This Google Tutorial (96 slides total) on Machine Learning is the best:

🌎 https://bit.ly/2kyUKne

#BigData #DataScience #NeuralNetworks #AI #DeepLearning #ML #Algorithms #DataScientists #ReinforcementLearning

❇️ @AI_Python
✴️ @AI_Python_EN

3 reasons why #Coding (specifically, #Python) Should Become Your Official Corporate Language:

1)Teaches to think and solve problems.

2)Teaches the principles of Open-Source.

3)Teaches to work collaboratively.

https://bit.ly/2z3Qp2A #abdsc #DataScience #DataScientists
❇️ @AI_Python_EN
🗣 @AI_Python_arXiv
✴️ @AI_Python

A tip for all prospective #datascientists: #datascience is nothing but a new means to solve #business problems. #problemsolving will be only key to success. Coding languages & technologies will keep changing. Problem solving skills will always be top priority for any recruiter.

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_Arxiv

Check out the new "Machine Learning Guide for 2019", which includes 20 Free Resources (blogs & videos) to Learn Machine Learning: https://lnkd.in/ejqejpA by the Open Data Science Conference (ODSC) team.

#BigData #DataScience #DataScientists #AI #DeepLearning


🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

BEST DEEP LEARNING JOKES SO FAR

I can't stop laugh when watching this, an over-dramatization film on Deep Learning deployment. Funny, because its really often happen in Data Science live.

#deeplearning #python #anaconda #pyception

Disclaimer: This trailer is made by Anaconda, Inc.. This is made for AnacondaCON 2018 you can see full video in their youtube channel https://lnkd.in/fe5CW9N For 2019 in April you can register this https://anacondacon.io/

#technology #datascience #DataScientists

🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

To become a data scientist, is it better to be a generalist or a specialist?

The answer: you need to be both.

There is a very broad set of requirements to work as a data scientist, and you need familiarity with all of them to do the job:

* Data loading
* Data manipulation
* Feature engineering
* Model selection
* Model tuning
* Model evaluation
* Coding
* Visualization
* Report creation
* Presenting
* Business acumen
* Etc

What you also need:

👉 One area of specialization where you bring unique expertise to the team.

Data science is a team sport, and in order to build an effective team, you need complimentary skills that lift the team above the sum of its parts.

Each individual should be able to function on their own, but also contribute a unique skills set to the team.

👉 Agree or disagree?

#datascience #teams #aspiring #DataScientists

❇️ @AI_Python
🗣 @AI_Python_Arxiv
✴️ @AI_Python_EN

SELECTED MISTAKE THAT TOO COMMON FROM DATA SCIENCE ASPIRANTS

Getting that first break in #DataScience is tough. Check out these 4 awesome articles to learn tips and tricks from experts on how to have a fulfilling career in this field:

1. 13 Common Mistakes Amateur #DataScientists Make and How to Avoid Them - https://lnkd.in/f348chG

2. Busted! 11 Myths Data Science Transitioners Need to Avoid - https://lnkd.in/fmygG9B

3. 4 Secrets for a Future Ready Career in Data Science - https://lnkd.in/feNxs8b

4. The Most Comprehensive Data Science & #MachineLearning Interview Guide You’ll Ever Need - https://lnkd.in/fR2uGgE

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

Here are the COMPLETE Lecture notes on Professor Andrew Ng's


Stanford Machine Learning Lecture: https://lnkd.in/gR5sRHg

#lecturing #machinelearning #beginner #artificialintellegence #fundamentals #artificailintelligence #neuralnetwork #repository #datascientists #computervision #neuralnetworks

✴️ @AI_Python_EN
❇️ @AI_Python
🗣 @AI_Python_arXiv

As a #datascience professional, you are bound to come across applications and problems to be solved through #LinearProgramming. Better get started today with these two awesome tutorials:

Introductory guide on Linear Programming for (aspiring) #datascientists - https://lnkd.in/fWcqKMn

A Beginner’s guide to Shelf Space Optimization using Linear Programming - https://lnkd.in/f8swcdR
✴️ @AI_Python_EN

Comprehensive Collection of #DataScience and #MachineLearning Resources for #DataScientists includes “Great Articles on Natural Language Processing” +much more 👉https://bit.ly/2nvMXIx #abdsc #BigData #AI #DeepLearning #Databases #Coding #Python #Rstats #NeuralNetworks #NLProc

✴️ @AI_Python_EN

Useful #Linux cheat sheet for #datascientists

✴️ @AI_Python_EN

Google Machine Learning (101 slides) - Jason Mayes

posts: https://lnkd.in/ev9S2hh

#machinelearning #artificialintelligence #datascience #ml #ai #DeepLearning #BigData #NeuralNetworks #Algorithms #DataScientists #ReinforcementLearning

✴️ @AI_Python_EN

#DataScience Learning Path For Complete Beginners:
https://bit.ly/2JhcjXW
#BigData #MachineLearning #AI #DataScientists #Python

✴️ @AI_Python

A collection of research papers on decision trees, classification trees, and regression trees with implementations:
https://github.com/benedekrozemberczki/awesome-decision-tree-papers
#BigData #MachineLearning #AI #DataScience #Algorithms #NLProc #Coding #DataScientists

✴️ @AI_Python_EN

--Activation Function in CNN--
-Image Analysis-

#imageanalysis #machinelearning #clustering #datascientists #kmeans #deeplearning #neuralnetwork #underfitting

Convolutional #NeuralNetworks (CNN) for Image Classification — a step by step illustrated tutorial: https://dy.si/hMqCH
BigData #AI #MachineLearning #ComputerVision #DataScientists #DataScience #DeepLearning #Algorithms

✴️ @AI_Python_EN

Model interpretation and feature importance is a key for #datascientists to learn when running #machinelearing models. Here is a snippet from the #Genomics perspective.
a) Feature importance scores highlight parts of the input most predictive for the output. For DNA sequence-based models, these can be visualized as a sequence logo of the input sequence, with letter heights proportional to the feature importance score, which may also be negative (as visualized by letters facing upside down).
b ) Perturbation-based approaches perturb each input feature (left) and record the change in model prediction (centre) in the feature importance matrix (right). For DNA sequences, the perturbations correspond to single base substitutions.
c) Backpropagation- based approaches compute the feature importance scores using gradients or augmented gradients such as DeepLIFT (Deep Learning Important FeaTures)* for the input features with respect to model prediction.
Link to this lovely paper:
https://lnkd.in/dfmvP9c

❇️ @AI_Python_EN