More updates and links added across various categoies: Cloud, Courses…

…, Data, NLP, and Python Programming
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diff --git a/Programming-in-Python.md b/Programming-in-Python.md
@@ -50,6 +50,7 @@
 - [Understand the use of *args and **kwargs](https://morioh.com/p/252b73e0be0a?f=5c21f93bc16e2556b555ab2f&fbclid=IwAR2P_D8kr9Gf2gCjd2pf57ugkuv0qBfG0JEuAijGgl3JE2o_N1_MVk7u8CM)
 - [Here are some great Python Resources to learn #DataScience and #MachineLearning](https://www.linkedin.com/posts/asif-bhat_datascience-machinelearning-activity-6638314806367621120-vWp0)
 - [👉 🐍Machine Learning Projects with Python 🐍👈](https://www.linkedin.com/posts/asif-bhat_machine-learning-projects-activity-6643602873957605376-5VJ4)
+- [Python NumPy for Artificial Intelligence : 14. Array Comparison | Logical Operations](https://www.youtube.com/watch?v=G8qbRwB-L8Y&feature=youtu.be)
 
 ## Courses
 

diff --git a/cloud-devops-infra/README.md b/cloud-devops-infra/README.md
@@ -141,6 +141,7 @@ reproducible research
   - [Graphcore are making Poplar Software Documentation publicly available](https://www.graphcore.ai/posts/graphcore-makes-poplar-sdk-docs-publicly-available?utm_content=121700693&utm_medium=social&utm_source=linkedin&hss_channel=lcp-10812092)
   - [Watch the Graphcore quick guide to the #IPU](https://www.graphcore.ai/products/ipu?utm_campaign=Machine%20Intelligence%20Positioning&utm_content=120000808&utm_medium=social&utm_source=linkedin&hss_channel=lcp-10812092) [LinkedIn Post](https://www.linkedin.com/posts/graphcore_ipu-ai-semiconductors-activity-6640930639287730176-bRvL)
   - [Dissecting the Graphcore IPU Architecture via Microbenchmarking](https://www.graphcore.ai/hubfs/assets/pdf/Citadel%20Securities%20Technical%20Report%20-%20Dissecting%20the%20Graphcore%20IPU%20Architecture%20via%20Microbenchmarking%20Dec%202019.pdf?utm_content=109984229&utm_medium=social&utm_source=twitter&hss_channel=tw)
+  - [Learn how to develop and train models for the Graphcore #IPU using TensorFlow](https://hubs.ly/H0qFL1Y0)
   - [Graphcore C2 Card performance for image-based deep learning application](https://www.graphcore.ai/hubfs/Graphcore%20C2%20Card%20performance%20for%20image%20based%20deep%20learning%20application_v2.pdf)
   - [Graphcore Whitepaper: DELL DSS8440 GRAPHCORE IPU SERVER](https://www.graphcore.ai/hubfs/Lead%20gen%20assets/DSS8440%20IPU%20Server%20White%20Paper_2020.pdf)
   - [Graphcore Benchmarks](https://www.graphcore.ai/hubfs/assets/pdf/Benchmarks_slides_May2020-comp.pdf)

diff --git a/courses.md b/courses.md
@@ -22,7 +22,6 @@
 ## Algorithms
 
 - [Algorithms at Coursera by Wayne and Sedgewick](https://www.coursera.org/course/algs4partI) | [2](https://www.coursera.org/browse/computer-science/algorithms)
-- [🉐 𝘈𝘋𝘝𝘈𝘕𝘛𝘈𝘎𝘌𝘚 𝘈𝘕𝘋 🉐𝘗𝘐𝘛𝘍𝘈𝘓𝘓𝘚 𝘖𝘍 𝘋𝘐𝘍𝘍𝘌𝘙𝘌𝘕𝘛 𝘈𝘓𝘎𝘖𝘙𝘐𝘛𝘏𝘔𝘚](https://www.linkedin.com/posts/ashishpatel2604_datascience-deeplearning-machinelearning-activity-6668792791658852352-87Po)
 
