Added more links for python, data, maths, stats, probability, etc...

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diff --git a/Programming-in-Python.md b/Programming-in-Python.md
@@ -37,11 +37,14 @@
 - [Regex resources by Chris Albon](https://chrisalbon.com/#regex)
 - [WTF Python repo](https://github.com/satwikkansal/wtfpython)
 - [Introduction to Python](https://github.com/Imperial-College-Data-Science-Society/Lecture-1-Intro-to-Python)
+- [Introduction of Python Programming](https://www.linkedin.com/posts/nabihbawazir_python-self-study-activity-6605392829378367488-Q6Uz)
+- [Writing your first program in Python (2019) - Brown University](https://www.linkedin.com/posts/nabihbawazir_introductions-to-python-activity-6606104345299771392-t7f1)
 
 ## Intermediate / Advanced Python
 
 - [Scientific Python](https://github.com/Imperial-College-Data-Science-Society/Lecture-2-Scientific-Python)
 - [Neural Networks Matrices exploration - Under the Hood Mathematical Operations](https://github.com/souravs17031999/NeuralNets-Pure-Python)
+- [Understand the use of *args and **kwargs](https://morioh.com/p/252b73e0be0a?f=5c21f93bc16e2556b555ab2f&fbclid=IwAR2P_D8kr9Gf2gCjd2pf57ugkuv0qBfG0JEuAijGgl3JE2o_N1_MVk7u8CM)
 
 ## Courses
 
@@ -81,6 +84,7 @@ See **Python: Best practices** and **Python: Testing** under [Courses](./courses
 * [pytype](https://github.com/google/pytype) - A static type analyzer for Python code.
 * [Review of Python Static Analysis Tools](https://www.codacy.com/blog/review-of-python-static-analysis-tools/)
 * [Python Static Analysis Tools ](https://luminousmen.com/post/python-static-analysis-tools)
+* [PANDAS  👉 Reading and Writing Data 👈](https://www.linkedin.com/posts/asif-bhat_pandas-activity-6610445263372947456-oxup)
 * See [awesome-static-analysis for Python](https://github.com/mre/awesome-static-analysis/blob/master/README.md#python)
 
 ### Python wrappers
@@ -119,6 +123,10 @@ with nothing but Python
         - [Security](https://anvil.works/docs/security)
     - [Forum](https://anvil.works/forum/)
   - [Assembly](https://mardix.github.io/assembly/) - A Pythonic Object-Oriented Web Framework built on Flask
+- [A curated list of awesome Python frameworks, libraries, software and resources](https://github.com/vinta/awesome-python)
+- [Explanation of most popular Data Science Library (in Python)](https://www.linkedin.com/posts/nabihbawazir_artificialintellegence-datascience-machinelearning-activity-6617700382200164352-RDhs)
+- [50 most popular Python libraries and frameworks used in data science](https://www.linkedin.com/posts/nabihbawazir_python-datascience-dataanalysis-activity-6604589510447722496-AvyE)
+- [Python for 9 Purposes: The graphics miss Scikit-Learn and of course "Pandas"](https://www.linkedin.com/posts/nabihbawazir_python-for-9-purposesthe-graphics-miss-scikit-learn-activity-6605860742774259712-Was0)
 
 ## Best practices
 

diff --git a/README-details.md b/README-details.md
@@ -91,6 +91,18 @@ See [Mathematics, Statistics, Probability & Probabilistic programming](./details
   - [Learn Data Science by bitgrit](https://github.com/bitgrit-official/learndatascience)
   - **[and other related topics: Stats, Visualisations, Cheatsheets, etc...](data/README.md#data)**
 
