Home AI Courses Fundamentals of ML in Healthcare

Fundamentals of ML in Healthcare

(0 reviews)

Gain an understanding of the fundamental methods for training machine learning models with data, investigate advanced neural network architectures, and comprehend the challenges posed by dynamic medical practice on clinical machine learning applications by learning to bridge biostatistics, machine learning, and computer programming.

Key AI Functions:AI Healthcare,Machine Learning,Biostatistics,Neural Network

Description

Reviews

Alternative Tools

Description for Fundamentals of ML in Healthcare

Establish critical connections between the disciplines of biostatistics, machine learning, and conventional computer programming.

Discover the latest in neural network architectures for a variety of applications, including object detection and segmentation, as well as text classification.

Discover critical methods for utilizing data to train, validate, and test machine learning models.

Develop an understanding of the impact of discontinuous timelines and dynamic medical practice on the development and deployment of clinical machine learning applications.

Level: Beginner

Certification Degree:Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by Stanford University

Duration: 14 hours (approximately)

Schedule: Flexible

Reviews for Fundamentals of ML in Healthcare

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Learning TensorFlow: the Hello World of ML

IBM AI Product Manager

Gen AI Guiding Product Managers

Gen AI Assisting Data Analysts

AI Engineering

Alternative Tools for Fundamentals of ML in Healthcare

Akkio

Paid

The AI tool empowers non-programmers to construct and deploy AI, featuring data transformation, insights generation, identification of critical drivers, and prediction and forecasting functionalities to enhance business decision-making and planning processes.

#research #marketing

Gen AI Assisting Data Scientists

Utilize generative AI to advance in the field of data science. Develop hands-on generative AI skills that are in high demand to accelerate your data science career in under one month.

#Artificial Intelligence (AI) #Data Science

Gen AI Supporting Data Engineers

Begin your vocation in generative AI data engineering. Obtain the necessary skills to secure a position as a data engineer by acquiring an understanding of generative AI. No prior experience is required.

#Convolutional Neural Network #Information Engineering

GenAI An Introductory Course

Learn to leverage Generative AI for automation, software development, and optimizing outputs with Prompt Engineering.

#Artificial Intelligence (AI) #Python Programming

ML on Google Cloud

Learn machine learning with Google Cloud. End-to-end machine learning experimentation in the real world

#Tensorflow #Machine Learning

Gen AI in Marketing Specialization

Utilize Generative AI to optimize marketing creativity. Explore the potential of Generative AI to revolutionize and influence your marketing organization.

#Generative AI #Large Language Models

The Rise of Gen AI

This course covers the development, impact, and future of Generative AI through lectures, critical AI technologies, and interactive assessments.

#Creative Leap #GPT-4

Gen AI - Art of the Possible An Intro

Learn the significance, use cases, history, and pros and cons of generative AI in a business context, with a focus on its relationship to machine learning and services at Amazon.

#Generative AI #Amazon Web Services

Retrieval Augmented Generation (RAG) - Intro

Acquire practical skills to build a generative AI application by constructing a retrieval augmented generation (RAG) system using data, Qdrant, and LLMs.

#Python Programming #Machine Learning

Vertex AI - An Introduction

The "Introduction to Vertex AI" course provides a four-hour, practical, and fundamental overview of Vertex AI, ideal for professionals and enthusiasts aiming to leverage AI effectively.

#Critical Thinking #MLOps (Machine Learning Operations)