 ## Cambridge Spark
 

diff --git a/data/data-exploratory-analysis.md b/data/data-exploratory-analysis.md
@@ -55,6 +55,7 @@ aka *_Exploratory Data Analysis_*
 - [Have u ever heard about Bounded Clustering?](https://towardsdatascience.com/bounded-clustering-7ac02128c893) [LinkedIn Post](https://www.linkedin.com/posts/ashishpatel2604_bounded-clustering-activity-6604231470691217408-Fhyn)
 - [Spectral Clustering : How Math is Redefining Decision Making](https://www.datasciencecentral.com/profiles/blogs/spectral-clustering-how-math-is-redefining-decision-making) [LinkedIn Post](https://www.linkedin.com/posts/data-science-central_spectral-clustering-how-math-is-redefining-activity-6644369189828120576-R50H)
 - [Python: Implementing a k-means algorithm with sklearn](https://www.datasciencecentral.com/profiles/blogs/python-implementing-a-k-means-algorithm-with-sklearn) [LinkedIn Post](https://www.linkedin.com/posts/vincentg_python-implementing-a-k-means-algorithm-activity-6646407378474450944-wDzH)
+- [K-means Clustering on Ordinal Data](https://www.linkedin.com/posts/towards-data-science_k-means-clustering-on-ordinal-data-activity-6668777271676960768-bZ05)
 - [Journey to Machine Learning – K-Means Clustering](https://www.linkedin.com/pulse/all-cheatsheets-one-place-vipul-patel/) [LinkedIn Post](https://www.linkedin.com/posts/vipulppatel_data-analytics-businessintelligence-activity-6640085732100710400-oGp7)
    - [Comparison of Segmentation Approaches using Clustering (9 pages)](https://www.linkedin.com/feed/update/urn:li:activity:6540091805428518912?lipi=urn%3Ali%3Apage%3Ad_flagship3_pulse_read%3BmoauZl5XRFyXpGV91RiG2w%3D%3D)
    - [Guide to HIERARCHICAL Clustering (23 pages) and how to Perform it in Python](https://www.linkedin.com/feed/update/urn:li:activity:6539263090955997184/) 

diff --git a/data/data-generation.md b/data/data-generation.md
@@ -59,10 +59,11 @@
 
 - [Generating music in the raw audio domain](https://www.youtube.com/watch?v=y8mOZSJA7Bc) by [Sander Dieleman](http://benanne.github.io/about/)
 
-### Resampling
+### Sampling
 
 - [Random Number Generation and Sampling Methods](https://www.codeproject.com/Articles/1190459/Random-Number-Generation-and-Sampling-Methods)
 - [Resampling Methods: Bootstrap vs jackknife](https://www.linkedin.com/posts/data-science-central_resampling-methods-bootstrap-vs-jackknife-activity-6610622844785221632-bSXb)
+- [Oversampling/Undersampling in Logistic Regression](https://www.linkedin.com/posts/vincentg_oversamplingundersampling-in-logistic-regression-activity-6664247426364252162-Md0U)
 
 # Contributing
 

diff --git a/data/data-preparation.md b/data/data-preparation.md
@@ -41,6 +41,10 @@
 - [Data preparation for factor analysis](https://www.linkedin.com/posts/data-science-central_data-preparation-for-factor-analysis-activity-6608507889915092992-T1Vv)
 - [7 Pandas Functions to Reduce Your Data Manipulation Stress by Andre Ye](https://towardsdatascience.com/7-pandas-functions-to-reduce-your-data-manipulation-stress-25981e44cc7d) [LinkedIn Post](https://www.linkedin.com/posts/towards-data-science_7-pandas-functions-to-reduce-your-data-manipulation-activity-6655006784069214208-R9Zn)
 
