DareData

JULY 29, 2020

The Challenges of Medical Data In recent times, there have been several developments in applications of machine learning to the medical industry. Odds are that your local hospital, pharmacy or medical institution's definition of being data-driven is keeping files in labelled file cabinets, as opposed to one single drawer.

Data Pipeline 52

Data Pipeline Healthcare Medical Pipeline-centric 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. An example of document structure in healthcare insurance.

Machine Learning 75

Machine Learning Insurance Medical Healthcare 75

RandomTrees

DECEMBER 20, 2023

To replicate human cognition, AI uses a system named deep neural network. Modern medical professionals and institutions use Edge AI for surgical procedures. Moreover, it allows patients to monitor their activities and perform remote surgeries.

IT 52

IT Hospitality Healthcare Deep Learning 52

ProjectPro

FEBRUARY 25, 2022

Each stage of the data pipeline passes processed data to the next step, i.e., it gives the output of one phase as input data into the next phase. Data Preprocessing- This step entails collecting raw and inconsistent data selected by a team of experts.

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

Knowledge Hut

NOVEMBER 2, 2023

Image Recognition: Machine learning models can be specifically programmed to identify or categorize photos, thus opening doors to a wide range of tasks such as object detection, facial recognition, medical image analysis, and more. While many people have questions like “Is generative AI a type of deep learning?”,

Machine Learning 52

Machine Learning Deep Learning Utilities Medical 52

Edureka

JANUARY 18, 2024

This guide provides a comprehensive understanding of the essential skills and knowledge required to become a successful data scientist, covering data manipulation, programming, mathematics, big data, deep learning, and machine learning technologies. Stay updated on data science advancements.

Data Science 40

Data Science Deep Learning Machine Learning NoSQL 40

ProjectPro

JULY 26, 2021

Some of the largest conglomerates like Uber, Airbnb, NVIDIA, Intel, and, quite naturally, Google use TensorFlow, consequently making using it a skill that is increasingly finding its way into job requirements for most of the data related job roles be it - data scientists, deep learning engineers, machine learning engineers , or AI engineers.

Project 40

Project Deep Learning Medical Datasets 40

U-Next

SEPTEMBER 23, 2022

It offers data that makes it easier to comprehend how the company is doing on a global scale. Additionally, it is crucial to present the various stakeholders with the current raw data. Drill-down, data mining, and other techniques are used to find the underlying cause of occurrences. Diagnostic Analytics.

Data Mining 40

Data Mining Business Analyst Deep Learning Algorithm 40

U-Next

AUGUST 18, 2022

Data Science may combine arithmetic, business savvy, technologies, algorithm, and pattern recognition approaches. These factors all work together to help us uncover underlying patterns or observations in raw data that can be extremely useful when making important business choices. Theaters, channels, etc.,

Unstructured Data 40

Unstructured Data Data Science Medical Business Intelligence 40

ProjectPro

OCTOBER 18, 2021

To build such ML projects, you must know different approaches to cleaning raw data. You can leverage these data to create a system that can predict the patient's ailment and forecast the admission. KenSci is an AI-based solution that can analyze clinical data and predict sickness along with more intelligent resource allocation.

Machine Learning 52

Machine Learning Project Datasets Algorithm 52

AltexSoft

JUNE 26, 2023

Data collection revolves around gathering raw data from various sources, with the objective of using it for analysis and decision-making. It includes manual data entries, online surveys, extracting information from documents and databases, capturing signals from sensors, and more.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Edureka

FEBRUARY 12, 2025

Encoder Network Purpose : Encodes the input data xx into a latent representation zz by learning the parameters μencodermu_{text{encoder}} and σencodersigma_{text{encoder}} of the approximate posterior distribution q(z∣x)q(z|x). Architecture : Input: Raw data xx (e.g., image pixels or text embeddings).

Architecture 40

Architecture Medical Pharmaceutical Deep Learning 40

ProjectPro

AUGUST 16, 2021

Machine Learning Projects on Classification 2. Machine Learning Projects on Prediction 3. Machine Learning Projects on Computer Vision 4. Machine Learning Projects on Natural Language Processing (NLP) 5. Deep Learning and Neural Network Projects 6.

Machine Learning 40

Machine Learning Coding Project Deep Learning 40

ProjectPro

OCTOBER 27, 2021

Use the Morning Star Dataset to implement this machine learning project in financial domain. Unlock the ProjectPro Learning Experience for FREE 6. Transaction Fraud Detection Project Fraud detection has been a significant problem in the banking, insurance, and medical sectors.

Finance 52

Finance Machine Learning Project Banking 52

ProjectPro

FEBRUARY 19, 2021

From petal recognition to text classification, it is used on top(as a classification layer) of some of the most sophisticated deep Learning architectures out there. It is easy to implement and versatile. It can be used with a variety of output classes and it also outputs the magnitude of association with a class.

Machine Learning 40

Machine Learning Algorithm Datasets Data Science 40

AltexSoft

OCTOBER 22, 2021

Well, the technology behind such “magic” is called deep learning. In this post, we’ll explain what deep learning is, how it works, how it’s different from traditional machine learning, and what areas it can be applied within. Get ready because you’re about to go deep into deep learning.

Deep Learning 52

Deep Learning Algorithm Machine Learning Raw Data 52

AltexSoft

DECEMBER 15, 2021

Below are several real-life examples, proving the practicality of automated machine learning across different industries. The University of Pittsburgh Medical Center, or UPMC for short, sprawls across 40 hospitals and provides services in various specialty areas, including living donor liver transplants (LDLT.)

Machine Learning 59

Machine Learning Deep Learning Algorithm Telecommunication 59

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Machine Learning 64

Edureka

APRIL 1, 2025

Data augmentation is critical for boosting the performance of machine learning models, particularly deep learning models. The quality, amount, and importance of training data are important for how well these models perform. One of the main problems with using machine learning in real life is not having enough data.

Datasets 40

Datasets Machine Learning Deep Learning Data 40