+### Data Scientist
+
+- [How can I become a data scientist?](https://www.linkedin.com/posts/data-science-central_how-can-i-become-a-data-scientist-activity-6611453301030473728-0weA)
+- [Being a Data Science Contractor - UK: How to find work?](https://www.linkedin.com/posts/data-science-central_being-a-data-science-contractor-uk-how-activity-6618156469516849153-2WD-)
+- [How to switch career from Automation Testing to Data Science? Here is a simple guide.](https://careerhops.wixsite.com/mirrorneuron/post/automation-testing-to-data-science?fbclid=IwAR3HPbfiQ6Qmq4tJPbnI7SZA59QTuL9-IPObpgEIJ_q32O_xN9ZnNaG046M)
+- [9 Mistakes to avoid when starting your career in Data Science](https://www.linkedin.com/posts/nabihbawazir_datascience-machinelearning-artificialintelligence-activity-6620904711958687744-UiIm/)
+- [How can I become a data scientist?](https://www.linkedin.com/posts/data-science-central_how-can-i-become-a-data-scientist-activity-6611453301030473728-0weA)
+- [8 essential tools for data scientists](https://www.linkedin.com/posts/data-science-central_8-essential-tools-for-data-scientists-activity-6623350694613184512-esn2)
+- [Data Scientist is not One-Man-Army, but should know some tech concept, not mandatory to master (depend on the company), this is what I choose](https://www.linkedin.com/posts/nabihbawazir_data-scientist-is-not-one-man-army-but-should-activity-6602851212972912640-t828)
+- [The Ultimate Learning Path to Become a Data Scientist and Master Machine Learning](https://www.analyticsvidhya.com/blog/2019/01/learning-path-data-scientist-machine-learning-2019/)
+- [♦️MUST READ ARTICLES FOR DATA SCIENCE ENTHUSIAST♦️](https://www.linkedin.com/posts/asif-bhat_datascience-neverstoplearning-datanalytics-activity-6608609171401166848-U8Do)
+
 ### Graphs
   - [A number of interesting links on Graph Networks by Yaz](https://github.com/yazdotai/graph-networks)
   - [Graph databases](./data/README.md#databases)

diff --git a/cloud-devops-infra/README.md b/cloud-devops-infra/README.md
@@ -85,6 +85,10 @@
   - [GPU Server 1 of 2](./gpus/GPU-Server-side-1-of-2.jpg) | [GPU Server 2 of 2](./gpus/GPU-Server-side-2-of-2.jpg) | [Applications of GPU servers](./gpus/Applications-of-GPU-Server.jpg) - [checkout the manufacturers](http://manli.com/en/)
   - [Embedded Vision Solutions for NVIDIA Jetson Series](https://www.avermedia.com/professional/category/nvidia_jetson_solutions) | [Embedded Vision Family Brochure](http://storage.avermedia.com/web_release_www/Solutions/Embedded_Vision_Solutions_brochure_20190429.pdf)
   - Avermedia Box PC & Carrier (works with NVIDIA Jetson): [1](./gpus/Avermedia-Box-PC-and-Carrier-1-of-2-works-with-NVidia-Jetson.jpg) | [2](./gpus/Avermedia-Box-PC-and-Carrier-2-of-2-works-with-NVidia-Jetson.jpg)
+  - [Accelerating Wide & Deep Recommender Inference on GPUs](https://www.linkedin.com/posts/miguelusque_accelerating-wide-deep-recommender-inference-activity-6614061742936870913-oG2v)
+  - [Create GPU Arrays and Move to DL Frameworks with DLPack](https://www.linkedin.com/posts/activity-6625024900585316352-PucI)
+  - [GPU Accelerated data viz tools](https://www.linkedin.com/posts/murraydata_data-todashboard-activity-6623659330199781376-YIUQ)
+  - [This tool is nice to monitor not only RAPIDS but also deep learning workloads](https://www.linkedin.com/posts/miguelusque_gpu-dashboards-in-jupyter-lab-activity-6611570222585401344-n1Qe)
   - See [NVIDIA's RAPIDS](./gpus/rapids.md)
 
