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https://github.com/tirthajyoti/Data-science-best-resources

Carefully curated resource links for data science in one place
https://github.com/tirthajyoti/Data-science-best-resources

analytics api artificial-intelligence aws cheatsheet data-science data-wrangling database deep-learning linux machine-learning neural-network online-course python r reinforcement-learning scikit-learn sql statistics visualization

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Carefully curated resource links for data science in one place

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README 

        
![tdsp](https://raw.githubusercontent.com/tirthajyoti/Data-science-best-resources/master/images/tdsp-lifecycle2.png)



# Data Science Collected Resources

A trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place.



### Please feel free to [connect with me here on LinkedIn](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/) if you are interested in data science and would like to connect.



### Please visit my [Medium profile](https://medium.com/@tirthajyoti) to check out all of my data science articles.



### Please check this [Github Repo for all my Tutorial-style Machine Learning Jupyter notebooks](https://github.com/tirthajyoti/Machine-Learning-with-Python) 



---



## Artificial Intelligence related



[MONTRÉAL.AI ACADEMY: ARTIFICIAL INTELLIGENCE 101 FIRST WORLD-CLASS OVERVIEW OF AI FOR ALL](http://www.montreal.ai/ai4all.pdf)



 [OpenAI blog](https://blog.openai.com/)



[AI thinks like a corporation—and that’s worrying - Open Voices](https://www.economist.com/open-future/2018/11/26/ai-thinks-like-a-corporation-and-thats-worrying)



 [AITopics](https://aitopics.org/search)



 [Does the Brain Store Information in Discrete or Analog Form?](https://medium.com/mit-technology-review/does-the-brain-store-information-in-discrete-or-analog-form-f0e169361c99)



 [Explainable Artificial Intelligence (Part 1) — The Importance of Human Interpretable Machine…](https://towardsdatascience.com/human-interpretable-machine-learning-part-1-the-need-and-importance-of-model-interpretation-2ed758f5f476)



 [Is The Singularity Coming? – Arc Digital](https://arcdigital.media/is-the-singularity-coming-ef8580d4ce97)



 [Michael I. Jordan NYSE Machine Learning Presentation](https://www.youtube.com/watch?time_continue=2&v=17cp8PLKvOc)



 [Some scientists fear superintelligent machines could pose a threat to humanity | The Washington Post](https://www.washingtonpost.com/sf/national/2015/12/27/aianxiety/?noredirect=on&utm_term=.c3ac6321c831)



 [The Four Waves of A.I. | LinkedIn](https://www.linkedin.com/pulse/four-waves-ai-kai-fu-lee/)



 [When algorithms go wrong we need power to fight back, say researchers - The Verge](https://www.theverge.com/2018/12/8/18131745/ai-now-algorithmic-accountability-2018-report-facebook-microsoft-google)



## AWS related



 [Amazon CloudWatch - Application and Infrastructure Monitoring](https://aws.amazon.com/cloudwatch/)



 [Amazon DynamoDB - Overview](https://aws.amazon.com/dynamodb/)



 [Amazon Elastic Block Store (EBS) - Amazon Web Services](https://aws.amazon.com/ebs/)



 [Amazon Elastic File System (EFS) | Cloud File Storage](https://aws.amazon.com/efs/)



 [AWS Concepts: Understanding AWS - YouTube](https://www.youtube.com/watch?v=qcY-uiEHhn0)



 [AWS Concepts: Understanding the Course Material & Features - YouTube](https://www.youtube.com/watch?v=LKStwibxbR0&list=PLv2a_5pNAko2Jl4Ks7V428ttvy-Fj4NKU)



 [AWS In 10 Minutes | AWS Tutorial For Beginners | AWS Training Video | AWS Tutorial | Simplilearn - YouTube](https://www.youtube.com/watch?v=r4YIdn2eTm4)



 [AWS re:Invent 2017: Building production apps easily with Amazon Lightsail (CMP212) - YouTube](https://www.youtube.com/watch?v=29_LqYnomdg)



 [Classless Inter-Domain Routing - Wikipedia](https://en.wikipedia.org/wiki/Classless_Inter-Domain_Routing)



 [Cloud Compute Products – Amazon Web Services (AWS)](https://aws.amazon.com/products/compute/)



 [Cloud Object Storage | Store & Retrieve Data Anywhere | Amazon Simple Storage Service](https://aws.amazon.com/s3/)



 [Elastic Load Balancing - Amazon Web Services](https://aws.amazon.com/elasticloadbalancing/)



 [Getting Spark, Python, and Jupyter Notebook running on Amazon EC2](https://medium.com/@josemarcialportilla/getting-spark-python-and-jupyter-notebook-running-on-amazon-ec2-dec599e1c297)



 [Use PuTTY to access EC2 Linux Instances via SSH from Windows](https://linuxacademy.com/howtoguides/posts/show/topic/17385-use-putty-to-access-ec2-linux-instances-via-ssh-from-windows)



 [What is Cloud Computing? - Amazon Web Services](https://aws.amazon.com/what-is-cloud-computing/)

 

## Blogs, StacksExchanges



[7-Step Guide to Become a Machine Learning Engineer in 2021](https://www.dezyre.com/article/7-step-guide-to-become-a-machine-learning-engineer-in-2021/409)



[Reducing the Need for Labeled Data in Generative Adversarial Networks](https://ai.googleblog.com/2019/03/reducing-need-for-labeled-data-in.html)



[Jason's Google ML 101 deck](https://docs.google.com/presentation/d/1kSuQyW5DTnkVaZEjGYCkfOxvzCqGEFzWBy4e9Uedd9k/edit)



 [10 Free Must-Read Books for Machine Learning and Data Science](https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html?utm_content=buffer5a67a&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)



 [Advice to aspiring data scientists: start a blog – Variance Explained](http://varianceexplained.org/r/start-blog/)



 [Brandon Roher Blog](https://brohrer.github.io/blog.html)



 [Chris Albon - Data Science, Machine Learning, and Artificial Intelligence](https://chrisalbon.com/#Python)



 [Data Science Stack Exchange](http://datascience.stackexchange.com/)



 [Data Skeptic](https://dataskeptic.com/)



 [DataTau](http://www.datatau.com/)

 

 [explained.ai - Deep explanations of machine learning and related topics](https://explained.ai/)



 [FlowingData](http://flowingdata.com/)



 [Here Are (Approximately) 3000 Free Data Sources You Can Use Right Now](https://www.forbes.com/sites/metabrown/2017/06/30/here-are-approximately-3000-free-sources-for-data-you-can-use-right-now/amp/?utm_content=bufferef401&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)



 [If you want to learn Data Science, take a few of these statistics classes](https://medium.freecodecamp.com/if-you-want-to-learn-data-science-take-a-few-of-these-statistics-classes-9bbabab098b9)



 [Learn Data Science - Infographic (article) - DataCamp](https://www.datacamp.com/community/tutorials/learn-data-science-infographic)



 [LIGO Gravity Wave GW150914_tutorial](https://losc.ligo.org/s/events/GW150914/GW150914_tutorial.html)



 [O.R. & Analytics Success Stories - INFORMS](https://www.informs.org/Impact/O.R.-Analytics-Success-Stories)



 [OpenAI Blog](https://blog.openai.com/)



 [Paul Ford: What Is Code? | Bloomberg](https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/)



 [Science Isn’t Broken | FiveThirtyEight](https://fivethirtyeight.com/features/science-isnt-broken/#part1)



 [Scientifically Sound](https://scientificallysound.org/)



 [AIspace](http://aispace.org/)



 [Top 28 Cheat Sheets for Machine Learning, Data Science, Probability, SQL & Big Data](https://www.analyticsvidhya.com/blog/2017/02/top-28-cheat-sheets-for-machine-learning-data-science-probability-sql-big-data/?utm_content=buffer9e308&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)



 [GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows](https://www.kdnuggets.com/2018/08/github-python-data-science-spotlight.html)



## Books, Courses, Repos



 [Solved end-to-end Data Science projects](https://www.dezyre.com/projects/data-science-projects)

 

 [Dive into Deep Learning (An interactive deep learning book with code, math, and discussions)](https://d2l.ai/index.html)



 [Machine Learning Math book](https://mml-book.github.io/)



 [Learn to code | Codecademy](https://www.codecademy.com/)



 [Lecture Notes | Introduction to MATLAB | Electrical Engineering and Computer Science | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-094-introduction-to-matlab-january-iap-2010/lecture-notes/)



 [60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more](http://www.kdnuggets.com/2015/09/free-data-science-books.html)

 

 [Feature Engineering and Selection: A Practical Approach for Predictive Models](http://www.feat.engineering/)

 

 [Nerual Networks and Deep Learning - an online book](neuralnetworksanddeeplearning.com)

 



## Git and Github



[Adding an existing project to GitHub using the command line - User Documentation](https://help.github.com/articles/adding-an-existing-project-to-github-using-the-command-line/)



