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

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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

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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
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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

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Useful #Linux cheat sheet for #datascientists

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Google Machine Learning (101 slides) - Jason Mayes

posts: https://lnkd.in/ev9S2hh

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

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

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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

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--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

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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

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