 ## TPU
@@ -112,6 +116,7 @@
 ## IPU
 
   - [GraphCore](http://graphcore.ai) | Videos: [Simon Knowles - More complex models and more powerful machines](https://www.youtube.com/watch?v=dLvkF_TmyAc&feature=youtu.be) | [Graphcore tech Concept](https://youtu.be/cSXbhEsUUGo?t=916) | [A new kind of hardware designed for machine intelligence - GraphCore Presentations](http://www.bristol.bcs.org.uk/2017/graphcore.pdf) | [V‍ID‌EO‌‍: SCA‌LING‌‍ THRO‌UG‍HP‌‍U‌T P‍R‌O‍C‍ESSO‌‍RS FO‌‍R‍ MAC‌HINE INTELLIG‌ENC‌‍E](https://www.graphcore.ai/posts/video-scaling-throughput-processors-for-machine-intelligence)
+  - [What makes the IPU's architecture so efficient](https://www.linkedin.com/posts/graphcore_if-youd-like-to-know-what-makes-the-ipus-activity-6617716840384778240-PUS0)
 
 ## Performance
 

diff --git a/competitions.md b/competitions.md
@@ -33,6 +33,8 @@
 - [KDD Data mining and Knowledge Discovery cup](http://www.kdd.org/kdd-cup)
 - [VizDoom AI competition](http://vizdoom.cs.put.edu.pl/competition-cig-2017) [deadlink]
 - [Numerai](https://numer.ai/) - data science tournaments
+- [10 Data Science Competitions for you to hone your skills for 2020](https://towardsdatascience.com/10-data-science-competitions-for-you-to-hone-your-skills-for-2020-32d87ee19cc9)
+- [Kaggle Kernels Guide for Beginners — Step by Step Tutorial](https://towardsdatascience.com/kaggle-kernels-for-beginners-a-step-by-step-guide-3db6b1cd7606)
 
 ## Coding challenges
 

diff --git a/data/README.md b/data/README.md
@@ -18,6 +18,7 @@ The question to ask ourselves: _Do we know our data...?_
 - [Feature Selection](./README.md#feature-selection)
 - [Feature Engineering](./README.md#feature-engineering)
 - [Post model-creation analysis, ML interpretation/explainability](./README.md#post-model-creation-analysis-ml-interpretationexplainability)
+- [Model deployment](./README.md#model-deployment)
 - [Statistics](./README.md#statistics)
 - [Visualisation](./README.md#visualisation)
 - [Common mistakes when training models (data related)](./README.md#common-mistakes-when-training-models-data-related)
@@ -81,6 +82,14 @@ See [Feature engineering](./feature-engineering.md)
 
 See [Post model-creation analysis, ML interpretation/explainability](./model-analysis-interpretation-explainability.md)
 
+## Model deployment
+
+- [Model Deployment Methods and Techniques - Part 1](https://lnkd.in/ghaTe_d)
+- [Model Deployment Methods and Techniques - Part 2](https://lnkd.in/gk3cpzH)
+- [Model Deployment Methods and Techniques - Part 3](https://lnkd.in/gV_cQJ2)
+- [Model Deployment Methods and Techniques - Part 4](https://lnkd.in/g5zCV6w)
+- [Model Deployment Methods and Techniques - Part 5](https://www.linkedin.com/posts/srivatsan-srinivasan-b8131b_datascience-ml-machinelearning-activity-6615447096793407488-sN1y)
+
 ## Statistics
 
 See [Statistics.md](statistics.md#statistics)

diff --git a/data/data-exploratory-analysis.md b/data/data-exploratory-analysis.md
@@ -31,6 +31,7 @@ aka *_Exploratory Data Analysis_*
 - [Associations and Correlations - The Essential Elements](https://www.linkedin.com/posts/data-science-central_associations-and-correlations-the-essential-activity-6609987641754570752-61fE)
 - [13 Great Articles and Tutorials about Correlation](https://www.linkedin.com/posts/data-science-central_13-great-articles-and-tutorials-about-correlation-activity-6622173938812280832-Fa4a)
 - [Testing for Normality using Skewness and Kurtosis](https://www.linkedin.com/posts/ashishpatel2604_artificialintelligence-deeplearning-datascience-activity-6603851612719026176-zx0u)
+- [Variable Reduction: An art as well as Science](https://www.linkedin.com/posts/data-science-central_variable-reduction-an-art-as-well-as-science-activity-6607678425375342592-xrSp)
 