 [An Intro to Git and GitHub for Beginners (Tutorial)](https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners)



 [Follow these simple rules and you’ll become a Git and GitHub master](https://medium.freecodecamp.org/follow-these-simple-rules-and-youll-become-a-git-and-github-master-e1045057468f)



 [Git - Book](https://git-scm.com/book/en/v2)



 [git - the simple guide - no deep shit!](http://rogerdudler.github.io/git-guide/)



 [How not to be afraid of GIT anymore – freeCodeCamp.org](https://medium.freecodecamp.org/how-not-to-be-afraid-of-git-anymore-fe1da7415286)



 [joshnh/Git-Commands: A list of commonly used Git commands](https://github.com/joshnh/Git-Commands)



 [The beginner’s guide to contributing to a GitHub project – Rob Allen's DevNotes](https://akrabat.com/the-beginners-guide-to-contributing-to-a-github-project/)



 [Understanding the GitHub Flow · GitHub Guides](https://guides.github.com/introduction/flow/)



## Interesting Articles

 

 [Towards an anti-fascist AI (from opendemocracy.net)](https://www.opendemocracy.net/en/digitaliberties/towards-anti-fascist-ai/)

 

 [Becoming a Level 3.0 Data Scientist](https://www.kdnuggets.com/2019/05/becoming-a-level-3-data-scientist.html)



 [The Third-wave of Data Scientist](https://towardsdatascience.com/the-third-wave-data-scientist-1421df7433c9)

 

 [46 Most Intellectually Stimulating Sites That Will Spark Your Inner Genius in 10 Minutes a Day](https://medium.com/swlh/in-less-than-10-minutes-a-day-these-46-intellectually-stimulating-sites-will-spark-your-inner-d96ee6fc8387)



 [Artificial Intelligence Learns to Learn Entirely on Its Own | Quanta Magazine](https://www.quantamagazine.org/artificial-intelligence-learns-to-learn-entirely-on-its-own-20171018/?utm_content=buffer578b7&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)



 [Edward Witten Ponders the Nature of Reality | Quanta Magazine](https://www.quantamagazine.org/edward-witten-ponders-the-nature-of-reality-20171128/)



   [Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department | Stitch Fix Technology – Multithreaded](https://multithreaded.stitchfix.com/blog/2016/03/16/engineers-shouldnt-write-etl/)

   

  [Foundations Built for a General Theory of Neural Networks - Quanta Magazine](https://www.quantamagazine.org/foundations-built-for-a-general-theory-of-neural-networks-20190131)

   

 [General Thinking Tools: 9 Mental Models to Solve Difficult Problems](https://www.fs.blog/general-thinking-tools/)



 [How Social Media Endangers Knowledge | WIRED](https://www.wired.com/story/wikipedias-fate-shows-how-the-web-endangers-knowledge/)



 [In These Small Cities, AI Advances Could Be Costly - MIT Technology Review](https://www.technologyreview.com/s/609076/in-these-small-cities-ai-advances-could-be-costly/?utm_campaign=Owned+Social&utm_source=Facebook&utm_medium=Owned+Social)



 [Machine Learning’s ‘Amazing’ Ability to Predict Chaos | Quanta Magazine](https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/)



 [New Brain Maps With Unmatched Detail May Change Neuroscience | WIRED](https://www.wired.com/story/new-brain-maps-with-unmatched-detail-may-change-neuroscience/)



 [Pedro Domingos on the Arms Race in Artificial Intelligence - SPIEGEL ONLINE](http://www.spiegel.de/international/world/pedro-domingos-on-the-arms-race-in-artificial-intelligence-a-1203132.html)



 [Quantum Leaps in Quantum Computing? - Scientific American](https://www.scientificamerican.com/article/quantum-leaps-in-quantum-computing/?utm_source=facebook&utm_medium=social&utm_campaign=sa-editorial-social&utm_content&utm_term=physics_sa-magazine_text_free)



 [The Fragile State of the Midwest’s Public Universities - The Atlantic](https://www.theatlantic.com/business/archive/2017/10/midwestern-public-research-universities-funding/542889/?utm_source=vxfb)



 [The Future of Human Work Is Imagination, Creativity, and Strategy](https://hbr.org/2018/01/the-future-of-human-work-is-imagination-creativity-and-strategy?utm_campaign=hbr&utm_source=linkedin&utm_medium=social)



 [The Quantum Thermodynamics Revolution | Quanta Magazine](https://www.quantamagazine.org/the-quantum-thermodynamics-revolution-20170502?utm_content=buffere2607&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)



 [What Is Code? | Paul Ford| Bloomberg](https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/)



 [The Economics Of Artificial Intelligence - How Cheaper Predictions Will Change The World](https://www.forbes.com/sites/bernardmarr/2018/07/10/the-economics-of-artificial-intelligence-how-cheaper-predictions-will-change-the-world/#5b3b146f5a0d)



 [OpenAI’s Dota 2 defeat is still a win for artificial intelligence  - The Verge](https://www.theverge.com/2018/8/28/17787610/openai-dota-2-bots-ai-lost-international-reinforcement-learning)



 [Machine Learning Confronts the Elephant in the Room | Quanta Magazine](https://www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/)



## MOOC related



[Complete lecture notes of the Stanford/Coursera Machine Learning class by Andrew Ng](http://www.holehouse.org/mlclass/)



[200 universities just launched 560 free online courses. Here’s the full list.](https://medium.freecodecamp.org/200-universities-just-launched-560-free-online-courses-heres-the-full-list-d9dd13600b04)



 [Artificial Intelligence | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/index.htm)



 [Dashboard | MIT Professional Education Digital Programs](https://mitprofessionalx.mit.edu/dashboard)



 [Data Science A-Z™: Real-Life Data Science Exercises Included | Udemy](https://www.udemy.com/datascience/)



 [Data Science Essentials | edX](https://www.edx.org/course/data-science-essentials-microsoft-dat203-1x-2?source=aw&awc=6798_1489913955_d9818a031ea60b9e133f81baa8e0fcbb&utm_source=aw&utm_medium=affiliate_partner&utm_content=text-link&utm_term=315645_LearnDataSci)



 [How to choose effective MOOCs for machine learning and data science?](https://medium.com/@tirthajyoti/how-to-choose-effective-moocs-for-machine-learning-and-data-science-8681700ed83f)



 [I uncovered 1,150+ Coursera courses that are still completely free](https://medium.freecodecamp.org/coursera-free-online-courses-6d84cdb30da)



 [Information and Entropy | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm)



 [Introduction to Algorithms | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/index.htm)



 [Introduction to Data Analysis using Excel | edX](https://www.edx.org/course/introduction-data-analysis-using-excel-microsoft-dat205x-0)



 [Introduction to Python for Data Science | edX](https://www.edx.org/course/introduction-python-data-science-microsoft-dat208x-4?source=aw&awc=6798_1489913492_c663da04f25e4339087686b358457f93&utm_source=aw&utm_medium=affiliate_partner&utm_content=text-link&utm_term=315645_LearnDataSci#!)



 [Introduction to R for Data Science | edX](https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-3)



 [Mathematics for Computer Science | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/index.htm)



 [Programming with Python for Data Science!](https://courses.edx.org/courses/course-v1:Microsoft+DAT210x+2T2017/info)



 [Statistical Thinking for Data Science course](https://courses.edx.org/courses/course-v1:ColumbiaX+DS101X+1T2016/courseware/83b2b74597c44d858f1cd81edef2faf2/9ba7caa9efaf4b86b7521534f9c841d5/)



 [Top Data Science Online Courses in 2017 – LearnDataSci](http://www.learndatasci.com/best-data-science-online-courses/)



 [U. Wash ML course Jupyter Home](https://hub.coursera-notebooks.org/user/hwxlouxsysrhhnfzzinqjs/tree)



## SQL



 [A Visual Explanation of SQL Joins](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/)



 [Join (SQL) - Wikipedia](https://en.wikipedia.org/wiki/Join_(SQL))



 [PostgreSQL: Mathematical Functions and Operators](https://www.postgresql.org/docs/9.5/static/functions-math.html)



 [PostgreSQL: String Functions and Operators](https://www.postgresql.org/docs/9.5/static/functions-string.html)



 [Psycopg2 Tutorial - PostgreSQL with Python](https://wiki.postgresql.org/wiki/Psycopg2_Tutorial)



 [SQL Joins Explained](http://www.sql-join.com/)



 [The SQL Tutorial for Data Analysis | SQL Tutorial - Mode Analytics](https://community.modeanalytics.com/sql/tutorial/introduction-to-sql/)

 

  [SQL vs NoSQL or MySQL vs MongoDB - YouTube](https://www.youtube.com/watch?v=ZS_kXvOeQ5Y)



 [Thinking in SQL vs Thinking in Python](https://blog.modeanalytics.com/learning-python-sql/)

 

 [Kaggle SQL course (including BigQuery topics)](https://www.kaggle.com/learn/sql)

 

 ## Statistics

 