+### Transformations
+
+- [How to Use Quantile Transforms for Machine Learning](https://machinelearningmastery.com/quantile-transforms-for-machine-learning/)
+- [pre-processing filters like denoising, smoothing, thresholding, rescaling with python. As a part 3 of this series, in this post I have explained other pre-processing methods like masking, blending, image sharpening, erosion, dilation, geometric transformation](https://www.let-the-data-confess.com/image-pre-processing-through-opencv-part-3/)
 
 ### Scaling and normalisation
 

diff --git a/data/feature-engineering.md b/data/feature-engineering.md
@@ -23,6 +23,7 @@
     - [Feature engineering and Dimensionality reduction](https://towardsdatascience.com/dimensionality-reduction-for-machine-learning-80a46c2ebb7e)
     - [Seven Techniques for Data Dimensionality Reduction](https://www.kdnuggets.com/2015/05/7-methods-data-dimensionality-reduction.html)
     - [Linear Discriminant Analysis is a simple yet intuitive technique.  At it's first description it is very similar to PCA.  In PCA to find the eigen value and eigen factors we use covariance matrix](https://www.youtube.com/watch?v=D2HArUvOQaw&feature=youtu.be) | [Other PCA tutorials](https://youtu.be/D2HArUvOQaw)
+    - [Principal Component Analysis for Dimensionality Reduction in Python](https://www.linkedin.com/posts/jasonbrownlee_principal-component-analysis-for-dimensionality-activity-6664240738139799552-gCqp)
 
 # Contributing
 

diff --git a/details/julia-python-and-r/machine-learning.md b/details/julia-python-and-r/machine-learning.md
@@ -10,7 +10,7 @@
   - [A curated list of awesome Machine Learning frameworks, libraries and software](https://github.com/josephmisiti/awesome-machine-learning)
   - [Top ML repos](https://github.com/yazdotai/top-machine-learning)
   - [Hands on ML](https://github.com/ageron/handson-ml)
-  - [𝗣𝘆𝘁𝗵𝗼𝗻 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀](https://www.linkedin.com/posts/martinroberts_python-machine-learning-projects-activity-6620692910499295232-B8Gq)
+  - [3 types of projects you should do if you are just diving into #datascience, #machinelearning](https://www.linkedin.com/posts/ayonroy2000_datascience-machinelearning-activity-6668565412839604224-fIKZ)
   - [ML for Software Engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)
   - [PredictionIO, a machine learning server for developers and ML engineers. Built on Apache Spark, HBase and Spray](https://github.com/apache/predictionio)
   - [Dive into Machine Learning with Python Jupyter notebook and scikit-learn!](https://github.com/hangtwenty/dive-into-machine-learning)

diff --git a/details/machine-learning-algorithms.md b/details/machine-learning-algorithms.md
@@ -30,7 +30,7 @@
 - [140 ML Formulas](https://www.linkedin.com/posts/bo-li-8503b896_140-machine-learning-formulas-activity-6632888211825881088-8q_V)
 - [Sparse Forest with FIL](https://www.linkedin.com/posts/miguelusque_sparse-forests-with-fil-activity-6628874338785337344-89hf)
 - [Google T5 Explores the Limits of Transfer Learning](https://www.linkedin.com/posts/eric-feuilleaubois-ph-d-43ab0925_google-t5-explores-the-limits-of-transfer-activity-6631788661920804864-wH9d)
-
+- [🉐 𝘈𝘋𝘝𝘈𝘕𝘛𝘈𝘎𝘌𝘚 𝘈𝘕𝘋 🉐𝘗𝘐𝘛𝘍𝘈𝘓𝘓𝘚 𝘖𝘍 𝘋𝘐𝘍𝘍𝘌𝘙𝘌𝘕𝘛 𝘈𝘓𝘎𝘖𝘙𝘐𝘛𝘏𝘔𝘚](https://www.linkedin.com/posts/ashishpatel2604_datascience-deeplearning-machinelearning-activity-6668792791658852352-87Po)
 
