Multiple sections: updating sections covering programming in Python, …

…Cloud, JavaScript, ML, DL, RL and ML algorithms
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diff --git a/Programming-in-Python.md b/Programming-in-Python.md
@@ -48,6 +48,8 @@
 - [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)
+- [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)
 
 ## Courses
 

diff --git a/README-details.md b/README-details.md
@@ -30,6 +30,8 @@
 ### Artificial Intelligence
 
 - [Demystification of the key concepts of Artificial Intelligence and Machine Learning](https://github.com/virgili0/Virgilio/blob/master/serving/paradiso/demystification-ai-ml-dl/demystification-ai-ml-dl.md)
+- [12 thought leaders on LinkedIn who are creating original content to learn Artificial Intelligence and Machine Learning](https://www.linkedin.com/posts/ajitjaokar_12-thought-leaders-on-linkedin-who-are-creating-activity-6627455043429814272-MK_n)
+- [AI Repository by Goku Mohandas](https://www.linkedin.com/posts/asif-bhat_datascience-data-dataanalysis-activity-6643083915873615872-je6g)
 - See [Artificial Intelligence](./details/artificial-intelligence.md)
 
 ### Automation
@@ -44,6 +46,7 @@
 - [Data science competitions to build a better world](https://www.drivendata.org/)
 - [An ethics checklist for data scientists](http://deon.drivendata.org/)
   - [python package: deon](https://pypi.org/project/deon/)
+- [👉A Practical guide to Responsible Artificial Intelligence (AI) by PwC 👈](https://www.linkedin.com/posts/asif-bhat_ai-activity-6642499836232986624-FCNj)
 - [Data ethics literacy cards](https://public.zenkit.com/i/2RH604FcHf/iK2Z5J9kr/do-you-know-your-data?v=1wyJ56INd&hide=workspaceLists) by [Anisha Fernando](https://twitter.com/techgirl_9) | [Join the Slack community](https://dataethicsinpractice.slack.com/join/shared_invite/enQtODAwMDIxMzU3MTUyLTg5YTM3ZTIyYTg3ZTU0N2M4MDhmYjE3YmRlYTUyNTA5ZWE1MTRkYmJjYTNlM2YzYTk1YWNhZmY1N2RlYzg5Y2U) | [The Private Lives of Data: YouTube video](https://www.youtube.com/watch?v=pXtQj2UNMYE)
 
 ### Java

diff --git a/cloud-devops-infra/README.md b/cloud-devops-infra/README.md
@@ -57,6 +57,15 @@ reproducible research
   - [Nextflow](https://www.nextflow.io/) - Data-driven computational pipelines. Nextflow enables scalable and reproducible scientific workflows using software containers. It allows the adaptation of pipelines written in the most common scripting languages.
   - [StackHPC suites of repositories: AI, ML, DL, Cloud, HPC](https://github.com/stackhpc) | [StackHPC](http://stackhpc.com/)
   - [cortex](https://www.cortex.dev/) - Machine learning deployment platform: Deploy machine learning models to production
+  - [#Uber introduces #Fiber, an #AI development and distributed training platform for methods including reinforcement learning and population-based learning.](https://www.linkedin.com/posts/inna-vogel-nlp_introduction-to-fiber-activity-6649564159828606976-Jmi6) 
+  - [H2O Framework for Machine Learning](https://www.linkedin.com/posts/data-science-central_h2o-framework-for-machine-learning-activity-6635175109445382144-ISgt)
+  - [ML Framework: Introducing Ludwig, a Code-Free Deep Learning Toolbox](https://eng.uber.com/introducing-ludwig/)
+  - [ML Pipelines](https://www.linkedin.com/posts/artificialintelligenceconsultant_activity-6639429321943539712-vRug)
+  - [ML Framework by Abhishek Thakur](https://www.linkedin.com/posts/abhi1thakur_machinelearning-python-datascience-activity-6644289209181978625-wZtJ)
+    - [Episode 1 Intro and building a machine learning framework](https://lnkd.in/e5Syy5N)
+    - [Episode 2 A Cross Validation Framework](https://lnkd.in/eDjFTGW)
+    - [Episode 3 Handling Categorical Features in Machine Learning Problems](https://lnkd.in/e9Qc5fe)
+    - [Episode 4 Simple and Basic Binary Classification Metrics](https://lnkd.in/eGZtcPW)
   - [See also: Data > Programs and Tools](../data/programs-and-tools.md#programs-and-tools)
 
 ## CPU

diff --git a/details/javascript.md b/details/javascript.md
@@ -6,6 +6,7 @@
    - [Bring deep learning into the browser &mdash; In Browser AI](https://inbrowser.ai/)
    - [Run Keras models in the browser, with GPU support using WebGL](https://github.com/transcranial/keras-js)
    - [Tensorflow.js: bringing ML into the browser](https://skillsmatter.com/skillscasts/11876-tensorflow-js-bringing-ml-into-the-browser) [deadlink]
+   - [Machine Learning in Javascript- A compilation of Resources](https://twitter.com/DataScienceCtrl/status/1233099732672753665)
    - See [this link](https://github.com/josephmisiti/awesome-machine-learning#javascript) for more JavaScript related ML links