Google Doubles Down On Spammers With #TensorFlow. #BigData #Analytics #MachineLearning #DataScience #AI #NLProc #IoT #IIoT #PyTorch #Python #RStats #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #TransferLearning
π http://bit.ly/2U4ZSAf
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
π http://bit.ly/2U4ZSAf
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
π£ @AI_Python_arXiv
If youβre learning #datascience or #analytics, then really take the time to understand the art of making the complex simple.
No matter where you go, the data that you use for your:
- visualizations
- machine learning
- statistical analysis
- presentations
All boils down to how well you can communicate the results.
So build the habit on documentation, storytelling, and simplifying your thoughts on papers.
Spend that extra time to articulate your thoughts and think deeply on how you want to present your data.
Because thatβs a skill that will always be needed in any place you go.
And not only will you thank yourself for doing this in the future, but your team will love you for making it so simple for them. π
Also, who doesnβt love a simple and meaningful story.
#machinelearning #storytelling #communication
β΄οΈ @AI_Python_EN
No matter where you go, the data that you use for your:
- visualizations
- machine learning
- statistical analysis
- presentations
All boils down to how well you can communicate the results.
So build the habit on documentation, storytelling, and simplifying your thoughts on papers.
Spend that extra time to articulate your thoughts and think deeply on how you want to present your data.
Because thatβs a skill that will always be needed in any place you go.
And not only will you thank yourself for doing this in the future, but your team will love you for making it so simple for them. π
Also, who doesnβt love a simple and meaningful story.
#machinelearning #storytelling #communication
β΄οΈ @AI_Python_EN
Rust: Programming Language Cheat Sheet.
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #Rust #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #Linux #Programming
http://bit.ly/2HEVJzl
β΄οΈ @AI_Python_EN
#BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #Rust #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #Linux #Programming
http://bit.ly/2HEVJzl
β΄οΈ @AI_Python_EN
This guide gives a complete understanding about various #machinelearning algorithms along with R & Python #codes to run them. These #algorithms can be applied to any data problem:
Linear Regression,
Logistic Regression,
Decision Tree,
SVM,
Naive Bayes,
kNN,
K-Means,
#Random Forest.
If you are keen to master machine learning, start right away.
Link : bit.ly/2CpWIjH
#machinelearning #deeplearning #python #coding #linkedin #decisiontrees #logisticregression #linearregression #forest #analytics #randomization #computervision
β΄οΈ @AI_Python_EN
Linear Regression,
Logistic Regression,
Decision Tree,
SVM,
Naive Bayes,
kNN,
K-Means,
#Random Forest.
If you are keen to master machine learning, start right away.
Link : bit.ly/2CpWIjH
#machinelearning #deeplearning #python #coding #linkedin #decisiontrees #logisticregression #linearregression #forest #analytics #randomization #computervision
β΄οΈ @AI_Python_EN
image_2019-03-20_04-26-00.png
843.4 KB
A Brief History of Data Science (Pre-2010, i.e. prior to rise of deep learning & popular usage of the term "data science")
#
Note: Modified original version of infographic to add 3 seminal developments in the history of Artificial Intelligence:
- 1943: Artificial neuron model (McCulloch & Pitts)
- 1950: Turing Test (Alan Turing)