 [Common statistical tests are linear models (or: how to teach stats)](https://lindeloev.github.io/tests-as-linear/)

 

 [Introductory statistics - OpenText Library](https://saylordotorg.github.io/text_introductory-statistics/index.html)



 [Common statistical tests are linear models (or: how to teach stats)](https://lindeloev.github.io/tests-as-linear/)

 

 [Background: Markov chains](https://d18ky98rnyall9.cloudfront.net/_adadc80290e52a99b282ca9d7c1a41ee_background_MarkovChains.html)



 [OpenIntro Stats](https://www.openintro.org/index.php)



 [Regression Analysis Tutorial and Examples | Minitab](http://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-tutorial-and-examples)



 [The 10 Statistical Techniques Data Scientists Need to Master](https://towardsdatascience.com/the-10-statistical-techniques-data-scientists-need-to-master-1ef6dbd531f7)



 [The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes)](https://medium.com/analytics-vidhya/the-ultimate-guide-to-12-dimensionality-reduction-techniques-with-python-codes-2c2afdbc09e3)



 [Thomas Bayes and the crisis in science – TheTLS](https://www.the-tls.co.uk/articles/public/thomas-bayes-science-crisis/)



 [Welcome to STAT 505! | STAT 505](https://onlinecourses.science.psu.edu/stat505/node/1)



 [Introduction to Bayesian Linear Regression – Towards Data Science](https://towardsdatascience.com/introduction-to-bayesian-linear-regression-e66e60791ea7)



 [Regression Analysis Tutorial and Examples | Minitab](http://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-tutorial-and-examples)



 [The 10 Statistical Techniques Data Scientists Need to Master](https://towardsdatascience.com/the-10-statistical-techniques-data-scientists-need-to-master-1ef6dbd531f7)



 [Welcome to STAT 505! | STAT 505](https://onlinecourses.science.psu.edu/stat505/node/1)

 

 [Probability and Statistics Visually](https://seeing-theory.brown.edu)



## Visualizations (and image processing related)



[The paper describing Scikit-image from its core developers](https://peerj.com/articles/453/)



[Full-screen interactive that lets you explore the first 300 years of Data Visualization](https://infowetrust.com/scroll/)



[designing-great-visualizations.pdf](https://www.tableau.com/sites/default/files/media/designing-great-visualizations.pdf)



[Gallery of Data Visualization - Missed Opportunities and Graphical Failures](http://www.datavis.ca/gallery/missed.php)



 [Lesson 1-4, first visualization data - Govind Acharya | Tableau Public](https://public.tableau.com/profile/govind.acharya#!/vizhome/Lesson1-4firstvisualizationdata/Sheet1)



 [Mapping the 1854 Cholera Outbreak | Tableau Public](https://public.tableau.com/s/gallery/mapping-1854-cholera-outbreak)



 [Resources | Tableau Public](https://public.tableau.com/en-us/s/resources)



 [10 Free Must-Read Books for Machine Learning and Data Science](https://www.kdnuggets.com/2017/04/10-free-must-read-books-machine-learning-data-science.html?utm_content=buffer5a67a&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)



 [60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more](http://www.kdnuggets.com/2015/09/free-data-science-books.html)



 [Data Skeptic](https://dataskeptic.com/)



 [GGobi data visualization system.](http://www.ggobi.org/)



 [GitHub (Tirthajyoti Sarkar)](https://github.com/tirthajyoti)



 [Here Are (Approximately) 3000 Free Data Sources You Can Use Right Now](https://www.forbes.com/sites/metabrown/2017/06/30/here-are-approximately-3000-free-sources-for-data-you-can-use-right-now/amp/?utm_content=bufferef401&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)



 [If you want to learn Data Science, take a few of these statistics classes](https://medium.freecodecamp.com/if-you-want-to-learn-data-science-take-a-few-of-these-statistics-classes-9bbabab098b9)



 [Learn to code | Codecademy](https://www.codecademy.com/)



 [Lecture Notes | Introduction to MATLAB | Electrical Engineering and Computer Science | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-094-introduction-to-matlab-january-iap-2010/lecture-notes/)



 [Medium – Read, write and share stories that matter](https://medium.com/)



 [Scientifically Sound](https://scientificallysound.org/)



 [Top 28 Cheat Sheets for Machine Learning, Data Science, Probability, SQL & Big Data](https://www.analyticsvidhya.com/blog/2017/02/top-28-cheat-sheets-for-machine-learning-data-science-probability-sql-big-data/?utm_content=buffer9e308&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)



 [Learn Data Science - Infographic (article) - DataCamp](https://www.datacamp.com/community/tutorials/learn-data-science-infographic)



 [Homework 3](file:///C:/Users/Tirtha/Documents/Personal/GaTech%20OMSA/Courses/Fall%202018/ISYE%206501%20-%20Introduction%20to%20Analytics%20Modeling/HW/HW-3/Peer%20Review/2/Homework_3.html)



 ## Neural Network



### Videos



[Deep blueberry](https://mithi.github.io/deep-blueberry/ch0-introduction.html)



 [Brandon Rohrer - Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)](https://www.youtube.com/watch?v=WCUNPb-5EYI)



 [CS231n Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM - YouTube](https://www.youtube.com/watch?v=iX5V1WpxxkY)



 [Nuts and Bolts of Applying Deep Learning (Andrew Ng) - YouTube](https://www.youtube.com/watch?v=F1ka6a13S9I)



 [Siraj Raval - LSTM Networks - The Math of Intelligence (Week 8) - YouTube](https://www.youtube.com/watch?v=9zhrxE5PQgY)



 [Siraj Raval - Recurrent Neural Networks - The Math of Intelligence (Week 5) - YouTube](https://www.youtube.com/watch?v=BwmddtPFWtA)



 [Andrew Ng: Artificial Intelligence is the New Electricity - YouTube](https://www.youtube.com/watch?v=21EiKfQYZXc)



 [A Neural Network Playground](http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.53044&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false)



 [But what *is* a Neural Network? | Deep learning, chapter 1](https://www.youtube.com/watch?time_continue=80&v=aircAruvnKk)



 [Convolutional Networks in Java - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https://deeplearning4j.org/convolutionalnets.html)



 [CS231n Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/convolutional-networks/)



 [Deep Learning Fundamentals - Cognitive Class](https://cognitiveclass.ai/courses/introduction-deep-learning/?utm_content=buffer3ab0d&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)



 [Exploring LSTMs](http://blog.echen.me/2017/05/30/exploring-lstms/)



 [Feature Visualization](https://distill.pub/2017/feature-visualization/)



 [Neural networks and deep learning](http://neuralnetworksanddeeplearning.com/)



 [Understanding Hinton’s Capsule Networks. Part I: Intuition.](https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b)



 [Understanding LSTM Networks -- colah's blog](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)



 [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)



 [Andrej Carpathy blog - Hacker's guide to Neural Networks](http://karpathy.github.io/neuralnets/)



 [A Beginner's Guide to Recurrent Networks and LSTMs - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https://deeplearning4j.org/lstm.html#a-beginners-guide-to-recurrent-networks-and-lstms)



 [J Alammar – Explorations in touchable pixels and intelligent androids](http://jalammar.github.io/)



### Keras



 [Guide to the Sequential model - Keras Documentation](https://keras.io/getting-started/sequential-model-guide/)



 [Keras Documentation](https://keras.io/)



 [How to Use Word Embedding Layers for Deep Learning with Keras - Machine Learning Mastery](https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/)

 

### TensorFlow



 [Building Input Functions with tf.estimator  |  TensorFlow](https://www.tensorflow.org/get_started/input_fn)



 [Getting Started With TensorFlow  |  TensorFlow](https://www.tensorflow.org/get_started/get_started)



 [Installing TensorFlow on Windows  |  TensorFlow](https://www.tensorflow.org/install/install_windows)



 [TensorFlow](https://www.tensorflow.org/)



 [TensorFlow Linear Model Tutorial  |  TensorFlow](https://www.tensorflow.org/tutorials/wide)



 [TensorFlow Wide & Deep Learning Tutorial  |  TensorFlow](https://www.tensorflow.org/tutorials/wide_and_deep)



 [Using TensorFlow in Windows with a GPU | Heaton Research](http://www.heatonresearch.com/2017/01/01/tensorflow-windows-gpu.html)



 [Installation Guide Windows :: CUDA Toolkit Documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/)



 [7 Steps to Mastering Machine Learning With Python](https://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html)



 [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)



 [Berkeley AI Materials](http://ai.berkeley.edu/lecture_videos.html)



 [Deep Learning For Coders fast.ai](http://course.fast.ai/)



 [Lecture Collection | Machine Learning - Stanford course](https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599)



 [Microsoft Azure ML Cheat sheet](https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice)



 [Pedro Domigos Machine Learning lectures](https://www.youtube.com/user/UWCSE/playlists?shelf_id=16&sort=dd&view=50)



