{"id":14958228,"url":"https://github.com/tirthajyoti/data-science-best-resources","last_synced_at":"2025-05-14T19:07:33.784Z","repository":{"id":40356560,"uuid":"163244728","full_name":"tirthajyoti/Data-science-best-resources","owner":"tirthajyoti","description":"Carefully curated resource links for data science in one place","archived":false,"fork":false,"pushed_at":"2024-08-17T07:00:25.000Z","size":9369,"stargazers_count":3038,"open_issues_count":10,"forks_count":1001,"subscribers_count":120,"default_branch":"master","last_synced_at":"2025-04-10T04:54:19.779Z","etag":null,"topics":["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"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/tirthajyoti.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-12-27T03:51:46.000Z","updated_at":"2025-04-10T03:47:07.000Z","dependencies_parsed_at":"2023-01-30T17:01:34.744Z","dependency_job_id":"4a25bfd6-6479-4225-9603-f95f2598b293","html_url":"https://github.com/tirthajyoti/Data-science-best-resources","commit_stats":{"total_commits":108,"total_committers":5,"mean_commits":21.6,"dds":0.03703703703703709,"last_synced_commit":"1100c9b1ad9ac36fe19b34597bee73feccbecb3d"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tirthajyoti%2FData-science-best-resources","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tirthajyoti%2FData-science-best-resources/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tirthajyoti%2FData-science-best-resources/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/tirthajyoti%2FData-science-best-resources/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/tirthajyoti","download_url":"https://codeload.github.com/tirthajyoti/Data-science-best-resources/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254209859,"owners_count":22032897,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["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"],"created_at":"2024-09-24T13:16:32.443Z","updated_at":"2025-05-14T19:07:31.572Z","avatar_url":"https://github.com/tirthajyoti.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"![tdsp](https://raw.githubusercontent.com/tirthajyoti/Data-science-best-resources/master/images/tdsp-lifecycle2.png)\n\n# Data Science Collected Resources\nA trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place.\n\n### 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.\n\n### Please visit my [Medium profile](https://medium.com/@tirthajyoti) to check out all of my data science articles.\n\n### Please check this [Github Repo for all my Tutorial-style Machine Learning Jupyter notebooks](https://github.com/tirthajyoti/Machine-Learning-with-Python) \n\n---\n\n## Artificial Intelligence related\n\n[MONTRÉAL.AI ACADEMY: ARTIFICIAL INTELLIGENCE 101 FIRST WORLD-CLASS OVERVIEW OF AI FOR ALL](http://www.montreal.ai/ai4all.pdf)\n\n [OpenAI blog](https://blog.openai.com/)\n\n[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)\u003c/dt\u003e\n\n [AITopics](https://aitopics.org/search)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Is The Singularity Coming? – Arc Digital](https://arcdigital.media/is-the-singularity-coming-ef8580d4ce97)\u003c/dt\u003e\n\n [Michael I. Jordan NYSE Machine Learning Presentation](https://www.youtube.com/watch?time_continue=2\u0026v=17cp8PLKvOc)\u003c/dt\u003e\n\n [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\u0026utm_term=.c3ac6321c831)\u003c/dt\u003e\n\n [The Four Waves of A.I. | LinkedIn](https://www.linkedin.com/pulse/four-waves-ai-kai-fu-lee/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n## AWS related\n\n [Amazon CloudWatch - Application and Infrastructure Monitoring](https://aws.amazon.com/cloudwatch/)\n\n [Amazon DynamoDB - Overview](https://aws.amazon.com/dynamodb/)\n\n [Amazon Elastic Block Store (EBS) - Amazon Web Services](https://aws.amazon.com/ebs/)\n\n [Amazon Elastic File System (EFS) | Cloud File Storage](https://aws.amazon.com/efs/)\n\n [AWS Concepts: Understanding AWS - YouTube](https://www.youtube.com/watch?v=qcY-uiEHhn0)\n\n [AWS Concepts: Understanding the Course Material \u0026 Features - YouTube](https://www.youtube.com/watch?v=LKStwibxbR0\u0026list=PLv2a_5pNAko2Jl4Ks7V428ttvy-Fj4NKU)\n\n [AWS In 10 Minutes | AWS Tutorial For Beginners | AWS Training Video | AWS Tutorial | Simplilearn - YouTube](https://www.youtube.com/watch?v=r4YIdn2eTm4)\n\n [AWS re:Invent 2017: Building production apps easily with Amazon Lightsail (CMP212) - YouTube](https://www.youtube.com/watch?v=29_LqYnomdg)\n\n [Classless