 # Contributing
 

diff --git a/details/maths-stats-probability.md b/details/maths-stats-probability.md
@@ -50,6 +50,7 @@
 - [A Semi-Supervised Classification Algorithm using Markov Chain and Random Walk in R](https://www.linkedin.com/posts/data-science-central_a-semi-supervised-classification-algorithm-activity-6614306095047462912-7rjG)
 - [Interesting Problem: Self-correcting Random Walks](https://www.linkedin.com/posts/data-science-central_interesting-problem-self-correcting-random-activity-6622308830137114624-9D2L)
 - 24 Uses of Statistical Modeling: [Part I](https://www.linkedin.com/posts/data-science-central_24-uses-of-statistical-modeling-part-i-activity-6616738123302924288-fPqG) | [Part II](https://www.linkedin.com/posts/data-science-central_24-uses-of-statistical-modeling-part-ii-activity-6606560053312970752-6X1H)
+- [Statistical Models (detailed diagram)](https://www.dropbox.com/s/5a8w8kckyfeaix0/statistical%20models%20-%20diagram.pdf?dl=0)
 - [Encyclopedia of Statistics by Data Science Central](https://www.linkedin.com/posts/ashishpatel2604_encyclopediastatistics-activity-6606068070370902016-TA04)
 - [Statistical Inquiry Cycle](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintelligence-activity-6624989612928536576-Z7NE)
 - [Your Guide to Master Hypothesis Testing in Statistics](https://www.linkedin.com/posts/data-science-central_your-guide-to-master-hypothesis-testing-in-activity-6624332159144509441-HVq_)
@@ -59,45 +60,24 @@
 - [Didn't Learn Statistics Yet?](https://www.linkedin.com/posts/iamsivab_42-open-problems-in-mathematics-ugcPost-6604724523625472000-TieN)
 - [7 Traps to Avoid Being Fooled by Statistical Randomness](https://www.linkedin.com/posts/data-science-central_7-traps-to-avoid-being-fooled-by-statistical-activity-6607693525188427777-ZEsL)
 - [Three classes of metrics: centrality, volatility, and bumpiness](https://www.analyticbridge.datasciencecentral.com/profiles/blogs/three-classes-of-metrics-centrality-volatility-and-bumpiness)
-
-Why Including Effect Size and Knowing your Statistical Power ~ are Important](https://www.datasciencecentral.com/profiles/blogs/why-including-effect-size-and-knowing-your-statistical-power-are
-
-Important #Statistics formula in one picture...👈👈👈Must reas](https://www.linkedin.com/posts/ashishpatel2604_statistics-artificialintelligence-machinelearning-activity-6635127417809801216-l94G
-
-
-The Cartoon Guide To Statistics](https://www.linkedin.com/posts/iamsivab_the-cartoon-guide-to-statistics-activity-6638031133118423040-7h7Y
-
-9 Off-the-beaten-path Statistical Science Topics with Interesting Applications](https://www.linkedin.com/posts/data-science-central_9-off-the-beaten-path-statistical-science-activity-6645774952333131776-zGdV
-
-Here are some essential math/stats for #DataScience](https://www.linkedin.com/posts/nabihbawazir_datascience-datascience-statistics-activity-6639121813903368192-BdWh
-
-Stats + Data Science Education](https://www.linkedin.com/posts/mattdancho_datascience-machinelearning-activity-6639131770174357505-RZ_Z
-
-Statistics for Data Science in One Picture](https://www.linkedin.com/posts/data-science-central_statistics-for-data-science-in-one-picture-activity-6638978669270360064-4YSS
-
-Diff between stats and DS: big data and inferential stats](https://www.linkedin.com/posts/ajitjaokar_the-difference-between-statistics-and-data-activity-6637582237258825728-IAIm
-
-Data Science: The End of Statistics?](https://www.linkedin.com/posts/data-science-central_data-science-the-end-of-statistics-activity-6645803640802070528-BGmH