 [The Hitchhiker’s Guide to Machine Learning in Python](https://medium.com/@conordewey3/the-hitchhikers-guide-to-machine-learning-algorithms-in-python-bfad66adb378)



 [Top 10 Machine Learning Projects on Github](http://www.kdnuggets.com/2015/12/top-10-machine-learning-github.html)



 [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)



 [ISLR class videos](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/



 [Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first…](https://towardsdatascience.com/machine-learning-zero-to-hero-everything-you-need-in-order-to-compete-on-kaggle-for-the-first-time-18644e701cf1)



 [GOOGLE - Rules of Machine Learning:  |  Machine Learning Rules  |  Google Developers](https://developers.google.com/machine-learning/rules-of-ml/)



 [PySpark ML tutorial example](https://nbviewer.jupyter.org/github/anindya-saha/Data-Science-with-Spark/blob/master/predict-us-census-income-classification/predict-us-census-income.ipynb)



 [Python Generators Tutorial](https://www.dataquest.io/blog/python-generators-tutorial/)



 [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/)



 [Understanding the GitHub Flow · GitHub Guides](https://guides.github.com/introduction/flow/)



 [How to Prepare for a Machine Learning Interview - Semantic Bits](https://semanti.ca/blog/?how-to-prepare-for-a-machine-learning-interview)



 [Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data](https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463)



 [AI Knowledge Map: How To Classify AI Technologies](https://www.forbes.com/sites/cognitiveworld/2018/08/22/ai-knowledge-map-how-to-classify-ai-technologies/#5878d667773f)



## Apache Spark



 [Building A Linear Regression with PySpark and MLlib](https://towardsdatascience.com/building-a-linear-regression-with-pyspark-and-mllib-d065c3ba246a)



 [Complete Guide on DataFrame Operations in PySpark](https://www.analyticsvidhya.com/blog/2016/10/spark-dataframe-and-operations/)



 [Install_Spark_on_Windows10.pdf](https://www.ics.uci.edu/~shantas/Install_Spark_on_Windows10.pdf)



 [Introduction · Mastering Apache Spark](https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/)



 [MLlib: Main Guide - Spark 2.3.1 Documentation](http://spark.apache.org/docs/latest/ml-guide.html)



 [Overview - Spark 2.3.1 Documentation](https://spark.apache.org/docs/latest/)



 [RDD Programming Guide - Spark 2.3.1 Documentation](https://spark.apache.org/docs/latest/rdd-programming-guide.html)



 [rdflib 5.0.0-dev — rdflib 5.0.0-dev documentation](https://rdflib3.readthedocs.io/en/latest/index.html)



 [Spark SQL and DataFrames - Spark 2.3.1 Documentation](http://spark.apache.org/docs/latest/sql-programming-guide.html)



 [Welcome to Spark Python API Docs! — PySpark 2.3.1 documentation](http://spark.apache.org/docs/latest/api/python/)



## Cloud computing



 [Why You Should Consider Google AI Platform For Your Machine Learning Projects](https://www.forbes.com/sites/janakirammsv/2019/04/16/why-you-should-consider-google-ai-platform-for-your-machine-learning-projects/amp/)

 

 [Cloud Computing Tutorial for Beginners | Cloud Computing Explained | Cloud Computing | Simplilearn - YouTube](https://www.youtube.com/watch?v=RWgW-CgdIk0)



### Computation, Computing



 [A Short Guide to Hard Problems | Quanta Magazine](https://www.quantamagazine.org/a-short-guide-to-hard-problems-20180716/?fbclid=IwAR2Yz76T1uE835BC7STAdIZUA-xR4cPUI2BeC-yS7Bwkk96fUPOePeyNCZg)



### Data Mining



 [The 10 Mining Techniques Data Scientists Need for Their Toolbox](https://towardsdatascience.com/the-10-mining-techniques-data-scientists-need-for-their-toolbox-ae15a5733b02)



 [Wikipedia Data Science: Working with the World’s Largest Encyclopedia](https://towardsdatascience.com/wikipedia-data-science-working-with-the-worlds-largest-encyclopedia-c08efbac5f5c)



### Data wrangling related



  [A Brief Overview of Outlier Detection Techniques – Towards Data Science](https://towardsdatascience.com/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561)



## Docker, Containers



 [A Beginner-Friendly Introduction to Containers, VMs and Docker](https://medium.freecodecamp.org/a-beginner-friendly-introduction-to-containers-vms-and-docker-79a9e3e119b)



 [A fast and easy Docker tutorial for beginners (video series)](https://medium.freecodecamp.org/docker-quick-start-video-tutorials-1dfc575522a0)



 [Docker Compose in 12 Minutes - YouTube](https://www.youtube.com/watch?v=Qw9zlE3t8Ko)



 [How to Install and Use Docker on Ubuntu 18.04 | DigitalOcean](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-on-ubuntu-18-04)



 [How to Install Docker On Ubuntu 18.04 Bionic Beaver - LinuxConfig.org](https://linuxconfig.org/how-to-install-docker-on-ubuntu-18-04-bionic-beaver)



 [Learn Docker in 12 Minutes 🐳 - YouTube](https://www.youtube.com/watch?v=YFl2mCHdv24)



 [What is a Container? - YouTube](https://www.youtube.com/watch?v=EnJ7qX9fkcU)



 [What is Docker | Docker Tutorial for Beginners | Docker Container | DevOps Tools | Edureka - YouTube](https://www.youtube.com/watch?v=lcQfQRDAMpQ)



 [Building Your Own Data Science Platform With Python & Docker - YouTube](https://www.youtube.com/watch?v=NC2wXYHBrL0)



### Interview related



 [50+ Data Structure and Algorithms Interview Questions for Programmers](https://hackernoon.com/50-data-structure-and-algorithms-interview-questions-for-programmers-b4b1ac61f5b0)



## Web Technologies



### REST, API, Microservice



  [GraphQL vs. REST – Apollo GraphQL](https://blog.apollographql.com/graphql-vs-rest-5d425123e34b)



 [Microservices, APIs, and Swagger: How They Fit Together | Swagger](https://swagger.io/blog/api-strategy/microservices-apis-and-swagger/)



 [REST API concepts and examples - YouTube](https://www.youtube.com/watch?v=7YcW25PHnAA)



 [Web Architecture 101 – VideoBlocks Product & Engineering](https://engineering.videoblocks.com/web-architecture-101-a3224e126947)



 [REST API & RESTful Web Services Explained - YouTube](https://www.youtube.com/watch?v=LooL6_chvN4)



 [Our Collections – Towards Data Science](https://towardsdatascience.com/our-collections-3920888f831c)



### JSON, XML, HTML



[JSON Crash Course - YouTube](https://www.youtube.com/watch?v=wI1CWzNtE-M)

[Can I use... Support tables for HTML5, CSS3, etc](https://caniuse.com/)

[HTML5 Form Validation Examples < HTML | The Art of Web](http://www.the-art-of-web.com/html/html5-form-validation/)

 

### CSS



 [The CSS Handbook: a handy guide to CSS for developers](https://medium.freecodecamp.org/the-css-handbook-a-handy-guide-to-css-for-developers-b56695917d11)

 

 [Creating a Simple Website with HTML and CSS - Part 1 - YouTube](https://www.youtube.com/watch?v=A3Xgz9PHGuA)



 [CSS Introduction - W3Schools](https://www.w3schools.com/css/css_intro.asp)



 [Learn CSS in 12 Minutes - YouTube](https://www.youtube.com/watch?v=0afZj1G0BIE)



### JavaScript



 [Beginner JavaScript Tutorial - 1 - Introduction to JavaScript - YouTube](https://www.youtube.com/watch?v=yQaAGmHNn9s)



 [Eloquent JavaScript](http://eloquentjavascript.net/) 



 [Form Validation with JavaScript - Check for an Empty Text Field - YouTube](https://www.youtube.com/watch?v=Pc2e2YpKArg) 



 [JavaScript Basics Part 1](https://www.htmlgoodies.com/primers/jsp/article.php/3586411) 



 [JavaScript beginner tutorial 30 - form validation text boxes and passwords - YouTube](https://www.youtube.com/watch?v=y5UEXujzSag) 



 [JavaScript: Simple Form Validation - YouTube](https://www.youtube.com/watch?v=_Z-0cwONN6c) 



 [Learn JavaScript in 12 Minutes - YouTube](https://www.youtube.com/watch?v=Ukg_U3CnJWI) 



 [Machine Learning with JavaScript : Part 1 – Hacker Noon](https://hackernoon.com/machine-learning-with-javascript-part-1-9b97f3ed4fe5) 



 [Machine Learning with JavaScript : Part 2 – Hacker Noon](https://hackernoon.com/machine-learning-with-javascript-part-2-da994c17d483) 



 [W3School - JavaScript Form Validation](https://www.w3schools.com/js/js_validation.asp) 



 [W3schools - JavaScript Tutorial](https://www.w3schools.com/js/) 