 ### Outliers
 
@@ -50,6 +51,11 @@ aka *_Exploratory Data Analysis_*
 - [P-Value Explained in One Picture](https://www.linkedin.com/posts/data-science-central_p-value-explained-in-one-picture-activity-6617794081072443392-BG7_)
 - [p-value and level of significance explained](https://www.linkedin.com/posts/data-science-central_p-value-and-level-of-significance-explained-activity-6622189036842864640-lR81)
 
+### Regression Analysis
+
+- [Intro to Full Regression Analysis](https://www.youtube.com/watch?v=W4w1XX4fCu0&feature=share&fbclid=IwAR0nJRz8v4MZhTa8AL-1XlIit7neb_vfD0JzdhKooLKD4pX-U9kl6bB6Hro)
+- [Regression Analysis](https://bit.ly/2JzcOcb)
+
 # Contributing
 
 Contributions are very welcome, please share back with the wider community (and get credited for it)!

diff --git a/data/feature-engineering.md b/data/feature-engineering.md
@@ -12,9 +12,11 @@
 - [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)
 - [Feature Engineering and Feature Selection](https://media.licdn.com/dms/document/C511FAQF45u2wk4WYKQ/feedshare-document-pdf-analyzed/0?e=1570834800&v=beta&t=lNVqtm3JJYvvPHpsl0uc6mZJjVGWgJ8Toz29tNJA4GI) [deadlink]
+- [Hands-on Guide to Automated Feature Engineering - Prateek Joshi](https://www.linkedin.com/posts/vipulppatel_hands-on-guide-to-automated-feature-engineering-ugcPost-6612564773705924608-Utyb)
+- [Feature Engineering and Selection](https://www.linkedin.com/posts/nabihbawazir_feature-engineering-and-selection-ugcPost-6603534412548280320-XTIX)
+- [What is feature engineering and why do we need it?](https://www.linkedin.com/posts/srivatsan-srinivasan-b8131b_datascience-machinelearning-ml-activity-6623556433189363712-O7c4)
 - [ML topics expanded by Chris Albon](https://chrisalbon.com/#machine_learning) - look for topics: Feature Engineering • Feature Selection
 
-
 # Contributing
 
 Contributions are very welcome, please share back with the wider community (and get credited for it)!

diff --git a/data/feature-selection.md b/data/feature-selection.md
@@ -4,7 +4,7 @@
 - Forward Feature selection: [Blog on Towards DS](https://towardsdatascience.com/feature-importance-and-forward-feature-selection-752638849962) | [Scikit learn](https://mikulskibartosz.name/forward-feature-selection-in-scikit-learn-f6476e474ddd)
 - [What is dimensionality reduction? What is the difference between feature selection and extraction?](https://datascience.stackexchange.com/questions/130/what-is-dimensionality-reduction-what-is-the-difference-between-feature-selecti)
 - [Feature Engineering and Feature Selection](https://media.licdn.com/dms/document/C511FAQF45u2wk4WYKQ/feedshare-document-pdf-analyzed/0?e=1570834800&v=beta&t=lNVqtm3JJYvvPHpsl0uc6mZJjVGWgJ8Toz29tNJA4GI) [deadlink]
-
+- [Feature Selection Techniques in Machine Learning with Python - Raheel Shaikh](https://www.linkedin.com/posts/vipulppatel_feature-selection-techniques-in-ml-with-python-ugcPost-6603482535081062400-3ZH9)
 
