AI, Python, Cognitive Neuroscience
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If you’re 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 that’s 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. πŸ™‚

Also, who doesn’t love a simple and meaningful story.

#machinelearning #storytelling #communication

✴️ @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

✴️ @AI_Python_EN
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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

πŸ—£ @AI_Python_Arxiv
✴️ @AI_Python_EN
❇️ @AI_Python
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

βœ”οΈNumpy - https://bit.ly/1OLtuIF
βœ”οΈScipy - https://bit.ly/2f3pitB
βœ”οΈPandas - https://bit.ly/2qs1lAJ

#DataVisualization
βœ”οΈMatplotlib https://bit.ly/2gxxViI
βœ”οΈSeaborn https://bit.ly/2ABypQC
βœ”οΈPlotly https://bit.ly/2uJwULB
βœ”οΈBokeh https://bit.ly/2uOFbxQ

#ML #DL #ModelEvaluation
βœ”οΈScikit-Learn - https://bit.ly/2uYFNkw
βœ”οΈH20 - https://bit.ly/2M0hJnG
βœ”οΈXgboost - https://bit.ly/2M3Vdut
βœ”οΈTensorflow - https://bit.ly/2vfI5es
βœ”οΈCaffe- https://bit.ly/2a05bgt
βœ”οΈKeras - https://bit.ly/2vfDyZj
βœ”οΈPytorch - https://bit.ly/2uXWY5U
βœ”οΈTheano - https://bit.ly/2v3N805


#analytics #artificialintelligence #machinelearning
#recommend

✴️ @AI_Python_EN
Whether you’re 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, you’re a problem solver.

#datascience #machinelearning #analytics

✴️ @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.

βœ”οΈSTEP 1 - ARTICLES

- (short) KDnuggets article: https://lnkd.in/eRyTXcQ

- (long) O'Reilly article: https://lnkd.in/ehMHYsr

βœ”οΈSTEP 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

βœ”οΈSTEP 3 - COLLABORATE

- Join Explainable AI (XAI) Group: https://lnkd.in/dQjmhZQ

βœ”οΈSTEP 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

✴️ @AI_Python_EN