 [ClearlyDecoded.com - Yaakov Chaikin](https://clearlydecoded.com/) 



 [GoDaddy Hosting Account Getting Started Guide](https://www.godaddy.com/help/hosting-account-getting-started-guide-1361) 



 [How to Make A Website in 2018 - Web Hosting Guide | WHSR](https://www.webhostingsecretrevealed.net/web-hosting-beginner-guide/) 



 [jhu-ep-coursera/fullstack-course4: Example code for HTML, CSS, and Javascript for Web Developers Coursera Course](https://github.com/jhu-ep-coursera/fullstack-course4)

 

 [Free JavaScript Tutorial - Scaler](https://www.scaler.com/topics/javascript/) 



## LaTeX, Markdown, reST



 [Art of Problem Solving - LaTeX symbols](https://artofproblemsolving.com/wiki/index.php/LaTeX:Symbols)



 [Detexify LaTeX handwritten symbol recognition](http://detexify.kirelabs.org/classify.html)



 [http://quicklatex.com/](http://quicklatex.com/)

 

 [LaTeX symbol Wiki](https://oeis.org/wiki/List_of_LaTeX_mathematical_symbols#Set_and.2For_logic_notation)



 [The Comprehensive LaTeX Symbol ListThe Comprehensive LaTeX Symbol List - symbols-a4.pdf](http://ctan.math.illinois.edu/info/symbols/comprehensive/symbols-a4.pdf)



 [Pandoc - Pandoc User’s Guide](https://pandoc.org/MANUAL.html#pandocs-markdown)



 [MathJax Documentation — MathJax 2.7 documentation](http://docs.mathjax.org/en/latest/)



 [TeX Commands available in MathJax](http://www.onemathematicalcat.org/MathJaxDocumentation/TeXSyntax.htm)



## Linux, OS

 

 [How to Install Ubuntu Linux on VirtualBox on Windows 10 [Step by Step Guide] | It's FOSS](https://itsfoss.com/install-linux-in-virtualbox/)



 [Microsoft PowerShell Tutorial & Training Course – Microsoft Virtual Academy](https://mva.microsoft.com/en-us/training-courses/getting-started-with-microsoft-powershell-8276?l=r54IrOWy_2304984382)



 [Most Popular Linux Distributions and Why They Dominate the Market](https://blog.storagecraft.com/popular-linux-distributions-dominate-market/)



 [The Dead-Simple Guide to Installing a Linux Virtual Machine on Windows - StorageCraft Technology Corporation](https://blog.storagecraft.com/the-dead-simple-guide-to-installing-a-linux-virtual-machine-on-windows/)



 [[Solved] Could not get lock /var/lib/dpkg/lock Error in Ubuntu | It's FOSS](https://itsfoss.com/could-not-get-lock-error/)



## Time series 



 [Time Series Analysis in Python: An Introduction – Towards Data Science](https://towardsdatascience.com/time-series-analysis-in-python-an-introduction-70d5a5b1d52a)



 [RJT1990/pyflux: Open source time series library for Python](https://github.com/RJT1990/pyflux)



 [MaxBenChrist/awesome_time_series_in_python: This curated list contains python packages for time series analysis](https://github.com/MaxBenChrist/awesome_time_series_in_python)



 [Getting Started with Time Series — PyFlux 0.4.7 documentation](http://pyflux.readthedocs.io/en/latest/getting_started.html)



 [Introduction to ARIMA models](https://people.duke.edu/~rnau/411arim.htm)



 [Complete guide to create a Time Series Forecast (with Codes in Python)](https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/)



 [How to Create an ARIMA Model for Time Series Forecasting with Python](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/)

 

 [Time series with Siraj course by Kaggle](https://www.kaggle.com/learn/time-series-with-siraj)



## Interesting Articles



 [Debunking The Myths And Reality Of Artificial Intelligence - Forbes](https://www.forbes.com/sites/cognitiveworld/2019/04/22/debunking-the-myths-and-reality-of-artificial-intelligence/#614c70e743b5)

 

 [Artificial Intelligence — The Revolution Hasn’t Happened Yet](https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7)



 [Artificial Intelligence Learns to Learn Entirely on Its Own | Quanta Magazine](https://www.quantamagazine.org/artificial-intelligence-learns-to-learn-entirely-on-its-own-20171018/?utm_content=buffer578b7&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)



 [Can Buddhist philosophy explain what came before the Big Bang? | Aeon Essays](https://aeon.co/essays/can-buddhist-philosophy-explain-what-came-before-the-big-bang)



 [Coming to Grips with the Implications of Quantum Mechanics - Scientific American Blog Network](https://blogs.scientificamerican.com/observations/coming-to-grips-with-the-implications-of-quantum-mechanics/)



 [Did Toolmaking Pave the Road for Human Language? - The Atlantic](https://www.theatlantic.com/science/archive/2018/06/toolmaking-language-brain/562385/)



 [Edward Witten Ponders the Nature of Reality | Quanta Magazine](https://www.quantamagazine.org/edward-witten-ponders-the-nature-of-reality-20171128/)



[Gatekeeping and Elitism in Data Science](https://towardsdatascience.com/gatekeeping-and-elitism-in-data-science-74cf19cd5744)



 [How Do Aliens Solve Climate Change? - The Atlantic](https://www.theatlantic.com/science/archive/2018/05/how-do-aliens-solve-climate-change/561479/)



 [How I Learned to Stop Worrying About the LHC’s Missing New Physics](http://nautil.us/issue/64/the-unseen/fine-tuning-is-just-fine)



 [How Information Got Re-Invented – Limits – Medium](https://medium.com/s/nautilus-limits/how-information-got-re-invented-888fea36c4a5)



 [How Social Media Endangers Knowledge | WIRED](https://www.wired.com/story/wikipedias-fate-shows-how-the-web-endangers-knowledge/)



 [In These Small Cities, AI Advances Could Be Costly - MIT Technology Review](https://www.technologyreview.com/s/609076/in-these-small-cities-ai-advances-could-be-costly/?utm_campaign=Owned+Social&utm_source=Facebook&utm_medium=Owned+Social)



 [Inside Amazon’s $3.5 million competition to make Alexa chat like a human - The Verge](https://www.theverge.com/2018/6/13/17453994/amazon-alexa-prize-2018-competition-conversational-ai-chatbots)



 [Let’s make private data into a public good - MIT Technology Review](https://www.technologyreview.com/s/611489/lets-make-private-data-into-a-public-good/)



 [On Chomsky and the Two Cultures of Statistical Learning](http://norvig.com/chomsky.html)



 [Quantum Leaps in Quantum Computing? - Scientific American](https://www.scientificamerican.com/article/quantum-leaps-in-quantum-computing/?utm_source=facebook&utm_medium=social&utm_campaign=sa-editorial-social&utm_content&utm_term=physics_sa-magazine_text_free)



 [Strategy vs. Tactics: What's the Difference and Why Does it Matter?](https://fs.blog/2018/08/strategy-vs-tactics/)



 [The case for genetically engineering a smarter human-cyborg population to avoid the threat of existential catastrophe.](https://slate.com/technology/2018/09/genetic-engineering-to-stop-doomsday.html)



 [The Fragile State of the Midwest’s Public Universities - The Atlantic](https://www.theatlantic.com/business/archive/2017/10/midwestern-public-research-universities-funding/542889/?utm_source=vxfb)



 [The Quantum Thermodynamics Revolution | Quanta Magazine](https://www.quantamagazine.org/the-quantum-thermodynamics-revolution-20170502?utm_content=buffere2607&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)



 [The Way You Read Books Says A Lot About Your Intelligence, Here’s Why](https://medium.com/the-mission/the-way-you-read-books-says-a-lot-about-your-intelligence-find-out-why-c2127b00eb03)



 [To Build Truly Intelligent Machines, Teach Them Cause and Effect | Quanta Magazine](https://www.quantamagazine.org/to-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515/)



 [Why Is American Mass Transit So Bad? It's a Long Story. - CityLab](https://www.citylab.com/transportation/2018/08/how-america-killed-transit/568825/)



 [Yuval Noah Harari on what 2050 has in store for humankind | WIRED UK](https://www.wired.co.uk/article/yuval-noah-harari-extract-21-lessons-for-the-21st-century)



 [Yuval Noah Harari on Why Technology Favors Tyranny - The Atlantic](https://www-theatlantic-com.cdn.ampproject.org/c/s/www.theatlantic.com/amp/article/568330/)



 [Yuval Noah Harari: ‘The idea of free information is extremely dangerous’ | Culture | The Guardian](https://www.theguardian.com/culture/2018/aug/05/yuval-noah-harari-free-information-extremely-dangerous-interview-21-lessons)



 [Beyond Weird: Decoherence, Quantum Weirdness, and Schrödinger's Cat - The Atlantic](https://www.theatlantic.com/science/archive/2018/10/beyond-weird-decoherence-quantum-weirdness-schrodingers-cat/573448/)