Inter-Domain Routing - Wikipedia](https://en.wikipedia.org/wiki/Classless_Inter-Domain_Routing)\n\n [Cloud Compute Products – Amazon Web Services (AWS)](https://aws.amazon.com/products/compute/)\n\n [Cloud Object Storage | Store \u0026 Retrieve Data Anywhere | Amazon Simple Storage Service](https://aws.amazon.com/s3/)\n\n [Elastic Load Balancing - Amazon Web Services](https://aws.amazon.com/elasticloadbalancing/)\n\n [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)\n\n [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)\n\n [What is Cloud Computing? - Amazon Web Services](https://aws.amazon.com/what-is-cloud-computing/)\n \n## Blogs, StacksExchanges\n\n[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)\n\n[Reducing the Need for Labeled Data in Generative Adversarial Networks](https://ai.googleblog.com/2019/03/reducing-need-for-labeled-data-in.html)\n\n[Jason's Google ML 101 deck](https://docs.google.com/presentation/d/1kSuQyW5DTnkVaZEjGYCkfOxvzCqGEFzWBy4e9Uedd9k/edit)\n\n [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\u0026utm_medium=social\u0026utm_source=linkedin.com\u0026utm_campaign=buffer)\n\n [Advice to aspiring data scientists: start a blog – Variance Explained](http://varianceexplained.org/r/start-blog/)\n\n [Brandon Roher Blog](https://brohrer.github.io/blog.html)\n\n [Chris Albon - Data Science, Machine Learning, and Artificial Intelligence](https://chrisalbon.com/#Python)\n\n [Data Science Stack Exchange](http://datascience.stackexchange.com/)\n\n [Data Skeptic](https://dataskeptic.com/)\n\n [DataTau](http://www.datatau.com/)\n \n [explained.ai - Deep explanations of machine learning and related topics](https://explained.ai/)\n\n [FlowingData](http://flowingdata.com/)\n\n [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\u0026utm_medium=social\u0026utm_source=twitter.com\u0026utm_campaign=buffer)\n\n [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)\n\n [Learn Data Science - Infographic (article) - DataCamp](https://www.datacamp.com/community/tutorials/learn-data-science-infographic)\n\n [LIGO Gravity Wave GW150914_tutorial](https://losc.ligo.org/s/events/GW150914/GW150914_tutorial.html)\n\n [O.R. \u0026 Analytics Success Stories - INFORMS](https://www.informs.org/Impact/O.R.-Analytics-Success-Stories)\n\n [OpenAI Blog](https://blog.openai.com/)\n\n [Paul Ford: What Is Code? | Bloomberg](https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/)\n\n [Science Isn’t Broken | FiveThirtyEight](https://fivethirtyeight.com/features/science-isnt-broken/#part1)\n\n [Scientifically Sound](https://scientificallysound.org/)\n\n [AIspace](http://aispace.org/)\n\n [Top 28 Cheat Sheets for Machine Learning, Data Science, Probability, SQL \u0026 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\u0026utm_medium=social\u0026utm_source=linkedin.com\u0026utm_campaign=buffer)\n\n [GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows](https://www.kdnuggets.com/2018/08/github-python-data-science-spotlight.html)\n\n## Books, Courses, Repos\n\n [Solved end-to-end Data Science projects](https://www.dezyre.com/projects/data-science-projects)\n \n [Dive into Deep Learning (An interactive deep learning book with code, math, and discussions)](https://d2l.ai/index.html)\n\n [Machine Learning Math book](https://mml-book.github.io/)\n\n [Learn to code | Codecademy](https://www.codecademy.com/)\n\n [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/)\n\n [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)\n \n [Feature Engineering and Selection: A Practical Approach for Predictive Models](http://www.feat.engineering/)\n \n [Nerual Networks and Deep Learning - an online book](neuralnetworksanddeeplearning.com)\n \n\n## Git and Github\n\n[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/)\n\n [An Intro to Git and GitHub for Beginners (Tutorial)](https://product.hubspot.com/blog/git-and-github-tutorial-for-beginners)\u003c/dt\u003e\n\n [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)\n\n [Git - Book](https://git-scm.com/book/en/v2)\n\n [git - the simple guide - no deep shit!](http://rogerdudler.github.io/git-guide/)\n\n [How not to be afraid of GIT anymore – freeCodeCamp.org](https://medium.freecodecamp.org/how-not-to-be-afraid-of-git-anymore-fe1da7415286)\n\n [joshnh/Git-Commands: A list of commonly used Git commands](https://github.com/joshnh/Git-Commands)\n\n [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/)\n\n [Understanding the GitHub Flow · GitHub