 # Contributing
 

diff --git a/data/model-analysis-interpretation-explainability.md b/data/model-analysis-interpretation-explainability.md
@@ -38,6 +38,20 @@
 - [Machine Learning Project End to End with Python Code (data science focussed)](https://www.youtube.com/watch?v=ekV9QO5KHUY&list=PLcQCwsZDEzFkP9WMe6xvLrd_ZNGqoXOQY&fbclid=IwAR1z7XBl762FLyo-gVvdBDU1iCVqz89K1yfmJS1cbC4rZyEfF-jO30ZsYeY)
 - [Machine learning model explainability through Shapley values](https://faculty.ai/blog/machine-learning-model-explainability-through-shapley-values/) by [Christiane Ahlheim](https://www.linkedin.com/in/christiane-ahlheim-498263b2/) & [Markus Kunesch](https://www.linkedin.com/in/markus-kunesch/)
 - [Research on AI Safety](https://faculty.ai/research/) by [faculty.ai](https://faculty.ai)
+- [Explaining the Explainer: A First Theoretical Analysis of LIME](https://www.linkedin.com/posts/montrealai_artificialintelligence-deeplearning-machinelearning-activity-6622678147433316352-iu72)
+- [Explainable AI: The Royal Society](https://www.linkedin.com/posts/nabihbawazir_explainable-ai-the-royal-society-activity-6610121083649695744-SXrL)
+- [Important Model Evaluation Metrics for Machine Learning Everyone should know - Tavish Srivastava](https://www.linkedin.com/posts/vipulppatel_important-model-evaluation-metrics-everyone-ugcPost-6607395953429266432-cDiV)
+- [Read Pavel Pscheidl’s latest blog for a step by step on how to import, inspect, and score with MOJO models inside #H2O](https://lnkd.in/gbNtfMn) | [LinkedIn](https://www.linkedin.com/posts/pavel-pscheidl-19b15990_h2o-ai-ml-activity-6606566698516656128-Bk93)
+
+## Calibration
+
+- [Probability Calibration by Cambridge Spark](https://blog.cambridgespark.com/probability-calibration-c7252ac123f)
+- [We can build ensemble Models with Xgboosted trees. Individual trees are stacked for better predictions but they are prone to overfitting. These trees are difficult to interprete and may be inexplicable - Kevin Lemagnen](https://github.com/klemag/odsc2018-ensemble-demystified)
+
+## Overfitting
+
+- [A Quick Refresher on Overfitting and its removal Techniques](https://www.linkedin.com/posts/ashutoshtripathi1_what-is-overfitting-and-its-removal-techniques-ugcPost-6605478376671150080-fsVI)
+- [We can build ensemble Models with Xgboosted trees. Individual trees are stacked for better predictions but they are prone to overfitting. These trees are difficult to interprete and may be inexplicable - Kevin Lemagnen](https://github.com/klemag/odsc2018-ensemble-demystified)
 
 # Contributing
 

diff --git a/details/java-jvm.md b/details/java-jvm.md
@@ -131,6 +131,14 @@ MLPMNist using DL4J: [![MLPMNist using DL4J](https://img.shields.io/docker/pulls
 - See [Cloud/DevOps/Infra > Performance](../cloud-devops-infra/README.md#performance) - to find various ML performance benchmarking suites
 - Also see [Post model-creation analysis, ML interpretation/explainability](../data/README.md#post-model-creation-analysis-ml-interpretationexplainability)
 
+### Supervised
+
+  - [⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐](https://www.linkedin.com/posts/asif-bhat_cheat-sheet-supervised-and-unsupervised-ugcPost-6606216862718099457-3VrA)
+
+### Unsupervised
+
+  - [⭐ CHEAT SHEET : Supervised and Unsupervised Learning ⭐](https://www.linkedin.com/posts/asif-bhat_cheat-sheet-supervised-and-unsupervised-ugcPost-6606216862718099457-3VrA)
+
 ### Deep learning
 
   - [Deep Learning Theory](https://github.com/virgili0/Virgilio/blob/master/serving/purgatorio/select-and-train-machine-learning-models/deep-learning-theory/deep-learning-theory.md)