- 1956: Dartmouth Conference (McCarthy, Minsky, Shannon)
#datascience #statistics #analytics #machinelearning #bigdata #artificialintelligence #innovation #technology #history #ai #datamining #informatics #infographics #informationtechnology #computerscience #dataanalysis #deeplearning #neuroscience #mathematics #science
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
#
Note: Modified original version of infographic to add 3 seminal developments in the history of Artificial Intelligence:
- 1943: Artificial neuron model (McCulloch & Pitts)
- 1950: Turing Test (Alan Turing)
- 1956: Dartmouth Conference (McCarthy, Minsky, Shannon)
#datascience #statistics #analytics #machinelearning #bigdata #artificialintelligence #innovation #technology #history #ai #datamining #informatics #infographics #informationtechnology #computerscience #dataanalysis #deeplearning #neuroscience #mathematics #science
π£ @AI_Python_Arxiv
β΄οΈ @AI_Python_EN
βοΈ @AI_Python
"AI Needs Better Data, Not Just More Data"
> https://lnkd.in/gR5E7Re
#AI #ArtificialIntelligence #MI #MachineIntelligence
#ML #MachineLearning #DataScience #Analytics
#Data #BigData #IoT #4IR #DataPedigree
#Veracity #Trust #DataQuality #BetterData
β΄οΈ @AI_Python_EN
> https://lnkd.in/gR5E7Re
#AI #ArtificialIntelligence #MI #MachineIntelligence
#ML #MachineLearning #DataScience #Analytics
#Data #BigData #IoT #4IR #DataPedigree
#Veracity #Trust #DataQuality #BetterData
β΄οΈ @AI_Python_EN
How This Researcher Is Using #DeepLearning To Shut Down Trolls And Fake Reviews. #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #IoT #IIoT #PyTorch #Python #RStats #JavaScript #ReactJS #GoLang #Serverless #DataScientist #Linux
π https://bit.ly/2U2J5BX
β΄οΈ @AI_Python_EN
π https://bit.ly/2U2J5BX
β΄οΈ @AI_Python_EN
What are the best resources to learn major libraries for #DataScience in #Python. Here is my updated full list.
Will recommend to use Jupyter-Spyder environment to practice all these.
#DataLoading and #DataManipulation
βοΈNumpy - https://bit.ly/1OLtuIF
βοΈScipy - https://bit.ly/2f3pitB
βοΈPandas - https://bit.ly/2qs1lAJ
#DataVisualization
βοΈMatplotlib https://bit.ly/2gxxViI
βοΈSeaborn https://bit.ly/2ABypQC
βοΈPlotly https://bit.ly/2uJwULB
βοΈBokeh https://bit.ly/2uOFbxQ
#ML #DL #ModelEvaluation
βοΈScikit-Learn - https://bit.ly/2uYFNkw
βοΈH20 - https://bit.ly/2M0hJnG
βοΈXgboost - https://bit.ly/2M3Vdut
βοΈTensorflow - https://bit.ly/2vfI5es
βοΈCaffe- https://bit.ly/2a05bgt
βοΈKeras - https://bit.ly/2vfDyZj
βοΈPytorch - https://bit.ly/2uXWY5U
βοΈTheano - https://bit.ly/2v3N805
#analytics #artificialintelligence #machinelearning
#recommend
β΄οΈ @AI_Python_EN
Will recommend to use Jupyter-Spyder environment to practice all these.
#DataLoading and #DataManipulation
βοΈNumpy - https://bit.ly/1OLtuIF
βοΈScipy - https://bit.ly/2f3pitB
βοΈPandas - https://bit.ly/2qs1lAJ
#DataVisualization
βοΈMatplotlib https://bit.ly/2gxxViI
βοΈSeaborn https://bit.ly/2ABypQC
βοΈPlotly https://bit.ly/2uJwULB
βοΈBokeh https://bit.ly/2uOFbxQ
#ML #DL #ModelEvaluation
βοΈScikit-Learn - https://bit.ly/2uYFNkw
βοΈH20 - https://bit.ly/2M0hJnG
βοΈXgboost - https://bit.ly/2M3Vdut
βοΈTensorflow - https://bit.ly/2vfI5es
βοΈCaffe- https://bit.ly/2a05bgt
βοΈKeras - https://bit.ly/2vfDyZj
βοΈPytorch - https://bit.ly/2uXWY5U
βοΈTheano - https://bit.ly/2v3N805
#analytics #artificialintelligence #machinelearning
#recommend
β΄οΈ @AI_Python_EN
Whether youβre a:
- data scientist
- data analyst
- data engineer
- statistician
- BI Specialist
- business analyst
- software engineer
- research scientist
- machine learning engineer
At the end of the day, youβre a problem solver.
