When doing regression (or matching, or weighting, or whatever), don’t say “control for,” say “adjust for”

Blog post by Andrew Gelman, with a whole bunch of interesting comments to it

Link Review

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On the "AI with AI" podcast, hosts Andy Ilachinski and David Broyles talk about my book.

Transcript: "One of the best self-contained texts that I've seen on machine learning. It's by Andriy Burkov, he's a PhD in AI and he's a senior data scientist and Machine Learning team leader at Gartner. He has written the hundred-page machine learning book (that's the title by the way) and it's a little bit over a hundred pages. If you go to its site, you can purchase a PDF directly for 20 dollars. You can either purchase a hard copy. Obviously, if you do purchase a hard copy you can send an email, according to the site, to the publisher and you will get a PDF for free. It is short, it's to the point, it has detail. If you are a seasoned practitioner this will bring you up to speed on related methods that you may immediately use. this.
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HOW TO LEARN PYTHON FOR DATA SCIENCE?

Someone ask me to update how to learn data science and please give R alternative as well, so I make it relevant to today standard

✅ Step 1
Building Learning Path
https://lnkd.in/fduvKgb

✅ Step 2
Download and Install Anaconda
https://lnkd.in/gWHY_ij

✅ Step 3
a. Learn the basics of Python (Lists, Tuples, Dictionaries, etc)
b. Understand the basics of data structures and algorithms
https://lnkd.in/gYKnJWN

✅ Step 4
Do more practice problems in Python
Codeacademy: https://lnkd.in/gGQ7cuv

✅ Step 5
Learn the scientific libraries (NumPy, SciPy, Pandas)
Pandas: https://lnkd.in/g4DFNpJ

✅ Step 6
Machine Learning with Scikit-Learn
Machine Learning in 20min: https://lnkd.in/g-Su_um
Scikit-Learn Tutorial: https://lnkd.in/gSThdRD

✅ Step 7:
Practice your machine learning skills
Kaggle Machine Learning Tutorial: https://lnkd.in/gT5nNwS

✅ Step 8:
Practice advanced library
a.PyTorch
https://lnkd.in/fzS52P9

b.TensorFlow
https://lnkd.in/fXKQkGy

c.Dlib
https://lnkd.in/fzPM2Gs

Kaggle Machine Learning Tutorial: https://lnkd.in/gT5nNwS

#machinelearning #datascience #python #scikitlearn #numpy #algorithms

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New features from #DLI website we added a new menu page allow the user to easily find dataset and scientific paper from our website. First of all, read the scientific paper and find some match for our problem. After that find a good dataset and start to replicate the same model of the article. Once you did that, find the best implementation allow to fit with your specific problem. Enjoy Deep Learning!!! https://lnkd.in/dufCnMs

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#AI approach outperformed human experts (AGAIN) in identifying #cervical precancer!

A research team led by investigators from the National Institutes of Health and Global Good has developed a #deeplearning #algorithm that can analyze digital images of a woman's cervix and accurately identify precancerous changes that require medical attention. This artificial intelligence (AI) approach, called automated visual evaluation, has the potential to revolutionize cervical cancer screening, particularly in low-resource settings.

To create the algorithm, the research team used more than 60,000 cervical images from an NCI archive of photos collected during a cervical cancer screening study that was carried out in Costa Rica in the 1990s.

Overall, the algorithm performed better than all standard screening tests at predicting all cases diagnosed during the Costa Rica study. Automated visual evaluation identified precancer with greater accuracy (AUC=0.91) than a human expert review (AUC=0.69) or conventional cytology (AUC=0.71).

Paper here: https://lnkd.in/dxETi8K
#algorithms #prediction #cancer #machinelearning #cnn #transferlearning

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Even a "simple" statistical procedure such as K-means isn't really that simple.

First, we need to clarify the objectives of the clustering and decide which of dozens of clustering methods to use. The K-means family is not always a good choice.

If we do decide to go with K-means, which type (e.g., K-means, K-medians)?

What are our candidate variables? Do they need to be re-coded or re-scaled? If so, how?

What range of cluster solutions to test?

What distance/similarity measures to use? There are dozens, it's not just Euclidean distance.

Initial seed selection - there are at least ten ways I know of.

The number of iterations and replications must also be decided.

Last but not least, interpreting the results and communicating our interpretations can make or break a cluster analysis.

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Building your own PC for #AI #DeepLearning is 10x cheaper than renting out GPUs on cloud, so it seems.

We have been advising enterprises for quite a while about this "Build Your Own Deep Learning Monster" versus Buy dilemma. We've helped them save millions already with unique deployments already. (see full comparison here: https://lnkd.in/dn39Wr3)

Here are the options:

1. For instance, building your own cool deeplearning box at home costs merely $2900/- , just go to PCPartpicker and build your own configuration , here is an example: https://lnkd.in/dWxH2iJ

2. Building a monster at home can cost you a lot cheaper than the Nvidia DGX Super Station (Nvidia 64G GPU boxes sells from $50K to $70K

Do note that performance may vary if you build your own quad-GPU box at home.