-
-Statistical Significance Tests for Comparing Machine Learning Algorithms](https://machinelearningmastery.com/statistical-significance-tests-for-comparing-machine-learning-algorithms/
-
-Statistical Modeling; Selecting Predictors is a Challenge for Data Scientists](https://www.linkedin.com/posts/data-science-central_statistical-modeling-selecting-predictors-activity-6642542151878144000-3tD0
-
-Machine Learning vs. Traditional Statistics: Different philosophies, Different Approaches](https://www.linkedin.com/posts/data-science-central_machine-learning-vs-traditional-statistics-activity-6644415494516469760-WT--
-
-Becoming a Master of Statistical Inference by Robert Wood](https://www.linkedin.com/posts/towards-data-science_becoming-a-master-of-statistical-inference-activity-6644473375601369088-mKmB
-
-Ten Simple Rules for Effective Statistical Practice](https://www.linkedin.com/posts/data-science-central_ten-simple-rules-for-effective-statistical-activity-6640293340111794176-3B62
-
-💦 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗶𝘀 𝗮𝗻 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗮𝗿𝘁 𝗳𝗼𝗿 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮, 𝘀𝗼 𝗯𝗲𝗳𝗼𝗿𝗲 𝗱𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘁𝘀 𝗴𝗼𝗼𝗱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗼𝗳 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁'𝘀 𝘂𝘀𝗲𝗱.](https://www.linkedin.com/posts/ashishpatel2604_statistics-cheat-sheet-activity-6650269088838975488-NtgL
-
-Practical Statistics](https://www.youtube.com/watch?v=CwJ4pcEYjT0&list=PLcQCwsZDEzFnmUDaOHQeWbiP7N_acsFb9
-
-Deciphering information and misinformation: Inspired by the book "A Field Guide to Lies and Statistics"](https://www.linkedin.com/posts/data-science-central_deciphering-information-and-misinformation-activity-6650152295398981632-0TJn
-
-
-💦 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗶𝘀 𝗮𝗻 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗮𝗿𝘁 𝗳𝗼𝗿 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮, 𝘀𝗼 𝗯𝗲𝗳𝗼𝗿𝗲 𝗱𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘁𝘀 𝗴𝗼𝗼𝗱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗼𝗳 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁'𝘀 𝘂𝘀𝗲𝗱.](https://www.linkedin.com/posts/ashishpatel2604_statistics-cheat-sheet-activity-6650269088838975488-NtgL
-
+- [Why Including Effect Size and Knowing your Statistical Power ~ are Important](https://www.datasciencecentral.com/profiles/blogs/why-including-effect-size-and-knowing-your-statistical-power-are)
+- [Important #Statistics formula in one picture...👈👈👈Must reas](https://www.linkedin.com/posts/ashishpatel2604_statistics-artificialintelligence-machinelearning-activity-6635127417809801216-l94G)
+- [The Cartoon Guide To Statistics](https://www.linkedin.com/posts/iamsivab_the-cartoon-guide-to-statistics-activity-6638031133118423040-7h7Y)
+- [9 Off-the-beaten-path Statistical Science Topics with Interesting Applications](https://www.linkedin.com/posts/data-science-central_9-off-the-beaten-path-statistical-science-activity-6645774952333131776-zGdV)
+- [Here are some essential math/stats for #DataScience](https://www.linkedin.com/posts/nabihbawazir_datascience-datascience-statistics-activity-6639121813903368192-BdWh)
+- [Stats + Data Science Education](https://www.linkedin.com/posts/mattdancho_datascience-machinelearning-activity-6639131770174357505-RZ_Z)
+- [Statistics for Data Science in One Picture](https://www.linkedin.com/posts/data-science-central_statistics-for-data-science-in-one-picture-activity-6638978669270360064-4YSS)
