Which is the best tool amongst #Python, #R and #SAS for the job? If you are also looking for an answer, then this Infographic is what you should follow. https://lnkd.in/frqar5E

✴️ @AI_Python_EN

All ***Cheat Sheets*** in one place.

Github link - https://lnkd.in/fGeGXQs

#datascience #machinelearning #excel #deeplearning #python #R #sql #matlab #datamining #datawarehousing

✴️ @AI_Python_EN

The ability to deal with imbalanced datasets is a must-have for any #datascientist. Here are 4 tutorials to learn the different techniques of handling imbalanced data:

How to handle Imbalanced #Classification Problems in #MachineLearning? - https://buff.ly/2sIsR0M

Investigation on Handling Structured & Imbalanced Datasets with #DeepLearning - https://buff.ly/2MpxuG1

This Machine Learning Project on Imbalanced Data Can Add Value to Your #DataScience #Resume - https://buff.ly/2Mpr2i0

Practical Guide to deal with Imbalanced Classification Problems in #R - https://buff.ly/2MrS8Fr

✴️ @AI_Python_EN

The best way to learn #DeepLearning is by practicing it. But which framework to use? Here are 5 articles to get you started!

A Comprehensive Introduction to #PyTorch - https://bit.ly/2L8Rj7n

Learn How to Build Quick & Accurate Neural Networks using PyTorch (& 4 Case Studies) - https://bit.ly/2Vts9nY

Get Started with Deep Learning using #Keras and #TensorFlow in #R - https://bit.ly/2Iro2BY

TensorFlow 101: Understanding Tensors and Graphs - https://bit.ly/2GNg195

An Introduction to Implementing #NeuralNetworks using TensorFlow - https://bit.ly/2V17cBs

✴️ @AI_Python_EN

Data Visualization is a very important step in Data Science, so we should try to MASTER it.

Here are the useful links for Data Visualization -

1)Quick and Easy Data Visualizations in Python with Code.
(https://lnkd.in/fXJ-_Y8)

2)10 Useful Python Data Visualization Libraries for Any Discipline.
(https://lnkd.in/fBxbHwr)

3)Top 50 matplotlib Visualizations – The Master Plots (with full python code).
(https://lnkd.in/fGrnGax)

4)Data Visualization Effectiveness Profile.
(https://lnkd.in/f3v52Fd)

5)The Visual Perception of Variation in Data Displays.
(https://lnkd.in/fm-TbPM)

6)Matplotlib Tutorial – A Complete Guide to Python Plot w/ Examples.
(https://lnkd.in/fFkUgQP)

7)Interactive Data Visualization in Python With Bokeh.
(https://lnkd.in/fEfQAvg)

8) Data Visualization in R
https://lnkd.in/fEvZB_N

9) The Next Level of Data Visualization in Python (Plotly)
https://lnkd.in/fKn4cPM

#datascience #dataanalysis #datavisualization #python #r

✴️ @AI_Python_EN

letting beginners and experts alike learn about SAP HANA.
Download here --> https://lnkd.in/eTtdvi4

End to end Machine learning platform.
Bring your own language and microservices.Java, Node.js and Python are the officially supported languages.

SAP HANA is an ACID-compliant database and application development platform. You can use advanced data processing capabilities—text, graph, spatial, predictive, and more—to pull insights from all types of data.

#machinelearning #artificialintelligence #datascience #ml #ai #deeplearning #python #R #java #SQL

✴️ @AI_Python_EN

All You Need About Common #MachineLearning Algorithms

Here is the list of commonly used machine learning algorithms. The code is provided in both #R and #Python. These algorithms can be applied to almost any data problem:

✅Linear Regression
✅Logistic Regression
✅Decision Tree
✅SVM
✅Naive Bayes
✅kNN
✅K-Means
✅Random Forest
✅Dimensionality Reduction Algorithms
✅Gradient Boosting algorithms
✔️GBM
✔️XGBoost
✔️LightGBM
✔️CatBoost


#ai #datascienece

✴️ @AI_Python_EN

💡💡 Commonly used Machine Learning Algorithms 💡💡

Here is the list of commonly used machine learning algorithms. The code is provided in both #R and #Python. These algorithms can be applied to almost any data problem:

✅Linear Regression
✅Logistic Regression
✅Decision Tree
✅SVM
✅Naive Bayes
✅kNN
✅K-Means
✅Random Forest
✅Dimensionality Reduction Algorithms
✅Gradient Boosting algorithms
✔️GBM
✔️XGBoost
✔️LightGBM
✔️CatBoost

Credit: Analytics Vidhya,Sunil Ray

Thanks for the share Steve Nouri.

#datascience #deeplearning #ai #artificialintelligence #machinelearning #data #r #python

✴️ @AI_Python_EN

Quick links for all things #R and #Python:

1. Overview of using python with RStudio: https://lnkd.in/d5NkJAt
2. Python & #shiny: https://lnkd.in/dVfkE6b
3. Python & #rmarkdown: https://lnkd.in/dXpSd7i
4. Python with #plumber: https://lnkd.in/dn2pEAQ

For a central location to publish all of your team's data products (R artifacts, R & python mixed assets, and #jupyternotebooks), check out RStudio Connect: https://lnkd.in/dXW7iPG

✴️ @AI_Python_EN

Credit Risk Analysis Using #MachineLearning and #DeepLearning Models

Lovely paper by Peter Martey Addo, Dominique Guegan and Bertrand Hassani

Code on #Github (it's in #R)

https://github.com/brainy749/CreditRiskPaper

✴️ @AI_Python_EN