Added links to various sections i.e Data, Cheatsheets, NLP, Maths & S…

…tats
jd-13 · Nov 21, 2019 · 14fb9db · 14fb9db
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diff --git a/courses.md b/courses.md
@@ -106,6 +106,8 @@
 ### Deep Learning
 
 - [Code examples for the Stanford's course: TensorFlow for Deep Learning Research](https://github.com/chiphuyen/stanford-tensorflow-tutorials)
+- [Deep Learning course by Andrew Ng](https://media.licdn.com/dms/document/C4D1FAQHQjNkeFxn1bg/feedshare-document-pdf-analyzed/0?e=1573005600&v=beta&t=ED5ZdXVRQvpgSovpRO7sFGDWzk2edpphmnHqQLB_AXQ)
+
 
 #### Reinforcement Learning
 

diff --git a/data/README.md b/data/README.md
@@ -123,6 +123,7 @@ See [Databases](./databases.md)
 - [A Rubric for ML Production Readiness - Breck et al. 2017 by Jiameng Gao (28 rules to follow, suggested by Google)](https://docs.google.com/presentation/d/1-4gE9v1X7EP4rsBQlRtGA9IXDnBjlQPAqB3jlDBvUTU/edit#slide=id.p) | [Original Paper by Google](https://ai.google/research/pubs/pub46555)
 - [Understanding Data Science Problems - template of questions to ask](http://url4149.bitgrit.net/wf/click?upn=qJT0wq97YSVxi6S9Gi10QGqeT3JSC6xJnYDSgYEwjzRMycP3yLSx2r-2BNxQzJHe9QPJFpU2-2FggIOmAMx4-2FXJyS5Ct5nq0JGa-2BaeTR278cf4Y016UI8tNe1mgRL66MJsyWyvn6y4MQGXNy5SqWqhbPcw-3D-3D_sX8FRvZaj8ntSB52F-2FOI3mORNoWV2VSsIMLOasSO2VX6r5g4xczJm1Y1-2FwGOMI-2BlSq1KNsGohBLZURHm6k60Tf2HtckfAZ6grcZUQF65S5oJU988M9Tw34CKxkXDto40DimsP-2FidGRva8-2F1aqLSRqIqousS4hXEet-2FT5ghzTXSqhZy5rNdfAdgpvrkvvm-2BZIs0VBaYDiakrHtCwc5eIKRA-3D-3D)
 - [eBook: How to Succeed in Data Science](https://docs.google.com/document/d/1fvxDOdCjPx0wS4aqSOME3NyATJGN7sASLeEyygIvcJA/edit#) [deadlink]
+- [Data Fallacies by Nabih Bawazir](https://www.linkedin.com/posts/nabihbawazir_data-fallacies-to-avoid-ugcPost-6600634052464742400-fmlf)
 
 ## Credits
 

diff --git a/data/about-H2O-Driverless-AI.md b/data/about-H2O-Driverless-AI.md
@@ -11,7 +11,7 @@
         - https://www.youtube.com/results?search_query=h2o+driverless
         - https://www.youtube.com/watch?v=yzAhjinmdzk
         - https://www.youtube.com/watch?v=axIqeaUhow0
-
+    - [H2O meetups](https://github.com/h2oai/h2o-meetups)
 ---
 
 - [ ] [AI/ML/DL Library / Package / Framework: applicable]