+- [Diff between stats and DS: big data and inferential stats](https://www.linkedin.com/posts/ajitjaokar_the-difference-between-statistics-and-data-activity-6637582237258825728-IAIm)
+- [Data Science: The End of Statistics?](https://www.linkedin.com/posts/data-science-central_data-science-the-end-of-statistics-activity-6645803640802070528-BGmH)
+- [Statistical Significance Tests for Comparing Machine Learning Algorithms](https://machinelearningmastery.com/statistical-significance-tests-for-comparing-machine-learning-algorithms/)
+- [Statistical Modeling; Selecting Predictors is a Challenge for Data Scientists](https://www.linkedin.com/posts/data-science-central_statistical-modeling-selecting-predictors-activity-6642542151878144000-3tD0)
+- [Machine Learning vs. Traditional Statistics: Different philosophies, Different Approaches](https://www.linkedin.com/posts/data-science-central_machine-learning-vs-traditional-statistics-activity-6644415494516469760-WT--)
+- [Becoming a Master of Statistical Inference by Robert Wood](https://www.linkedin.com/posts/towards-data-science_becoming-a-master-of-statistical-inference-activity-6644473375601369088-mKmB)
+- [Ten Simple Rules for Effective Statistical Practice](https://www.linkedin.com/posts/data-science-central_ten-simple-rules-for-effective-statistical-activity-6640293340111794176-3B62)
+- [💦 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗶𝘀 𝗮𝗻 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗽𝗮𝗿𝘁 𝗳𝗼𝗿 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮, 𝘀𝗼 𝗯𝗲𝗳𝗼𝗿𝗲 𝗱𝗲𝗲𝗽 𝗱𝗶𝘃𝗲 𝗶𝗻𝘁𝗼 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘁𝘀 𝗴𝗼𝗼𝗱 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘁𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗼𝗳 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁'𝘀 𝘂𝘀𝗲𝗱.](https://www.linkedin.com/posts/ashishpatel2604_statistics-cheat-sheet-activity-6650269088838975488-NtgL)
+- [Practical Statistics](https://www.youtube.com/watch?v=CwJ4pcEYjT0&list=PLcQCwsZDEzFnmUDaOHQeWbiP7N_acsFb9)
+- [Deciphering information and misinformation: Inspired by the book "A Field Guide to Lies and Statistics"](https://www.linkedin.com/posts/data-science-central_deciphering-information-and-misinformation-activity-6650152295398981632-0TJn)
+- [The 17 equations that changed the course of history](https://www.linkedin.com/posts/vincentg_the-17-equations-that-changed-the-course-activity-6664334747927400449-ya4I)
 - [Statistics by Chris Albon](https://chrisalbon.com/#statistics) - covering Frequentist topics
 - [See Data > Statistics section more related links](../data/README.md#statistics)
 

diff --git a/natural-language-processing/library-framework-models-tools-services.md b/natural-language-processing/library-framework-models-tools-services.md
@@ -39,6 +39,7 @@
   - [How do we implement large-scale #NLP models on IPU](https://www.linkedin.com/posts/graphcore_arianna-saracino-product-support-engineer-activity-6615949485463920640-7Pwa)
   - [NLP in fraud dectection: Case study](https://www.linkedin.com/posts/data-science-central_natural-language-understanding-nlu-in-fraud-activity-6623003404279005184-Fg_L)
   - TextAttack – a really cool Python framework for attacking NLP models and augmenting text datasets: [GitHub](https://github.com/QData/TextAttack/) | [Tweet](https://twitter.com/lavanyaai/status/1260384065481392129)
+  - Albumentations package for NLP data augmentation: [Kaggle Kernel 1](https://www.kaggle.com/shonenkov/tpu-training-super-fast-xlmroberta) | [Kaggle Kernel 2](https://www.kaggle.com/shonenkov/nlp-albumentations)
 
 # Contributing