voice searches—like Google Assistant in Android smartphones, Siri in Apple devices, and Amazon’s Alexa in media devices. 

  1. Customer Service

Human agents are being replaced by online chatbots as customer service grows. We are seeing the shift in customer engagement across websites and social media platforms as these companies provide answers to frequently asked questions (FAQs) around topics such as shipping or product delivery, or cross-selling product recommendations. Slack and Messenger, for example, as well as virtual agents and voice assistants, are some examples of messaging bots on e-commerce sites with virtual agents.

3. Computer Vision

Computers and systems can use this AI technology to glean meaningful information from images, videos, and other visual inputs; Using this technology, they can take action based on these inputs. It is distinguished from image recognition tasks by its ability to provide recommendations. The application of computer vision in the industry of photo tagging on social media, radiology imaging in healthcare, and self-driving cars is based on convolutional neural networks.

  1. Recommendation Engines

Online retailers can make useful add-on recommendations to customers during checkout using data on past consumption behavior. AI algorithms can help us discover data trends for developing more effective cross-selling strategies.

  1. Automated Stock Trading

Without human intervention, AI-driven high-frequency trading platforms execute thousands or millions of trades every day in order to optimize stock portfolios. 

What is Training Data?

Machine learning algorithms develop an understanding of datasets by processing data and finding connections. In order to make this connection and find patterns in processed data, an ML system must first learn. After the ‘learning,’ it can then make decisions based on the learned patterns. ML algorithms can solve problems from retro observations – Exposing machines to relevant data over time allows them to evolve and improve. The training data quality directly influences the ML model’s performance quality. 

Cogito is a leading data annotation company assisting AI and machine learning enterprises with high-quality training data. In its decade-long journey as a data procurer, the company has built credibility for the accuracy and timely delivery of training data to ensure the quick accomplishment of data-driven AI models. 

What is Test Data?

When an ML model is built using training data, you need to test it with ‘unseen’ data. This testing data is used to evaluate the future predictions or classifications the model makes. The validation set is another partition of the dataset that is tested iteratively before the test data is entered; this testing allows developers to identify and correct overfitting before the test data is entered. 

Both positive and negative tests are performed using test data to verify functions produce the expected results for given inputs and to determine whether the software is capable of handling unusual, exceptional, or unexpected inputs. As your test data management strategy can be optimized by outsourcing data annotation to an industry expert, you can ensure quality information reaches test cases more quickly.

Training Dataset vs. Test Dataset

An ML model can learn patterns by learning insights from training data, which is approximately 80% of the complete dataset to be fed into the model. Testing data represent the actual dataset since they evaluate the model’s performance, monitor its progress, and skew it for optimal results.

The training data is typically 20% of the entire dataset, while the testing data confirms the model’s functionality. In essence, the training data train the model, and the testing data confirms its effectiveness. 

Enriching Datasets Using Data Annotation & Labeling

Building and training an ML model will require large volumes of training data. Data annotation is the process of adding tags and labels to training data. In order to achieve this goal, ML models require properly annotated training data in order to process data and gain specific information. 

Data annotation helps machines identify specific patterns and trends in data by connecting all the dots. Enterprises must understand how different factors affect the decision-making process in order to achieve business success. Data annotation services hold the key to accelerating businesses into the future. 

Cogito can Help with Data Annotation Services

With Live Enterprise, organizations can make intuitive decisions automatically at scale, get actionable insights from real-time solutions, experience anytime/anywhere, and get deep visibility into data across functions to become more productive with AI and machine learning innovations. Cogito offers training data annotation services for machine learning and artificial intelligence. The agile system at Cogito combines human-empowered data annotation and automated annotation & labelling tools to process unstructured data. 

This Post Is Originally Published at click here


What is Data Annotation?

Making the data understandable to machines by labeling using the certain techniques like outlining or shading the text or objects images is basically known as data annotation. The data can be anything from simple text to images or videos and audios available in various formats.

The main motive of data annotation is highlighting the important words, texts or objects using the annotation techniques to make it recognizable to machines or computer vision used for machine learning or artificial intelligence model developments.

Types of Data Annotation

There are different types of data annotation services offered by companies providing machine learning and AI training data. Text Annotation, Image Annotation, Audio Annotation, Video Annotation are the leading types of annotation services you can find in the market. And under each types there are certain annotation techniques like image annotation bounding boxes, cuboid, semantic, polygons and point or polylines are the popular annotation methods.

Types of Data Annotation

Data Annotation for AI and Machine Learning

Apart from few human oriented needs, most of the annotation is done for machine learning and AI data training. Data annotation techniques helps the machines to recognize the actual dimension, shape, size and types of content available on the web based services. Each data has its own format and most of them are understandable to humans but to make such data comprehensible to machines a precise data annotation services is required.

How to Get Annotated Data for Machine Learning or AI?

Developing the machine learning or AI models not only required special skills or knowledge but having a high-quality training data is also important to make such models functional and give the accurate results. Getting the quality training data with right annotation and labeling is difficult, especially if you are looking for free data annotation service.

Cogito is one the well-known companies offers the high-quality machine learning training data with proper annotation and data labeling. It is expert in image annotation and data labeling service for various industries like healthcare, automobiles, retail, agriculture, ecommerce, banking finance and information technology fields with well-diversified clients portfolio.

What is Best Data Labeling Process to Create Training Data for AI?

Data annotation in AI world is one of the most crucial processes to make available the set of training data for machine learning algorithms. And computer vision based AI model needs annotated images to make the various objects recognizable for better understanding of surroundings.

