Fundamentals of Clinical Data Science (Open-Access Book) - for healthcare & IT professionals: https://lnkd.in/eacNnjz
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For more interesting & helpful content on healthcare & data science, follow me and Brainformatika on LinkedIn.

Table of Contents

Part I. Data Collection

- Data Sources
- Data at Scale
- Standards in Healthcare Data
- Research Data Stewardship for Healthcare Professionals
- The EU’s General Data Protection Regulation (GDPR) in a Research Context

Part II. From Data to Model

- Preparing Data for Predictive Modelling
- Extracting Features from Time Series
- Prediction Modeling Methodology
- Diving Deeper into Models
- Reporting Standards & Critical Appraisal of Prediction Models

Part III. From Model to Application

- Clinical Decision Support Systems
- Mobile Apps
- Optimizing Care Processes with Operational Excellence & Process Mining
- Value-Based Health Care Supported by Data Science

#healthcare #datascience #digitalhealth #analytics #machinelearning #bigdata #populationhealth #ai #medicine #informatics #artificialintelligence #research #precisionmedicine #publichealth #science #health #innovation #technology #informationtechnology

✴️ @AI_Python_EN

**** USE CASES OF DATA SCIENCE IN HR ****

Data science is not only intended for those who want to become data scientists. Data Science is a science that can be applied to HR as well. Here are some reasons why a recruiter also needs to learn data science:

1. Strategic recruitment by learning data science
https://lnkd.in/f3S8zvA

2. Future recruitment processes are in AI
https://lnkd.in/fqvemej

3. Data Science selection process
https://lnkd.in/fiCRwPW

4. You can become a Recruitment Specialist in data science.
https://lnkd.in/fNGvtpG

5. You can design a dashboard that is ideal for the recruitment process.
https://lnkd.in/fghidHC

6. Insight on learning data science
https://lnkd.in/g5n3bRn

7. Predicting Employe Turnover
https://lnkd.in/fkMu3A6

8. AI for Candidate Selection
https://lnkd.in/f7Kf3Mf

9. Increasing Employee Happiness
https://lnkd.in/fTzNbsC

10. Predicting Performance
https://lnkd.in/fdCmR-B

#datascience #artificialinteligence #analytics

✴️ @AI_Python_EN

"Leading your organization to responsible AI"

http://bit.ly/2WoekED

#AI #Artificialintelligence #MI #MachineIntelligence
#ML #MachineLearning #DL #Analytics #BigData #IoT

✴️ @AI_Python_EN

****Python for Data Analysis by Boston University****

#datascience #dataanalysis #dataanalytics #python #analytics

✴️ @AI_Python_EN

omplete Deep Learning Drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

GitHub by Marimuthu K.: https://lnkd.in/eTUp4Hi

B. For Business Applications you can see at
Data Science Process https://lnkd.in/fMHtxYP
Data Visualization in Business https://lnkd.in/fYUCzgC
Understand How to answer Why https://lnkd.in/f396Dqg
Know Machine Learning Key Terminology https://lnkd.in/fCihY9W
Understand Machine Learning Implementation https://lnkd.in/f5aUbBM
Machine Learning Applications on Marketing https://lnkd.in/fUDGAQW
Machine Learning Applications on Retail https://lnkd.in/fihPTJf

#machinelearning #analytics #datascience #artificialintelligence

#artificialintelligence #deeplearning #machinelearning #reinforcementlearning

✴️ @AI_Python_EN

DeepAMD: Detect Early Age-Related Macular Degeneration.
#BigData #Analytics #DataScience #AI #MachineLearning #DeepLearning #IoT #IIoT #PyTorch #Python #CloudComputing #DataScientist #Linux
https://link.springer.com/chapter/10.1007%2F978-3-030-20873-8_40

✴️ @AI_Python_EN

This is Your Brain on Code 🧠💻🔢 computer programming is often associated with math, but researchers used functional MRI scans to show the role of the brain's language processing centers: https://lnkd.in/eN_-3RA

#datascience #machinelearning #ai #bigdata #analytics #statistics #artificialintelligence #datamining #computing #programmers #neuroscience

✴️ @AI_Python_EN

If you've read job descriptions in data lately you are probably confused. Are you a data scientist, machine learning engineer, or research scientist? Instead of title matching, try asking yourself these questions:

1. Can you use statistics to answer questions about a situation that is new to you? Meaning, is your comfort with stats solid enough that you can bring it to bear appropriately depending on scenario?

2. Can you explain why a particular model performs well in a scenario, rather than just noting it does well? Meaning, do you understand the inner workings of models to tune and make sense of why they do what they do?

3. If someone mentions time and space complexity to you, does it make sense? In a big data world, thinking carefully about load of a particular algorithm is extremely important. This matters particularly for MLE and science positions.

4. Can you build something new? Maybe there isn't a perfect algorithm for what you want. Maybe the package in R doesn't exist. Can you make it happen if you need to?

5. Do you know what it means to put something into production? Do you have examples of how you've succeeded or failed with this?

These questions are not all encompassing, but they point to some of the key skillsets you'll need.

#datascience #analytics #data

✴️ @AI_Python_EN

Getting System Information in Linux using Python Script.
#BigData #Analytics #DataScience #IoT #PyTorch #Python #RStats #TensorFlow #DataScientist #Linux

http://bit.ly/2X56cZa

✴️ @AI_Python_EN

Pytorch Implementation of Deep Flow.

#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #TensorFlow #CloudComputing #DataScientist #Linux

https://arxiv.org/pdf/1905.02884.pdf

✴️ @AI_Python_EN

Decision trees are extremely fast when it comes to classify unknown records. Watch this video to know how Decision Tree algorithm works, in an easy way - http://bit.ly/2Ggsb9l

#DataScience #MachineLearning #AI #ML #ReinforcementLearning #Analytics #CloudComputing #Python #DeepLearning #BigData #Hadoop

ANNOUNCING PYCARET 1.0.0 - An amazingly simple, fast and efficient way to do machine learning in Python. NEW OPEN SOURCE ML LIBRARY If you are a DATA SCIENTIST or want to become one, then this is for YOU....

PyCaret is a NEW open source machine learning library to train and deploy ML models in low-code environment.

It allows you to go from preparing data to deploying a model within SECONDS.

PyCaret is designed to reduce time and efforts spent in coding ML experiments. It automates the following:

- Preprocessing (Data Preparation, Feature Engineering and Feature Selection)
- Model Selection (over 60 ready-to-use algorithms)
- Model Evaluation (50+ analysis plots)
- Model Deployment
- ML Integration and Monitoring (Power BI, Tableau, Alteryx, KNIME and more)
- ..... and much more!

Watch this 1 minute video to see how PyCaret can help you in your next machine learning project.

The easiest way to install pycaret is using pip. Just type "pip install pycaret" into your notebook.

To learn more about PyCaret, please visit the official website https://www.pycaret.org

#datascience #datascientist #machinelearning #ml #ai #artificialintelligence #analytics #pycaret

❇️ @AI_Python_EN