diff --git a/data/data-exploratory-analysis.md b/data/data-exploratory-analysis.md
@@ -10,6 +10,8 @@ aka *_Exploratory Data Analysis_*
 - [Exploring and Transforming H2O DataFrame in R and Python](https://dzone.com/articles/exploring-amp-transforming-h2o-data-frame-in-r-and)
 - [ML with H2O by Sudalai Rajkumar](https://www.slideshare.net/0xdata/machine-learning-with-h2o-114163519) (slide 20 onwards)
 - [How to Use Statistics to Identify Outliers in Data](https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/)
+- [Outlier detection in Python](https://www.linkedin.com/posts/vipulppatel_outlier-detection-python-ugcPost-6598802093908209664-ElRG
+)
 - [Fundamentals of Data Visualization](https://serialmentor.com/dataviz/)
 - [Helpful Python Code Snippets for Data Exploration in Pandas](https://medium.com/@msalmon00/helpful-python-code-snippets-for-data-exploration-in-pandas-b7c5aed5ecb9)
 - [5 Steps to correctly prepare your data for your machine learning model](https://towardsdatascience.com/5-steps-to-correctly-prep-your-data-for-your-machine-learning-model-c06c24762b73?gi=6b4a6895ab1)

diff --git a/data/data-preparation.md b/data/data-preparation.md
@@ -9,13 +9,17 @@
 - [Journal of Statistical Software - TidyData](https://www.jstatsoft.org/article/view/v059i10/)
 - [5 Steps to correctly prepare your data for your machine learning model](https://towardsdatascience.com/5-steps-to-correctly-prep-your-data-for-your-machine-learning-model-c06c24762b73?gi=6b4a6895ab1)
 - [Introduction to Data Analysis and Cleaning presentation](../presentations/data/01-mam-ml-study-group-meetup/Introduction_to_Data_Analysis_and_Cleaning.pdf) by [Mark Bell](http://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/)
+- [Intro to Data Cleaning](https://media.licdn.com/dms/document/C561FAQFELM5YZM3-Rw/feedshare-document-pdf-analyzed/0?e=1572184800&v=beta&t=9HybdEtMdRqdqA6bJQeN-0sMDrEU8yBZwAvTUTm6dTo)
+
 
 ## Data preprocessing / Data wrangling / Data manipulation
 
 - [Data Preprocessing vs. Data Wrangling in Machine Learning Projects](https://www.infoq.com/articles/ml-data-processing)
 - [Improve Model Accuracy with Data Pre-Processing](https://machinelearningmastery.com/improve-model-accuracy-with-data-pre-processing/)
 - [5 Steps to correctly prepare your data for your machine learning model](https://towardsdatascience.com/5-steps-to-correctly-prep-your-data-for-your-machine-learning-model-c06c24762b73?gi=6b4a6895ab1)
 - [Introduction to Data Analysis and Cleaning presentation](../presentations/data/01-mam-ml-study-group-meetup/Introduction_to_Data_Analysis_and_Cleaning.pdf) by [Mark Bell](http://www.nationalarchives.gov.uk/about/our-research-and-academic-collaboration/our-research-and-people/staff-profiles/mark-bell/)
+- [Data Engineering Cook book](https://media.licdn.com/dms/document/C561FAQHLSPhPny5ENg/feedshare-document-pdf-analyzed/0?e=1572793200&v=beta&t=AW9YeWaxF4PCcSxN64Lin_BIlbumyHLHnL0gF-jLNto)
+- [Waimak: Open Source framework to create complex data flows in Apache Spark](https://github.com/CoxAutomotiveDataSolutions/waimak)
 - [Pandas](https://lnkd.in/gxSgfuQ)
 - [SQLAlchemy](https://lnkd.in/gjvbm7h)
 

diff --git a/details/articles-papers-code-data-courses.md b/details/articles-papers-code-data-courses.md
@@ -9,6 +9,11 @@
   - [Browse state-of-the-art](https://paperswithcode.com/sota)
   - [ML/DL/Data Science resources (scattered across the page)](https://github.com/ayonroy2000/100DaysOfML_TelegramGroup/blob/master/Resources.md)
   - [Papers by Google X](../papers/google-x/README.md#papers-by-members-of-google-and-google-x-aka-x-team)
+  - [Blogs by Neal Lathia](https://nlathia.github.io/)
+  - [Blogs by Weights & Biases](https://www.wandb.com/articles)
+  - Cambridge Spark: [tutorials](https://cambridgespark.com/tutorials/) | [blogs](https://blog.cambridgespark.com/) | [Medium](https://medium.com/@Cambridge_Spark)
+  - Analytics Vidhya: [blogs](https://www.analyticsvidhya.com/blog/) | [Archive](https://www.analyticsvidhya.com/blog-archive/) | [Learning Paths](https://www.analyticsvidhya.com/learning-paths-data-science-business-analytics-business-intelligence-big-data/)
+  - [Blogs by Lavanya Shukla](https://lavanya.ai/)
   - See [courses](../courses.md)
 
