Edureka

MARCH 26, 2025

Data imputation is the method of filling in missing or unavailable information in a dataset with other numbers. Impacts on the Final Model Missing data may lead to bias in the dataset, which could affect the final model’s analysis. What Is Data Imputation? This process is important for keeping data analysis accurate.

Medical 40

Medical Datasets Data Analysis Machine Learning 40

Edureka

JANUARY 21, 2025

Because they are trained on huge datasets and have billions of factors. RAG retrieves medical guidelines or research papers and generates patient-specific advice or summaries for healthcare providers. Healthcare RAG system needs extensive medical datasets and context-aware retrieval for accuracy.

Healthcare 52

Healthcare Education Medical Database 52

AltexSoft

OCTOBER 25, 2021

It can be manually transformed into structured data by hospital staff, but it’s never a priority in the medical setting. It creates barriers in already bloated administrative tasks and in case of emergency, can lead to medical complications. Medical transcription. Amazon Transcribe Medical. Available solutions.

Medical 52

Medical Healthcare Process Hospitality 52

Snowflake

SEPTEMBER 5, 2023

Step 4: Test-driving the deployment For our example, we will use the model to run text categorization for a news items dataset from Kaggle , which we first store in a Snowflake table.

Medical 134

Medical Python Government Datasets 134

U-Next

MARCH 1, 2023

Datasets are obtained, and forecasts are made using a regression approach. Applications Online Realtors Heart Disease Prognosis This project is helpful from a medical aspect because it is intended to offer online medical advice and direction to those with cardiac issues.

Project 96

Project Medical Banking Healthcare 96

Knowledge Hut

JULY 4, 2023

Pattern recognition is used in a wide variety of applications, including Image processing, Speech recognition, Biometrics, Medical diagnosis, and Fraud detection. And is used in a wide variety of applications, including image processing, speech recognition, and medical diagnosis.

Machine Learning 98

Machine Learning Medical Algorithm Deep Learning 98

Edureka

MARCH 12, 2025

Overview of DeepSeek AI’s Research Paper DeepSeek AI’s research paper goes into great depth about the architecture, dataset selection, model training, and performance benchmarks. Advanced Fine-Tuning and RLHF (Reinforcement Learning with Human Feedback) Fine-tuned using domain-specific datasets to improve real-world applications.

Datasets 40

Datasets Medical Architecture Healthcare 40

Edureka

MARCH 15, 2025

By learning the details of smaller datasets, they better balance task-specific performance and resource efficiency. It is seamlessly integrated across Meta’s platforms, increasing user access to AI insights, and leverages a larger dataset to enhance its capacity to handle complex tasks. What are Small language models?

Entertainment 74

Entertainment Retail Education Datasets 74

Edureka

MARCH 27, 2025

CycleGAN, unlike traditional GANs, does not require paired datasets, in which each image in one domain corresponds to an image in another. Problem With Image-to-Image Translation Traditional picture-to-image translation algorithms, such as Pix2Pix, need paired datasets, in which each input image corresponds to a target image.

Datasets 40

Datasets Medical Architecture Algorithm 40

AltexSoft

MAY 12, 2022

For further steps, you need to load your dataset to Python or switch to a platform specifically focusing on analysis and/or machine learning. You have three options to obtain data to train machine learning models: use free sound libraries or audio datasets, purchase it from data providers or collect it involving domain experts.

Machine Learning 122

Machine Learning Building Deep Learning Healthcare 122

Towards Data Science

AUGUST 29, 2023

Today, we will delve into the intricacies the problem of missing data , discover the different types of missing data we may find in the wild, and explore how we can identify and mark missing values in real-world datasets. Image by Author. Let’s consider an example. Image by Author. Image by Author. Image by Author.

Datasets 117

Datasets Machine Learning Data Data Science 117

Monte Carlo

JULY 30, 2024

Even if an incorrect diagnosis was made, you would still want that to remain on the record (along with the correct updated diagnoses) so a medical professional could understand the full history, and context of a patient’s journey.

Data Validation 96

Data Validation Datasets Medical Raw Data 96

Snowflake

OCTOBER 17, 2024

CHG Healthcare , a healthcare staffing company with over 45 years of industry expertise, uses AI/ML to power its workforce staffing solutions across 700,000 medical practitioners representing 130 medical specialties. CHG builds and productionizes its end-to-end ML models in Snowflake ML.