Data annotation process involves from collection of data to labeling, quality check and validation that makes the raw data usable for machine learning training. For supervised machine learning projects, without labeled data, it is not possible to train the AI model.

During the whole process, well trained human power with right tools and techniques, data is annotated as per the requirements and then processed in a highly secured environment to clients. The data is encrypted to make sure it can be safely delver to the clients to avoid any risk. So, right here we will discuss about the data labeling process to step wise facts.


Collection of Datasets

The first step towards data annotation is understand the problem to provide the precise training data. Hence, collecting the datasets from client is an important aspect. So, the raw data is collected directly from the client in the well-organized format.

Also Read : How to Create Training Data for Machine Learning?

The data is collected through a proper channel to make sure its originality and security. Many business enterprises follow the different routes to send the data for labeling. Sometimes it is supplied in encrypted format and after data annotation it is again sent to client in the secured format.

Labeling of Dataset

After acquiring the data, organizing the labeling process is the next part of data labeling. Actually, for the supervised machine learning labeled data is required, and proper labeling is important to make sure AI model get trained precisely and work in the right manner.

Choosing the right tools and technique is another factor for data labeling. And in image annotation is done to create the training data sets for computer vision based AI model. The quality is also need to be ensured to make sure the model can predict with the accurate results. To consider all these points two points also need to discussed here – how to label data and who will label the data.

How to Label Data: After getting the data set for labeling, the annotation team has to decide the type of annotation applied here, like detecting, classifying and segmentation of the object. Here if client provides the specific tool or software, then annotators use to annotate the images using the same.

Once the data sets are assigned to annotators and instructed what type of annotation and what are the tools will be best suitable to annotate the data.

Who Will Label the Data: Similarly, the next step into data labeling process comes, who will annotate or label the data. Here, two options are available for the AI companies – first organize the in-house data labeling facility which could be easy control for you and might cost less but it can take extraordinary time due to collection and labeling of entire data sets.

The second option is outsource the labeling task to other data annotation companies, who have team of well-trained and experienced annotators to label the data for machine learning with better efficiency and quality. The best part of outsourcing is data has the ability to aggregate quickly. While on the other hand transparency, accuracy and high-cost are the concerning factors with outsourcing services.

Quality Check and Evaluation

After annotating the data, checking the quality is one of the most important factors of data labeling process. Here, qualified annotator manually check the quality of each annotated images to make sure machine learning algorithm get trained with right accuracy.

Also Read : How to Build Training Data for Computer Vision?

Here, the data sets are also evaluated to validate the same, and if there is any correction the data is annotated correctly and finally validated for machine learning training. Here highly experienced, annotators are required to prudently the check the quality of data labeled to make sure AI companies get the best and high-quality datasets at best pricing.

Final Delivery of Annotated Datasets

The last step in data annotation process is after labeling, the data need to be safety delivered to client. Here again the authenticity and privacy of data is ensured till the data is delivered to client. And the mode of delivering the data also depends on the company to company but there should be safe mode to send such data with complete confidentiality and safety.

Data Labeling Process at Cogito

Most of the companies follow the above discussed data labeling process but few companies have more complex or even more sophisticated but secured data annotation process. Cogito is one the companies providing the world-class data labeling solution with next level of accuracy. It is following the international standards for data security and privacy to ensure the originality of AI model.

What are the Current Challenges to Develop the Machine Learning Based AI Robotics?

Robotics is one of the most innovative development of Machine Learning (ML) and Artificial Intelligence (AI). Earlier it was performing the repetitive types of tasks where there no changes in the pattern. But now, thanks to machine learning, the AI robotics are becoming more inelegant with self-decision making capability to perform different types of tasks or action without human intervention.

Continue reading “What are the Current Challenges to Develop the Machine Learning Based AI Robotics?”

Best Data Labeling and Annotation Services for AI and Machine Learning

Cogito is the industry leader in data labeling service and annotation services to provide the training data sets for AI and machine learning model developments. All types of AI and ML services requires the training data for algorithms with next level of accuracy making AI possible into various fields like healthcare, retail and automotive and robotics etc.

Apart from AI and ML training data sets, Cogito is also render the various other services like Data Collection & Classification, Audio Video Transcription and Contact Center Services to wide range of industries with affordable pricing. It is basically involved in image annotation services at large scale with team of well-qualified and trained annotators for different types of projects from different fields to give quality results.

data labeling service provider

Services Offered by Cogito:

  • Visual Search
  • Image Annotation
  • Content Moderation
  • Sentiment Analysis
  • Data Collection
  • Data Classification
  • Search Relevance
  • Audio Transcription
  • Video Transcription
  • OCR Transcription
  • Machine Learning
  • Virtual Assistant
  • ChatBot Training
  • Healthcare Training Data
  • Contact Center Services

The services offered by Cogito is specially for the AI and ML companies in USA, Canada, UK and other countries in Europe and other continents. It is one of the best annotation service provider in the industry and annotating images under the world-class working environment to deliver each project timely while ensuring the customize requirements and budget of the customers.

How to Find Best Image Annotation Company in India?

Image annotation is becoming the need of AI industry to train the machines with large volume of visual data sets. It is a kind of object lebelling in images making recognizable for computer vision that helps machine learning algorithms to understand the similar objects. And there are various types of image annotation techniques applied while doing this job.

Continue reading “How to Find Best Image Annotation Company in India?”