 # Contributing

diff --git a/details/cheatsheets.md b/details/cheatsheets.md
@@ -19,6 +19,9 @@
   - [Algorithms related cheatsheets - Big-O cheatsheet](http://bigocheatsheet.com/)
   - [ML Cheatsheet by Nabih Ibrahim Bawazir](https://media.licdn.com/dms/document/C511FAQGFXvKuRmt5Rg/feedshare-document-pdf-analyzed/0?e=1569596400&v=beta&t=n4ZRWnYpc1KOTo8lZ2sKmOI7rRChPkApOQEQBBDhaXg) [deadlink]
   - [Statistics cheatsheet by Nabih Ibrahim Bawazir](https://media.licdn.com/dms/document/C511FAQF31AWGmSTzMQ/feedshare-document-pdf-analyzed/0?e=1573030800&v=beta&t=11ugKu44wK--uA9WG98V_r6_LY_xu6I8Y-YSaM1BOsQ)
+  | [Stats Cheatsheet](https://www.linkedin.com/posts/nabihbawazir_statistics-cheatsheet-ugcPost-6600946387649695744-I-Kk)
+
+  - [Data Science Cheatsheet - DataCamp](https://media.licdn.com/dms/document/C561FAQE-ZaQS7uMQMA/feedshare-document-pdf-analyzed/0?e=1572357600&v=beta&t=xPh84UiqmQRCf9PMs8tt1vCtM3eQln8vPkLqBSWssEw)
 
 # Contributing
 

diff --git a/details/maths-stats-probability.md b/details/maths-stats-probability.md
@@ -50,6 +50,8 @@
 - [Probability Learning II: How Bayes’ Theorem is applied in Machine Learning](https://towardsdatascience.com/probability-learning-ii-how-bayes-theorem-is-applied-in-machine-learning-bd747a960962)
 - [Learning & Reasoning in Artificial Intelligence](https://www.youtube.com/watch?v=K_GOHepjY2o) by [Thomas Lukasiewicz](http://www.cs.ox.ac.uk/thomas.lukasiewicz/)
 - Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED)
+- [Bayesian Framework](https://ekroc.weebly.com/uploads/2/1/6/3/21633182/bayesworkshop1.pdf)
+- [Implementing the Bayesian Framework](https://ekroc.weebly.com/uploads/2/1/6/3/21633182/bayesworkshop2.pdf)
 - [Data Science](../courses.md#data-science) in [Courses](../courses.md#courses)
 
 # Contributing

diff --git a/natural-language-processing/general.md b/natural-language-processing/general.md
@@ -8,7 +8,7 @@
 - [OpenAI let us try its state-of-the-art NLP text generator](https://venturebeat.com/2019/02/14/openai-let-us-generate-text-with-an-ai-model-that-achieves-state-of-the-art-performance-in-several-nlp-tasks/)
 - [LaMachine](https://proycon.github.io/LaMachine/) - LaMachine is a unified software distribution for Natural Language Processing. Integration of numerous open-source NLP tools, programming libraries, web-services and web-applications in a single Virtual Research Environment that can be installed on a wide variety of machines (bare-metal, Virtual Machines and Docker containers).
 - [Awesome NLP](https://github.com/keon/awesome-nlp)
-
+- [Facebook's LASER](https://github.com/facebookresearch/LASER)
 
 # Contributing