Food 111

Food Medical Healthcare AWS 111

Edureka

APRIL 16, 2025

Applied in everything from medical development to image creation, VAEs learn to compress and creatively replicate data. Designing the Model We’ll use TensorFlow and Keras to build a VAE for the MNIST dataset. Ever utilized a program that creates blurriness-enhanced images or lifelike faces?

Medical 40

Medical Certification Deep Learning Datasets 40

Knowledge Hut

JANUARY 12, 2024

The main agenda is to remove the redundant and dependent features by changing the dataset onto a lower-dimensional space. variables) in a particular dataset while retaining most of the data. They make predictions based upon the probability that a new input dataset belongs to each class.

Machine Learning 97

Machine Learning Datasets Medical Algorithm 97

Edureka

APRIL 28, 2025

Dataset-dependent : IS relies on the Inception model trained on ImageNet, which may not be suitable for non-natural images (e.g., medical scans). Higher scores mean better quality and diversity, but ideal values depend on the dataset. To tackle these, researchers often compare IS with a more robust metric, FID.

Medical 40

Medical Certification Datasets Coding 40

Edureka

FEBRUARY 13, 2025

FSL uses this idea to help with situations where it is hard, costly, or almost impossible to collect data, like: Finding rare diseases when there isn’t much medical image data available. Training the Similarity Function A big-named dataset like ImageNet is used to teach the model how to understand similarities in a supervised way.

Deep Learning 52

Deep Learning Datasets Data Machine Learning 52

ProjectPro

FEBRUARY 5, 2021

Nonetheless, it is an exciting and growing field and there can't be a better way to learn the basics of image classification than to classify images in the MNIST dataset. Table of Contents What is the MNIST dataset? Test the Trained Neural Network Visualizing the Test Results Ending Notes What is the MNIST dataset?

Datasets 40

Datasets Deep Learning Medical Algorithm 40

Edureka

FEBRUARY 12, 2025

Pre-training and Fine-tuning BERT’s exceptional language understanding is the result of a two-stage process: Pre-training: Two tasks are used to pre-train BERT on big-text datasets: Masked Language Modeling (MLM): With the help of context, the model learns to guess the meaning of unseen words in sentences. legal, medical).

IT 40

IT Banking Datasets Architecture 40

Knowledge Hut

JULY 28, 2023

Machine learning offers scalability and efficiency, processing large datasets quickly. A dataset's anomalies may provide valuable information about inconsistencies, mistakes, fraud, or unusual events. Global or Point Outliers: These anomalies are discrete data points that stand out from the rest of the dataset in a significant way.

Machine Learning 98

Machine Learning Algorithm Datasets Deep Learning 98

Knowledge Hut

MAY 3, 2024

Types of Machine Learning: Machine Learning can broadly be classified into three types: Supervised Learning: If the available dataset has predefined features and labels, on which the machine learning models are trained, then the type of learning is known as Supervised Machine Learning. A sample of the dataset is shown below.

Machine Learning 98

Machine Learning Algorithm Datasets Medical 98

Knowledge Hut

JANUARY 18, 2024

They can work with various tools to analyze large datasets, including social media posts, medical records, transactional data, and more. R has become increasingly popular among data scientists because of its ease of use and flexibility in handling complex analyses on large datasets.

Programming Language 98

Programming Language Data Science Programming Java 98

Edureka

JUNE 7, 2024

These models are trained on vast datasets which allow them to identify intricate patterns and relationships that human eyes might overlook. In healthcare, generative AI can assist in medical image analysis and report writing, while predictive models forecast patient outcomes. And that’s the tip of the iceberg of possibilities.

Deep Learning 114

Deep Learning Media Algorithm Manufacturing 114

RandomTrees

JANUARY 22, 2024

It improves accessibility, encourages innovation for greater value, lowers disparities in research and treatment, and harnesses large-scale medical data analysis to create new data. Treatment strategies are customized using machine learning and predictive analytics according to a patient’s medical history.

Medical 52

Medical Healthcare Electronics Datasets 52

RandomTrees

APRIL 30, 2024

It has completely changed our approach to medical diagnosis, treatment, and remote patient care. From medical image analysis to drug discovery and personalized treatment, Generative AI is revolutionizing global health initiatives and telemedicine. It is critical to prevent the progression of disease and improve treatment outcomes.