 [Life Is a Braid in Spacetime – Time – Medium](https://medium.com/s/nautilus-time/life-is-a-braid-in-spacetime-16dbf74d105f)



 [Mental Models: How to Train Your Brain to Think in New Ways - James Clear - Pocket](https://getpocket.com/explore/item/mental-models-how-to-train-your-brain-to-think-in-new-ways-820549098)



 [Don’t Compete. Create! - Darius Foroux - Pocket](https://getpocket.com/explore/item/don-t-compete-create-2068896981)



 [Tesla will live and die by the Gigafactory - The Verge](https://www.theverge.com/transportation/2018/11/30/18118451/tesla-gigafactory-nevada-video-elon-musk-jobs-model-3)



 [So you want to be a Research Scientist – Vincent Vanhoucke – Medium](https://medium.com/@vanhoucke/so-you-want-to-be-a-research-scientist-363c075d3d4c)



 [Homeland Security Will Let Software Flag Potential Terrorists](https://theintercept.com/2018/12/03/air-travel-surveillance-homeland-security/)



 [What Happens When a World Order Ends](https://www.foreignaffairs.com/articles/2018-12-11/how-world-order-ends)



 [Kevin Slavin: How algorithms shape our world | TED Talk](https://www.ted.com/talks/kevin_slavin_how_algorithms_shape_our_world?language=en#t-229771)



 [The Brain's Autopilot Mechanism Steers Consciousness - Scientific American](https://www.scientificamerican.com/article/the-brains-autopilot-mechanism-steers-consciousness/)



 [What is Intelligence? – Towards Data Science](https://towardsdatascience.com/what-is-intelligence-a69cbd8bb1b4)



 [This Is Exactly How You Should Train Yourself To Be Smarter - Michael Simmons - Pocket](https://getpocket.com/explore/item/this-is-exactly-how-you-should-train-yourself-to-be-smarter)



 [How to be More Productive and Eliminate Time Wasting Activities by Using the “Eisenhower Box” - James Clear - Pocket](https://getpocket.com/explore/item/how-to-be-more-productive-and-eliminate-time-wasting-activities-by-using-the-eisenhower-box)



 [The blind spot of science is the neglect of lived experience | Aeon Essays](https://aeon.co/essays/the-blind-spot-of-science-is-the-neglect-of-lived-experience)



 ## Julia



 [A Complete Tutorial to Learn Data Science with Julia from Scratch](https://www.analyticsvidhya.com/blog/2017/10/comprehensive-tutorial-learn-data-science-julia-from-scratch/)



## Machine Learning



### Experiment tracking

[ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It](https://neptune.ai/blog/ml-experiment-tracking)



### Fairness and bias

[Evaluating machine learning models for fairness and bias](https://towardsdatascience.com/evaluating-machine-learning-models-fairness-and-bias-4ec82512f7c3)



### Deployment of ML



[Creating data science APIs with Flask](https://faculty.ai/blog/creating-data-science-apis-with-flask/)



[Flask and Heroku for online Machine Learning deployment](https://towardsdatascience.com/flask-and-heroku-for-online-machine-learning-deployment-425beb54a274)



[Overview of the different approaches to putting Machine Learning (ML) models in production](https://medium.com/analytics-and-data/overview-of-the-different-approaches-to-putting-machinelearning-ml-models-in-production-c699b34abf86)



 [[Guide] Building Data Science Web Application with React, NodeJS, and MySQL](https://towardsdatascience.com/guide-building-data-science-web-application-with-react-nodejs-and-mysql-1c55416ff0fb)



 [A beginner’s guide to training and deploying machine learning models using Python](https://medium.freecodecamp.org/a-beginners-guide-to-training-and-deploying-machine-learning-models-using-python-48a313502e5a)



 [A Guide to Scaling Machine Learning Models in Production](https://hackernoon.com/a-guide-to-scaling-machine-learning-models-in-production-aa8831163846)



 [Deploying Keras Deep Learning Models with Flask – Towards Data Science](https://towardsdatascience.com/deploying-keras-deep-learning-models-with-flask-5da4181436a2)



 [Deploying Machine Learning at Scale - Algorithmia Blog](https://blog.algorithmia.com/deploying-machine-learning-at-scale/)



 [Deploying Machine Learning has never been so easy – Towards Data Science](https://towardsdatascience.com/https-towardsdatascience-com-deploying-machine-learning-has-never-been-so-easy-bbdb500a39a)



 [Quora - How do you take a machine learning model to production?](https://www.quora.com/How-do-you-take-a-machine-learning-model-to-production)



 [Tutorial to deploy Machine Learning model in Production as API with Flask](https://www.analyticsvidhya.com/blog/2017/09/machine-learning-models-as-apis-using-flask/)



 [From Big Data to micro-services: how to serve Spark-trained models through AWS lambdas](https://towardsdatascience.com/from-big-data-to-micro-services-how-to-serve-spark-trained-models-through-aws-lambdas-ebe129f4849c)



 [How to deliver on Machine Learning projects – Insight Data](https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0)



 [Deploying a Keras Deep Learning Model as a Web Application in P](https://towardsdatascience.com/deploying-a-keras-deep-learning-model-as-a-web-application-in-p-fc0f2354a7ff)



### Genetic Algorithm



 [Genetic Algorithm Implementation in Python – Towards Data Science](https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6)



 [Introduction to Optimization with Genetic Algorithm](https://towardsdatascience.com/introduction-to-optimization-with-genetic-algorithm-2f5001d9964b)



 [A tutorial on Differential Evolution with Python · Pablo R. Mier](https://pablormier.github.io/2017/09/05/a-tutorial-on-differential-evolution-with-python/)



### Keras



 [Guide to the Sequential model - Keras Documentation](https://keras.io/getting-started/sequential-model-guide/)



 [Keras Documentation](https://keras.io/)



 [How to Use Word Embedding Layers for Deep Learning with Keras - Machine Learning Mastery](https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/)



### Neural Network



### Videos



 [Brandon Rohrer - Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)](https://www.youtube.com/watch?v=WCUNPb-5EYI)



 [CS231n Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM - YouTube](https://www.youtube.com/watch?v=iX5V1WpxxkY)



 [Nuts and Bolts of Applying Deep Learning (Andrew Ng) - YouTube](https://www.youtube.com/watch?v=F1ka6a13S9I)



 [Siraj Raval - LSTM Networks - The Math of Intelligence (Week 8) - YouTube](https://www.youtube.com/watch?v=9zhrxE5PQgY)



 [Siraj Raval - Recurrent Neural Networks - The Math of Intelligence (Week 5) - YouTube](https://www.youtube.com/watch?v=BwmddtPFWtA)



 [Andrew Ng: Artificial Intelligence is the New Electricity - YouTube](https://www.youtube.com/watch?v=21EiKfQYZXc)



 [A Beginner's Guide to Recurrent Networks and LSTMs - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https://deeplearning4j.org/lstm.html#a-beginners-guide-to-recurrent-networks-and-lstms)



 [A Neural Network Playground](http://playground.tensorflow.org/#activation=tanh&batchSize=10&dataset=circle&regDataset=reg-plane&learningRate=0.03&regularizationRate=0&noise=0&networkShape=4,2&seed=0.53044&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&initZero=false&hideText=false)



 [A Visual Guide to Evolution Strategies](http://blog.otoro.net/2017/10/29/visual-evolution-strategies/)



 [Andrej Carpathy blog - Hacker's guide to Neural Networks](http://karpathy.github.io/neuralnets/)



 [Best (and Free!!) Resources to understand Nuts and Bolts of Deep learning](https://hackernoon.com/best-and-free-resources-to-understand-nuts-and-bolts-of-deep-learning-9c51166ffdf5)



 [But what *is* a Neural Network? | Deep learning, chapter 1](https://www.youtube.com/watch?time_continue=80&v=aircAruvnKk)



 [Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data](https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463)



 [Convolutional Networks in Java - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https://deeplearning4j.org/convolutionalnets.html)



 [CS231n Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/convolutional-networks/)



 [Deep Dive into Math Behind Deep Networks – Towards Data Science](https://towardsdatascience.com/https-medium-com-piotr-skalski92-deep-dive-into-deep-networks-math-17660bc376ba)



 [Deep Learning Fundamentals - Cognitive Class](https://cognitiveclass.ai/courses/introduction-deep-learning/?utm_content=buffer3ab0d&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)



 [Exploring LSTMs](http://blog.echen.me/2017/05/30/exploring-lstms/)



 [Feature Visualization](https://distill.pub/2017/feature-visualization/)



 [J Alammar – Explorations in touchable pixels and intelligent androids](http://jalammar.github.io/)



 [Learning without Backpropagation: Intuition and Ideas (Part 1) – Tom Breloff](http://www.breloff.com/no-backprop/)



 [Must know Information Theory concepts in Deep Learning (AI)](https://towardsdatascience.com/must-know-information-theory-concepts-in-deep-learning-ai-e54a5da9769d)