#datascience #machinelearning #analytics
β΄οΈ @AI_Python_EN
- data scientist
- data analyst
- data engineer
- statistician
- BI Specialist
- business analyst
- software engineer
- research scientist
- machine learning engineer
At the end of the day, youβre a problem solver.
#datascience #machinelearning #analytics
β΄οΈ @AI_Python_EN
Machine Learning (ML) & Artificial Intelligence (AI): From Black Box to White Box Models in 4 Steps - Resources for Explainable AI & ML Model Interpretability.
βοΈSTEP 1 - ARTICLES
- (short) KDnuggets article: https://lnkd.in/eRyTXcQ
- (long) O'Reilly article: https://lnkd.in/ehMHYsr
βοΈSTEP 2 - BOOKS
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (free e-book): https://lnkd.in/eUWfa5y
- An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI (free e-book): https://lnkd.in/dJm595N
βοΈSTEP 3 - COLLABORATE
- Join Explainable AI (XAI) Group: https://lnkd.in/dQjmhZQ
βοΈSTEP 4 - PRACTICE
- Hands-On Practice: Open-Source Tools & Tutorials for ML Interpretability (Python/R): https://lnkd.in/d5bXgV7
- Python Jupyter Notebooks: https://lnkd.in/dETegUH
#machinelearning #datascience #analytics #bigdata #statistics #artificialintelligence #ai #datamining #deeplearning #neuralnetworks #interpretability #science #research #technology #business #healthcare
β΄οΈ @AI_Python_EN
βοΈSTEP 1 - ARTICLES
- (short) KDnuggets article: https://lnkd.in/eRyTXcQ
- (long) O'Reilly article: https://lnkd.in/ehMHYsr
βοΈSTEP 2 - BOOKS
- Interpretable Machine Learning: A Guide for Making Black Box Models Explainable (free e-book): https://lnkd.in/eUWfa5y
- An Introduction to Machine Learning Interpretability: An Applied Perspective on Fairness, Accountability, Transparency, and Explainable AI (free e-book): https://lnkd.in/dJm595N
βοΈSTEP 3 - COLLABORATE
- Join Explainable AI (XAI) Group: https://lnkd.in/dQjmhZQ
βοΈSTEP 4 - PRACTICE
- Hands-On Practice: Open-Source Tools & Tutorials for ML Interpretability (Python/R): https://lnkd.in/d5bXgV7
- Python Jupyter Notebooks: https://lnkd.in/dETegUH
#machinelearning #datascience #analytics #bigdata #statistics #artificialintelligence #ai #datamining #deeplearning #neuralnetworks #interpretability #science #research #technology #business #healthcare
β΄οΈ @AI_Python_EN
Hereβs tips to build business portfolio
β Step 1. Understand Data Science Implementation
https://lnkd.in/fMHtxYP
β Step 2. Understand what business want
https://lnkd.in/f396Dqg
β Step 3. Know Machine Learning Key Terminology
https://lnkd.in/fCihY9W
β Step 4. Know Business Implementation of Data Science
https://lnkd.in/f5aUbBM
If you have problem to get datasets in implement those 4, you can try a list of 10 GREAT
#SelfStarting #DataScience Projects to work on:
βBEGINNERβ
1. Pokemon - Weedle's Cave π
Python - https://lnkd.in/gcKWWQ2
2. Titanic ML π’
Python - https://lnkd.in/gafie9m
3. Housing Prices Prediction π‘
Python - https://lnkd.in/gX2FSDk
βINTERMEDIATEβ
4. Instacart Market Basket Analysis π
Python - https://lnkd.in/gkNaXqH
5. Quora Question Pairs π₯
Project :https://lnkd.in/f3HQZsT
Tutorial (Python)- https://lnkd.in/fEzf-Xp
6. Human Resource Analytics π΄π»
Python - https://lnkd.in/gVUPfWm
βADVANCEDβ
7. Analyzing Soccer Player Faces β½οΈ
Python - https://lnkd.in/gUys_TS
8. Recruit Restaurant Visitor Forecasting π±
Python - https://lnkd.in/gjQvf74
9. TensorFlow Speech Recognition π£
Python - https://lnkd.in/g8SSPfW
βMASTERYβ
10. More complete guide from Analytics Vidhya
https://lnkd.in/g_QjzGe.
