Coursera - AI for Medicine Specialization - Printable Version +- Softwarez.Info - Software's World! (https://softwarez.info) +-- Forum: Library Zone (https://softwarez.info/Forum-Library-Zone) +--- Forum: Video Tutorials (https://softwarez.info/Forum-Video-Tutorials) +--- Thread: Coursera - AI for Medicine Specialization (/Thread-Coursera-AI-for-Medicine-Specialization--667339) |
Coursera - AI for Medicine Specialization - OneDDL - 11-14-2024 Free Download Coursera - AI for Medicine Specialization Last updated 10/2024 MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English + subtitle | Duration: 137 Lessons ( 6h 18m ) | Size: 813 MB What you'll learn Diagnose diseases from x-rays and 3D MRI brain images Predict patient survival rates more accurately using tree-based models Estimate treatment effects on patients using data from randomized trials Automate the task of labeling medical datasets using natural language processing Skills you'll gain Image Segmentation Machine Learning natural language extraction time-to-event modeling model interpretation AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization . Applied Learning Project Medicine is one of the fastest-growing and important application areas, with unique challenges like handling missing data. You'll start by learning the nuances of working with 2D and 3D medical image data. You'll then apply tree-based models to improve patient survival estimates. You'll also use data from randomized trials to recommend treatments more suited to individual patients. Finally, you'll explore how natural language extraction can more efficiently label medical datasets. Homepage Screenshot Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live No Password - Links are Interchangeable |