Machine Learning and Its Uses

Machine Learning has evolved with time, and the way this advanced technology works is quite different now as compared to when ML was first introduced. Machine Learning principles revolve around ‘iterative learning’ and data engineering mechanisms to help machines adapt to any new data acquired or interacted with. The machine remembers decisions of the past and repeats decisions that were made under similar circumstances.

How do Machine Learning Algorithms Work?
Machine Learning models are built by combining the right algorithm with the right tool or process to generate the desired results or take the desired decisions.

The key Machine Learning algorithms working their way up the ladder include random forests, decision trees, neural networks, multivariate adaptive regression, vector machine support, and more!

The right tools and processes may include maintaining overall data quality, comprehensive data management, data labeling, exploring data to visualize possible results, deploying models seamlessly to generate reliable and repetitive actions and/or results, and automating an accurate data-to-decision process.

Machine Learning Today

People around the world are relying on Machine Learning to conquer complex mathematical calculations accurately to manage big data and more. Giants like Netflix and Amazon are using Machine Learning to analyze our taste in movies and display content suggestions that we’re likely to be interested in. Twitter uses Machine Learning to enable and implement linguistic rules. Even multiple industries are using Machine Learning to detect fraud and misdemeanor.

AI and Machine Learning have been an impeccably popular topic of discussion over the past decade, and both of these revolutionary technologies have changed the way we live life. Today, we will talk about the latter and dive into the gamut of uses of Machine Learning.

Image Annotation
Image annotation refers to the process of labeling digital images with the help of both human input and computer-assistance. To give information about the objects within a given image to the computer vision model, a Machine Learning engineer predetermines what labels are to be used. Image labeling helps engineers use Machine Learning to record important data points in the image data to understand the level of accuracy achieved by the system.

For example, if an engineer places relevant bounding boxes around specific objects using any image annotation tool to apply labels, we can conclude that the engineer is annotating the image.

The number of image annotations per image can vary from project to project. While some projects may have a single label representing the entire content in any given image, others may have multiple objects tagged within a single image, considering each image has a different label.

In simpler terms, image annotation is the process used to prepare images that will be used to create Machine Learning algorithms that can perform specific computer vision tasks. Machine Learning engineers use annotated images to train computers and enable them to annotate images automatically.
The different types of Image Annotation would include bounding box, polygon masks, 3D cuboid, semantic segmentation, polyline, and keypoint/landmark annotations.

AIW helps you leverage expert image annotation services to help you train your Machine Learning models with more accuracy and more humanized decision-making capabilities.

Image Annotation Use Cases:

• Training datasets for AI cameras to ensure round-the-clock surveillance

• Training datasets for self-driving cars to enable autonomous driving

• Image annotation to enable crop health surveillance & livestock management

• Application of semantic segmentation in medical imaging devices for anatomy labeling

Video Annotation

Video Annotation refers to the process of labeling video clips that are used to train computer vision models to identify moving objects or detect anomalies. The objects in video annotation are annotated frame-by-frame so that machine learning models can recognize them. The result is used to train Machine Learning and Artificial Intelligence models. Since the focus object is in motion for video annotation, the process is more complex.

The different types of video annotation comprises landmark annotation, semantic annotation, 3D cuboid annotation, polygon annotation, polyline annotation, and 2D bounding box.

AIW is helping derive high quality datasets by implementing time-tested video annotation strategies in specific techniques tailored to fit your requirements.

The process of image annotation begins with gathering project requirements, designing the annotation workflow, production, quality checks, delivery and feedback.

Video Annotation Use Cases:

• Tracking operating boundaries with Polyline Video Annotation

• Training datasets to build facial recognition models using Keypoint Video Annotation

• Semantic Video Annotation for frame-by-frame processing of CCTV footages

Text Annotation

Text annotation is a process wherein engineers assign specific labels to a given document or digital file to define the content. Different criteria are used to highlight different types of sentence formations. The tools involved in text annotation ideally use a sequence-to-sequence neural network approach to complete intended tasks like translating a block of text from any specific source language to a predetermined target language.

Text annotation has a myriad of applications such as Natural Language Processing solutions, auto questions and answers, smart chatbots, neural machine translation, sentiment analysis, and more.

The main types of text annotation include entity annotation, entity linking, text classification, relationship annotation, sentiment annotation, and linguistic annotation.

AIW comes with an extensive portfolio in helping improve efficiency and productivity across multiple use cases of text annotation.

Text Annotation Use Cases

• Text Annotation for search engines to generate accurate and relevant search results

• Text Annotation for language translation software

• Text Annotation for code detection

Audio Transcription

Audio Transcription is another major application of Machine Learning and AIW is well versed with enabling accurate audio transcription in different types of media, across multiple industries, even for exceptionally large or complex datasets.

Some of the major use cases of Audio Transcription enabled by Machine Learning include edited transcription, verbatim transcription, phonetic transcription, intelligent verbatim transcription, and more.

ML is transforming transcription services across the globe, helping generate more authentic outputs mainly because manual transcription is under a high risk of errors. To ensure absolute accuracy in transcription, the datasets need intensive training. Machine Learning has helped eliminate the limitations of and expand the possibilities of transcription.

Applications of Machine Learning

Businesses and organizations have come to realize the potential of Machine Learning over time. Hence, multiple industries are now leveraging machine learning technology to obtain actionable insights and stay ahead of the curve in the market.

Finance

Machine Learning helps financial organizations to gain key insights from bulks of financial data and prevent financial frauds. This technology also helps make accurate trade and investment decisions by studying past patterns and outcomes. ML has made some significant contributions in cyber surveillance by helping authorities track institutions and individuals that are under any sort of financial risk and prevent fraud.

Marketing & Sales

Businesses worldwide are implementing machine learning algorithms to study purchase histories and display personalized content (products, services, and ads) recommendations to customers. Achieving the goal to provide a personalized shopping experience to every customer has expanded the horizon of opportunities for sales and marketing.

Government Bodies

Public safety bodies and other relevant government bodies have multiple data sources to analyze to identify meaningful patterns and gather actionable insights. Some organizations are using sensor data to achieve maximum efficiency at minimum costs. Machine Learning is also helping uncover identity thefts and fraud.

Transport

Machine Learning helps businesses in the travel industry study individual travel histories and identify patterns to make predictions. Hotels are using ML to track guest preferences and past guest history to deliver a personalized and memorable guest stay. Transportation service providers are using ML to predict possible roadblocks on specific routes and choose more hassle-free routes.

When we talk about transportation, it is inevitable to mention cab booking applications that use Machine Learning to detect location, estimate duration, find the best route, and also display your most frequently visited places on the top by studying rise history and patterns.

Healthcare

Wearable sensors and devices have completely changed the game for the healthcare industry. These sensors are recording data to track the health of patients in real time. Wearable sensors are helping doctors and healthcare practitioners monitor vital data including heart rate, blood pressure, and other vital parameters that help derive conclusions about a patient’s overall health condition. Doctors can even use patient history data to predict recurrence or occurrence of any potential ailments in the future. Diagnosis and treatment has become more accurate, reliable, and efficient, owing to this revolutionary technology.