#ml #forecasting #analytics
β΄οΈ @AI_Python_EN
β Step 1. Understand Data Science Implementation
https://lnkd.in/fMHtxYP
β Step 2. Understand what business want
https://lnkd.in/f396Dqg
β Step 3. Know Machine Learning Key Terminology
https://lnkd.in/fCihY9W
β Step 4. Know Business Implementation of Data Science
https://lnkd.in/f5aUbBM
If you have problem to get datasets in implement those 4, you can try a list of 10 GREAT
#SelfStarting #DataScience Projects to work on:
βBEGINNERβ
1. Pokemon - Weedle's Cave π
Python - https://lnkd.in/gcKWWQ2
2. Titanic ML π’
Python - https://lnkd.in/gafie9m
3. Housing Prices Prediction π‘
Python - https://lnkd.in/gX2FSDk
βINTERMEDIATEβ
4. Instacart Market Basket Analysis π
Python - https://lnkd.in/gkNaXqH
5. Quora Question Pairs π₯
Project :https://lnkd.in/f3HQZsT
Tutorial (Python)- https://lnkd.in/fEzf-Xp
6. Human Resource Analytics π΄π»
Python - https://lnkd.in/gVUPfWm
βADVANCEDβ
7. Analyzing Soccer Player Faces β½οΈ
Python - https://lnkd.in/gUys_TS
8. Recruit Restaurant Visitor Forecasting π±
Python - https://lnkd.in/gjQvf74
9. TensorFlow Speech Recognition π£
Python - https://lnkd.in/g8SSPfW
βMASTERYβ
10. More complete guide from Analytics Vidhya
https://lnkd.in/g_QjzGe.
#ml #forecasting #analytics
β΄οΈ @AI_Python_EN
Fundamentals of Clinical Data Science (Open-Access Book) - for healthcare & IT professionals: https://lnkd.in/eacNnjz
#
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 EUβs 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
β΄οΈ @AI_Python_EN
#
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 EUβs 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
β΄οΈ @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
β΄οΈ @AI_Python_EN
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
β΄οΈ @AI_Python_EN
"Leading your organization to responsible AI"
http://bit.ly/2WoekED
#AI #Artificialintelligence #MI #MachineIntelligence
#ML #MachineLearning #DL #Analytics #BigData #IoT
β΄οΈ @AI_Python_EN
http://bit.ly/2WoekED
#AI #Artificialintelligence #MI #MachineIntelligence
#ML #MachineLearning #DL #Analytics #BigData #IoT
β΄οΈ @AI_Python_EN
Python for Data Analysis.pdf
1.1 MB
****Python for Data Analysis by Boston University****
#datascience #dataanalysis #dataanalytics #python #analytics
β΄οΈ @AI_Python_EN
#datascience #dataanalysis #dataanalytics #python #analytics
β΄οΈ @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
β΄οΈ @AI_Python_EN
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
β΄οΈ @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
β΄οΈ @AI_Python_EN
#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
β΄οΈ @AI_Python_EN
This is Your Brain on Code π§ π»π’ 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
β΄οΈ @AI_Python_EN
#datascience #machinelearning #ai #bigdata #analytics #statistics #artificialintelligence #datamining #computing #programmers #neuroscience
β΄οΈ @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
β΄οΈ @AI_Python_EN
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
β΄οΈ @AI_Python_EN