 [Neural networks and deep learning](http://neuralnetworksanddeeplearning.com/)



 [Neural Style Transfer: Creating Art with Deep Learning using tf.keras and eager execution](https://medium.com/tensorflow/neural-style-transfer-creating-art-with-deep-learning-using-tf-keras-and-eager-execution-7d541ac31398)



 [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)



 [Understanding Hinton’s Capsule Networks. Part I: Intuition.](https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b)



 [Understanding LSTM Networks -- colah's blog](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)



 [A Neural Network in 13 lines of Python (Part 2 - Gradient Descent) - i am trask](https://iamtrask.github.io/2015/07/27/python-network-part2/)



 [How Do Artificial Neural Networks Learn? – Towards Data Science](https://towardsdatascience.com/how-do-artificial-neural-networks-learn-773e46399fc7)



 [The Neural Network Zoo - The Asimov Institute](http://www.asimovinstitute.org/neural-network-zoo/)



 [A History of Deep Learning | Import.io](https://www.import.io/post/history-of-deep-learning/)



 [The Ultimate NanoBook to understand Deep Learning based Image Classifier](https://towardsdatascience.com/https-medium-com-rishabh-grg-the-ultimate-nanobook-to-understand-deep-learning-based-image-classifier-33f43fea8327)



### NLP



 [How to solve 90% of NLP problems: a step-by-step guide](https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e)



 [Coding & English Lit: Natural Language Processing in Python](https://medium.com/@kellylougheed/coding-english-lit-natural-language-processing-in-python-ba8ebae4dde3)



 [TextBlob: Simplified Text Processing — TextBlob 0.15.1 documentation](https://textblob.readthedocs.io/en/dev/)



 [Python Regular Expression Tutorial (article) - DataCamp](https://www.datacamp.com/community/tutorials/python-regular-expression-tutorial)

 

 [Stanford NLP](https://stanfordnlp.github.io/stanfordnlp/)



### Reinforcement Learning



[Reinforcement Learning Course - Full Machine Learning Tutorial](https://www.youtube.com/watch?v=ELE2_Mftqoc)



 [A brief introduction to reinforcement learning – freeCodeCamp.org](https://medium.freecodecamp.org/a-brief-introduction-to-reinforcement-learning-7799af5840db)



 [An introduction to Reinforcement Learning – freeCodeCamp.org](https://medium.freecodecamp.org/an-introduction-to-reinforcement-learning-4339519de419)



 [Key Papers in Deep RL — Spinning Up documentation](https://spinningup.openai.com/en/latest/spinningup/keypapers.html#model-free-rl)



 [Nuts & Bolts of Reinforcement Learning: Model Based Planning using Dynamic Programming](https://medium.com/analytics-vidhya/nuts-bolts-of-reinforcement-learning-model-based-planning-using-dynamic-programming-d71d52011b53)



 [Reinforcement Learning: A Deep Dive | Toptal](https://www.toptal.com/machine-learning/deep-dive-into-reinforcement-learning)



 [Part 1: Key Concepts in RL — Spinning Up documentation](https://spinningup.openai.com/en/latest/spinningup/rl_intro.html)



 [Dissecting Reinforcement Learning-Part.1](https://mpatacchiola.github.io/blog/2016/12/09/dissecting-reinforcement-learning.html)



 [Reinforcement Q-Learning from Scratch in Python with OpenAI Gym – LearnDataSci](https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/)



 [Google AI Blog: Curiosity and Procrastination in Reinforcement Learning](https://ai.googleblog.com/2018/10/curiosity-and-procrastination-in.html)



 [Reinforcement Learning: Monte Carlo Learning using OpenAI Gym](https://www.analyticsvidhya.com/blog/2018/11/reinforcement-learning-introduction-monte-carlo-learning-openai-gym/?utm_source=linkedin.com)



### TensorFlow



 [Building Input Functions with tf.estimator  |  TensorFlow](https://www.tensorflow.org/get_started/input_fn)



 [Getting Started With TensorFlow  |  TensorFlow](https://www.tensorflow.org/get_started/get_started)



 [Installing TensorFlow on Windows  |  TensorFlow](https://www.tensorflow.org/install/install_windows)



 [TensorFlow](https://www.tensorflow.org/)



 [TensorFlow Linear Model Tutorial  |  TensorFlow](https://www.tensorflow.org/tutorials/wide)



 [TensorFlow Wide & Deep Learning Tutorial  |  TensorFlow](https://www.tensorflow.org/tutorials/wide_and_deep)



 [Using TensorFlow in Windows with a GPU | Heaton Research](http://www.heatonresearch.com/2017/01/01/tensorflow-windows-gpu.html)



 [Installation Guide Windows :: CUDA Toolkit Documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/)



 [7 Steps to Mastering Machine Learning With Python](https://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html)



 [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)



 [Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur | No Free Hunch](http://blog.kaggle.com/2016/07/21/approaching-almost-any-machine-learning-problem-abhishek-thakur/)



 [Automated Machine Learning Hyperparameter Tuning in Python](https://towardsdatascience.com/automated-machine-learning-hyperparameter-tuning-in-python-dfda59b72f8a)



 [Berkeley AI Materials](http://ai.berkeley.edu/lecture_videos.html)



 [Deep Learning For Coders fast.ai](http://course.fast.ai/)



 [Essentials of Machine Learning Algorithms (with Python and R Codes)](https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/?utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)



 [GOOGLE - Rules of Machine Learning:  |  Machine Learning Rules  |  Google Developers](https://developers.google.com/machine-learning/rules-of-ml/)



 [http://www.r2d3.us/visual-intro-to-machine-learning-part-2/](http://www.r2d3.us/visual-intro-to-machine-learning-part-2/)



 [ISLR class videos](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/)



 [Lecture Collection | Machine Learning - Stanford course](https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599)



 [Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first…](https://towardsdatascience.com/machine-learning-zero-to-hero-everything-you-need-in-order-to-compete-on-kaggle-for-the-first-time-18644e701cf1)



 [Microsoft Azure ML Cheat sheet](https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice)



 [Open Machine Learning Course (beta) • mlcourse.ai](https://mlcourse.ai/)



 [Pedro Domigos Machine Learning lectures](https://www.youtube.com/user/UWCSE/playlists?shelf_id=16&sort=dd&view=50)



 [The Hitchhiker’s Guide to Machine Learning in Python](https://medium.com/@conordewey3/the-hitchhikers-guide-to-machine-learning-algorithms-in-python-bfad66adb378)



 [Top 10 Machine Learning Projects on Github](http://www.kdnuggets.com/2015/12/top-10-machine-learning-github.html)



 [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)



 ### Optimization and ML



 [Learning to Optimize with Reinforcement Learning – The Berkeley Artificial Intelligence Research Blog](https://bair.berkeley.edu/blog/2017/09/12/learning-to-optimize-with-rl/)



### Kaggle



[Hello Kaggle! - A Kaggle Guide for someone who is new at Kaggle](https://github.com/stevekwon211/Hello-Kaggle)



## Python



### Tutorials



[Everything About Python — Beginner To Advanced](https://medium.com/fintechexplained/everything-about-python-from-beginner-to-advance-level-227d52ef32d2)



### Jupyter and IDE related



[Interactive spreadsheets in Jupyter](https://towardsdatascience.com/interactive-spreadsheets-in-jupyter-32ab6ec0f4ff)



[PyCharm for data scientists](https://www.kdnuggets.com/2019/05/pycharm-data-scientists.html)



[Built-in magic commands — IPython 6.2.1 documentation](http://ipython.readthedocs.io/en/stable/interactive/magics.html)



[Concrete Statistics Jupyter Notebook Peter Norvig](http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb)



[Economics simulation Jupyter Notebook Peter Norvig](http://nbviewer.jupyter.org/url/norvig.com/ipython/Economics.ipynb)



[Markdown Cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)



[Using Interact — Jupyter Widgets 7.0.3 documentation](http://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html)

 

[Pixie - visual Python debugger for Jupyter notebook](https://medium.com/ibm-watson-data-lab/the-visual-python-debugger-for-jupyter-notebooks-youve-always-wanted-761713babc62)



### Matplotlib, Seaborn, Visualization



 [color example code: colormaps_reference.py — Matplotlib 2.0.2 documentation](https://matplotlib.org/examples/color/colormaps_reference.html)



 [ggplot | Home](http://ggplot.yhathq.com/)



 [Matplotlib 1.5.1](http://matplotlib.org/1.5.1/index.html)



 [Matplotlib Plotting commands summary —](http://matplotlib.org/1.5.1/api/pyplot_summary.html)



 [Matplotlib tutorial](http://www.labri.fr/perso/nrougier/teaching/matplotlib/)



 [Seaborn tutorial — seaborn 0.7.1 documentation](http://seaborn.pydata.org/tutorial.html)



### MOOC courses



[Github/jmportilla/Complete-Python-Bootcamp: Lectures](https://github.com/jmportilla/Complete-Python-Bootcamp)