#machinelearning #gpus #cloudml

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Solving an interesting probability problem using Python and tensorflow-probability, my new article in Towards Data Science (Online Publication)

https://lnkd.in/gH9pkUG

#probability #python #datascience #technology

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The Best #FREE Books for Learning #DataScience

Link => bit.ly/AIFreeBooks

#ai #analytics #artificialinteligence #bi #bigdata #data #machinelearning

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Here's an infographic which displays most commonly used tools for data visualization by data scientists and data analysts for creating simple yet powerful visualizations! Download the infographic below. How many of them do you use? https://lnkd.in/fNVG4HW

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Here’s a list of 10 GREAT #SelfStarting #DataScience Projects to work on:

➖BEGINNER➖

⚠️ NOTE: The links provided will redirect you to a recommended Kaggle Kernel that I enjoyed. Use it as a reference before starting on your project :)

⚠️ IMPORTANT: Number 10 is a MUST DO!

1. Pokemon - Weedle's Cave 🐛
Python - https://lnkd.in/gcKWWQ2

2. Titanic ML 🚢
Python - https://lnkd.in/gafie9m
R - https://lnkd.in/gRRa7HV

3. Housing Prices Prediction 🏡
Python - https://lnkd.in/gX2FSDk
R - https://lnkd.in/ggFJSyd


➖INTERMEDIATE➖

4. Instacart Market Basket Analysis 🛒
Python - https://lnkd.in/gkNaXqH
R- https://lnkd.in/g2gthxu

5. Quora Question Pairs 👥
Project :https://lnkd.in/f3HQZsT
Tutorial (Python)- https://lnkd.in/fEzf-Xp

6. Human Resource Analytics 🕴🏻
Python - https://lnkd.in/gVUPfWm
R -https://lnkd.in/gHusQYX


➖ADVANCED➖

7. Analyzing Soccer Player Faces ⚽️
Python - https://lnkd.in/gUys_TS

8. Recruit Restaurant Visitor Forecasting 🍱
Python - https://lnkd.in/gjQvf74

9. TensorFlow Speech Recognition 🗣
Python - https://lnkd.in/g8SSPfW


➖MASTERY➖

10. Not Enough?
This is more complete guide from Analytics Vidhya
https://lnkd.in/g_QjzGe.

➖
Thanks!
#ml #tutorials #forecasting #analytics #guides

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"Machine Learning from scratch!"

Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library, by Quan Tran: https://lnkd.in/er_ZNgY

#ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning

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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

#artificialintelligence #deeplearning #machinelearning #reinforcementlearning

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Can anyone recommend a good book, article, YouTube or other source that explains AI and machine learning in terms ordinary businessperson can understand?

There are many excellent resources for those with a good background in statistics, AI and machine learning , e.g., the classic Artificial Intelligence (Russell and Norvig), but they are too long or too technical for most folks.

At the other extreme, there are also many news articles, blogs, conference presentations and what I call airplane books that I find superficial or even misleading.

My reason for asking is that I am often asked this question myself but don't have a good answer. Thanks in advance for your thoughts.

Share With Me Please: @farzadHEYdaryy

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Google's Open Images V4 is a publicly available dataset that contains 15.4M annotated bounding boxes for over 600 object categories. It has 1.9M images and is largest among all existing public image datasets with object location annotations.

We share a tutorial with a script for a fast downloader that allows you to filter and download images based on various classes and categories of your interest.

https://lnkd.in/gspXR6K

If you find this tool useful, please share.

#computervision #machinelearning #ai #training #objectdetection #deeplearning

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A useful guide for all beginners in #machinelearning & #datascience - It lists down the most active data scientist on github, free books, ipython notebooks, tutorials on github. https://bit.ly/2I9xmvM

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Getting that first break in #DataScience can be quite tough. Here are 3 inspiring stories from our community on how they transitioned into data science from other fields:

Step by Step process of How I Became a #MachineLearning Expert in 10 Months - https://lnkd.in/fUwbkR5 Madhukar Jha

Journey from an IT Engineer to Head of #Analytics - https://lnkd.in/fFPDC-Z Ritesh Mohan Srivastava

From a Paper Delivery Boy to a Lead #DataEngineer & #QlikView Luminary - https://lnkd.in/fKpgKyf

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WHAT IS USE CASES 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 Turover
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 #humanresource #artificialinteligence #analytics

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BERT: Pre-training of #deeplearning Bidirectional Transformers for Language Understanding

BERT slides

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