Medical 52

Medical Healthcare Algorithm Consulting 52

AltexSoft

NOVEMBER 17, 2021

Digitizing medical reports and other records is one of the critical tasks for medical institutions to optimize their document flow. But some healthcare organizations like FDA implement various document classification techniques to process tons of medical archives daily. Stating categories and collecting training dataset.

Machine Learning 75

Machine Learning Insurance Medical Healthcare 75

RandomTrees

JUNE 24, 2024

Fig: 1: Image Annotation Challenges of manual Annotation Complications in manually annotating visual data: It is Time-consuming and labor-intensive, especially for large datasets. Scalability limitations which make it impractical for large datasets. Initially, we used a custom dataset focused on potholes.

Medical 52

Medical Machine Learning Retail Datasets 52

Knowledge Hut

MAY 31, 2023

It involves analyzing vast amounts of health-related data, including health records, medical images, and genetic information, using machine learning algorithms, natural language processing, computer vision, and other AI technologies to enhance the health of patients, lower costs, and boost the effectiveness of the delivery of healthcare.

Project 96

Project Healthcare Deep Learning Transportation 96

Knowledge Hut

OCTOBER 27, 2023

Data analytics projects involve using statistical and computational techniques to analyse large datasets with the aim of uncovering patterns, trends, and insights. These datasets can be used to explore a wide range of research topics, including healthcare, finance, marketing , and social media. Let’s delve deep to understand it.

Data Analytics 52

Data Analytics Coding Project Medical 52

Cloudera

APRIL 6, 2022

“Maybe you could have multiple destinations on Earth with the same dataset, doing different things.” In healthcare , for example, doctors are starting to leverage ML for real-time analysis of data to improve medical care. Moreover, interpreting AI results from the data is not overly difficult.

Big Data 103

Big Data Machine Learning Medical Insurance 103

RandomTrees

JANUARY 29, 2024

Machine learning models rely heavily on large and diverse datasets to train and improve their ability to understand and interpret visual information. From recognizing faces and detecting objects to navigating autonomous vehicles and dissecting medical images, its applications span a wide spectrum.

Medical 52

Medical Retail Entertainment Datasets 52

Precisely

MARCH 7, 2024

From documenting losses and damages to verifying that a claim submission meets all the necessary criteria, each step requires meticulous attention to detail and often entails reviewing lengthy narrative documents such as accident reports, medical records, and legal demands letters.

Insurance 72

Insurance Process Medical Data Governance 72

Knowledge Hut

JUNE 4, 2024

For example, if a patient has been using a certain medication for a long time and has not caused any side effects yet, then it might be safe for that patient to continue using this medication. Analysing medical images has been proven to identify the tiniest microscopic defects.

Pharmaceutical 52

Pharmaceutical Data Science Medical Machine Learning 52

Knowledge Hut

NOVEMBER 27, 2023

For instance, sales of a company, medical records of a patient, stock market records, tweets, Netflix’s list of programs, audio files on Spotify, log files of a self-driven car, your food bill from Zomato, and your screen time on Instagram. There is a much broader spectrum of things out there which can be classified as data.

Data Science 97

Data Science Healthcare Machine Learning Algorithm 97

ProjectPro

OCTOBER 14, 2021

The publicly available Kaggle dataset of the Tesla Stock Data from 2010 to 2020 can be used to implement this project. Maybe you could even consider gathering more data from the source of the Tesla Stock dataset. You could undertake this exercise using the publicly available Cervical Cancer Risk Classification Dataset.

Machine Learning 52

Machine Learning Project Insurance Medical 52

Knowledge Hut

MAY 3, 2024

Spark's primary data structure is Resilient Distributed Datasets (RDD). Each dataset in an RDD is split into logical divisions that may be calculated on several cluster nodes. Memory Management RDD is used by Spark to store data in a distributed fashion (i.e., cache, local space). It is a distributed collection of immutable things.

Kafka 98

Kafka Scala Java Amazon Web Services 98

Scott Logic

SEPTEMBER 13, 2024

They are trained on large datasets to recognise patterns and make predictions or decisions based on new information. During the model evaluation phase (validation mode), we will use a labelled dataset of emails to calculate metrics like accuracy, precision and recall. At their core, ML models learn from data.

Medical 52

Medical Hospitality Datasets Machine Learning 52