 [Jupyter Notebook - Udemy Complete Python Bootcamp course](http://nbviewer.jupyter.org/github/jmportilla/Complete-Python-Bootcamp/tree/master/)



 [Python for Data Science and Machine Learning Bootcamp | Udemy](https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp/learn/v4/overview)



 [Computational Science and Engineering I | Mathematics | MIT OpenCourseWare](https://ocw.mit.edu/courses/mathematics/18-085-computational-science-and-engineering-i-fall-2008/index.htm)



 [Foundations of Machine Learning (A course by Bloomberg)](https://www.techleer.com/articles/536-foundations-of-machine-learning-a-course-by-bloomberg/)



### NumPy and SciPy

 

 [Linear algebra (numpy.linalg) — NumPy v1.12 Manual](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html)



 [NumPy v1.12 Universal functions](https://docs.scipy.org/doc/numpy/reference/ufuncs.html)



 [NumPy v1.13.dev0 Manual](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html)



 [Random sampling (numpy.random) — NumPy v1.13 Manual](https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html)



 [SciPy — SciPy v0.19.0 Reference Guide](https://docs.scipy.org/doc/scipy/reference/?v=20170402183812)



 [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/#id7)



 [numpy-100/100 Numpy exercises with hint.md at master · rougier/numpy-100](https://github.com/rougier/numpy-100/blob/master/100%20Numpy%20exercises%20with%20hint.md)



### Pandas



 [Pandas 0.20.3 documentation](http://pandas.pydata.org/pandas-docs/stable/)



 [Pandas: Python Data Analysis Library](http://pandas.pydata.org/)



### Setup, PyPi, Creating your own packages

 [Home | Read the Docs](https://readthedocs.org/)



 [How to publish your own Python Package on PyPi – freeCodeCamp](https://medium.freecodecamp.org/how-to-publish-a-pyton-package-on-pypi-a89e9522ce24)

 

 [Step-by-Step Guide to Creating R and Python Libraries (in JupyterLab)](https://towardsdatascience.com/step-by-step-guide-to-creating-r-and-python-libraries-e81bbea87911)



 [How to submit a package to PyPI — Peter Downs](http://peterdowns.com/posts/first-time-with-pypi.html)



 [Packaging and Distributing Projects — Python Packaging User Guide](https://packaging.python.org/tutorials/distributing-packages/#setup-args)



 [reStructuredText Primer — Sphinx 1.8.0+ documentation](http://www.sphinx-doc.org/en/master/rest.html#rst-primer)



 [Using TestPyPI — Python Packaging User Guide](https://packaging.python.org/guides/using-testpypi/)



 [How to open source your Python library | Opensource.com](https://opensource.com/article/18/12/tips-open-sourcing-python-libraries?utm_medium=Email&utm_campaign=weekly&sc_cid=701f2000000RRCwAAO)



### Spark and AWS



 [Amazon Web Services (AWS) - Cloud Computing Services](https://aws.amazon.com/)



 [Connecting to Your Linux Instance from Windows Using PuTTY - Amazon Elastic Compute Cloud](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/putty.html)



 [Install Spark on Windows (PySpark) – Michael Galarnyk – Medium](https://medium.com/@GalarnykMichael/install-spark-on-windows-pyspark-4498a5d8d66c)



### Projects

[10 Steps to Set Up Your Python Project for Success](https://towardsdatascience.com/10-steps-to-set-up-your-python-project-for-success-14ff88b5d13)



### Tools and Utilities



 [itertools — Functions creating iterators for efficient looping — Python 3.6.3 documentation](https://docs.python.org/3/library/itertools.html)



### Web Data Analytics



 [Processing XML in Python with ElementTree - Eli Bendersky's website](https://eli.thegreenplace.net/2012/03/15/processing-xml-in-python-with-elementtree)



 [Using BeautifulSoup to parse HTML and extract press briefings URLs | Computational Journalism, Spring 2016](http://www.compjour.org/warmups/govt-text-releases/intro-to-bs4-lxml-parsing-wh-press-briefings/)



 [28 Jupyter Notebook tips, tricks and shortcuts](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/)



 [A curated list of awesome Python frameworks, libraries, software and resources](https://github.com/vinta/awesome-python)



 [Archived Problems - Project Euler](https://projecteuler.net/archives)



 [Choosing the right estimator — scikit-learn 0.18.1 documentation](http://scikit-learn.org/stable/tutorial/machine_learning_map/)



 [CodeSkulptor](http://www.codeskulptor.org/)



 [CodeSkulptor](http://py3.codeskulptor.org/)



 [Installing XGBoost For Anaconda on Windows (IT Best Kept Secret Is Optimization)](https://www.ibm.com/developerworks/community/blogs/jfp/entry/Installing_XGBoost_For_Anaconda_on_Windows?lang=en)



 [Pandas 0.20.3 - API Reference](http://pandas.pydata.org/pandas-docs/stable/api.html)



 [Pandas 0.20.3 Cookbook](http://pandas.pydata.org/pandas-docs/stable/cookbook.html)



 [PostgreSQL + Python | Psycopg](http://initd.org/psycopg/)



 [Problems - CodeAbbey](http://www.codeabbey.com/index/task_list)



 [Project Jupyter | Home](http://jupyter.org/)



 [PY4E - Python for Everybody](https://www.py4e.com/)



 [Python 2.7.13 documentation](https://docs.python.org/2/tutorial/index.html)



 [Python Conquers The Universe | Adventures across space and time with the Python programming language](https://pythonconquerstheuniverse.wordpress.com/)



 [Python Flask From Scratch - YouTube](https://www.youtube.com/playlist?list=PLillGF-RfqbbbPz6GSEM9hLQObuQjNoj_)



 [Python Tricks 101 – Hacker Noon](https://hackernoon.com/python-tricks-101-2836251922e0)



 [Python tutorial - TutorialsPoint](https://www.tutorialspoint.com/python/index.htm)



 [Regular Expressions for Data Scientists](https://www.dataquest.io/blog/regular-expressions-data-scientists/)



 [Simple Linear Regression Analysis - ReliaWiki](http://reliawiki.org/index.php/Simple_Linear_Regression_Analysis#Fitted_Regression_Line)



 [Introduction — Python 101 1.0 documentation](https://python101.pythonlibrary.org/intro.html)



 [Documenting Python Code: A Complete Guide – Real Python](https://realpython.com/documenting-python-code/)



 [MIT AI: Python (Guido van Rossum) - YouTube](https://www.youtube.com/watch?v=ghwaIiE3Nd8&feature=youtu.be&fbclid=IwAR1xgzx1qvHu9b8YweZDO9s4868iH27gPPlBWlPiNA9IjxLbNjjwEOCghxo)



 [Python IDEs and Code Editors (Guide) – Real Python](https://realpython.com/python-ides-code-editors-guide/)

 

 [Advanced Python web scraping tricks and tips](https://www.codementor.io/blog/python-web-scraping-63l2v9sf2q)



## R related



 [A Beginner’s Guide to Neural Networks with R](http://www.kdnuggets.com/2016/08/begineers-guide-neural-networks-r.html)

 

 [A Comprehensive Guide to Data Visualisation in R for Beginners](https://towardsdatascience.com/a-guide-to-data-visualisation-in-r-for-beginners-ef6d41a34174)



 [An R Introduction to Statistics | R Tutorial](http://www.r-tutor.com/)



 [Data Manipulation with dplyr | R-bloggers](https://www.r-bloggers.com/data-manipulation-with-dplyr/)



 [Data Science and Machine Learning Bootcamp with R | Udemy](https://www.udemy.com/data-science-and-machine-learning-bootcamp-with-r/learn/v4/overview)

 

 [Explore R | Discover libraries, source code, top authors, trending discussions | kandi](https://kandi.openweaver.com/explore/r)

 

 [ggplot2-cheatsheet.pdf](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)



 [Machine Learning A-Z™: Download Practice Datasets - SuperDataScience - Big Data | Analytics Careers | Mentors | Success](https://www.superdatascience.com/machine-learning/)



 [Quick-R: Home Page](http://www.statmethods.net/index.html)



 [R mailing lists archive](http://tolstoy.newcastle.edu.au/R/)



 [R Tutorial Series - Statistical Tests | Saranya Anandh | Pulse | LinkedIn](https://www.linkedin.com/pulse/r-tutorial-series-statistical-tests-saranya-anandh?trk=v-feed&lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BDlqO%2B6C4r4CsNhYU4n9rsw%3D%3D)



 [R: Control for Rpart Fits](https://stat.ethz.ch/R-manual/R-devel/library/rpart/html/rpart.control.html)



 [R: Recursive Partitioning and Regression Trees](https://stat.ethz.ch/R-manual/R-devel/library/rpart/html/rpart.html)



 [Short-refcard.pdf](https://cran.r-project.org/doc/contrib/Short-refcard.pdf)



 [Theme • ggplot2](http://ggplot2.tidyverse.org/reference/theme.html)