Guides](https://guides.github.com/introduction/flow/)\n\n## Interesting Articles\n \n [Towards an anti-fascist AI (from opendemocracy.net)](https://www.opendemocracy.net/en/digitaliberties/towards-anti-fascist-ai/)\n \n [Becoming a Level 3.0 Data Scientist](https://www.kdnuggets.com/2019/05/becoming-a-level-3-data-scientist.html)\n\n [The Third-wave of Data Scientist](https://towardsdatascience.com/the-third-wave-data-scientist-1421df7433c9)\n \n [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)\n\n [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\u0026utm_medium=social\u0026utm_source=facebook.com\u0026utm_campaign=buffer)\n\n [Edward Witten Ponders the Nature of Reality | Quanta Magazine](https://www.quantamagazine.org/edward-witten-ponders-the-nature-of-reality-20171128/)\n\n   [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/)\n   \n  [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)\n   \n [General Thinking Tools: 9 Mental Models to Solve Difficult Problems](https://www.fs.blog/general-thinking-tools/)\n\n [How Social Media Endangers Knowledge | WIRED](https://www.wired.com/story/wikipedias-fate-shows-how-the-web-endangers-knowledge/)\n\n [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\u0026utm_source=Facebook\u0026utm_medium=Owned+Social)\n\n [Machine Learning’s ‘Amazing’ Ability to Predict Chaos | Quanta Magazine](https://www.quantamagazine.org/machine-learnings-amazing-ability-to-predict-chaos-20180418/)\n\n [New Brain Maps With Unmatched Detail May Change Neuroscience | WIRED](https://www.wired.com/story/new-brain-maps-with-unmatched-detail-may-change-neuroscience/)\n\n [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)\n\n [Quantum Leaps in Quantum Computing? - Scientific American](https://www.scientificamerican.com/article/quantum-leaps-in-quantum-computing/?utm_source=facebook\u0026utm_medium=social\u0026utm_campaign=sa-editorial-social\u0026utm_content\u0026utm_term=physics_sa-magazine_text_free)\n\n [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)\n\n [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\u0026utm_source=linkedin\u0026utm_medium=social)\n\n [The Quantum Thermodynamics Revolution | Quanta Magazine](https://www.quantamagazine.org/the-quantum-thermodynamics-revolution-20170502?utm_content=buffere2607\u0026utm_medium=social\u0026utm_source=facebook.com\u0026utm_campaign=buffer)\n\n [What Is Code? | Paul Ford| Bloomberg](https://www.bloomberg.com/graphics/2015-paul-ford-what-is-code/)\n\n [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)\n\n [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)\n\n [Machine Learning Confronts the Elephant in the Room | Quanta Magazine](https://www.quantamagazine.org/machine-learning-confronts-the-elephant-in-the-room-20180920/)\n\n## MOOC related\n\n[Complete lecture notes of the Stanford/Coursera Machine Learning class by Andrew Ng](http://www.holehouse.org/mlclass/)\n\n[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)\n\n [Artificial Intelligence | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/index.htm)\n\n [Dashboard | MIT Professional Education Digital Programs](https://mitprofessionalx.mit.edu/dashboard)\n\n [Data Science A-Z™: Real-Life Data Science Exercises Included | Udemy](https://www.udemy.com/datascience/)\n\n [Data Science Essentials | edX](https://www.edx.org/course/data-science-essentials-microsoft-dat203-1x-2?source=aw\u0026awc=6798_1489913955_d9818a031ea60b9e133f81baa8e0fcbb\u0026utm_source=aw\u0026utm_medium=affiliate_partner\u0026utm_content=text-link\u0026utm_term=315645_LearnDataSci)\n\n [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)\n\n [I uncovered 1,150+ Coursera courses that are still completely free](https://medium.freecodecamp.org/coursera-free-online-courses-6d84cdb30da)\n\n [Information and Entropy | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-050j-information-and-entropy-spring-2008/index.htm)\n\n [Introduction to Algorithms | MIT OpenCourseWare](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/index.htm)\n\n [Introduction to Data Analysis using Excel | edX](https://www.edx.org/course/introduction-data-analysis-using-excel-microsoft-dat205x-0)\n\n [Introduction to Python for Data Science | edX](https://www.edx.org/course/introduction-python-data-science-microsoft-dat208x-4?source=aw\u0026awc=6798_1489913492_c663da04f25e4339087686b358457f93\u0026utm_source=aw\u0026utm_medium=affiliate_partner\u0026utm_content=text-link\u0026utm_term=315645_LearnDataSci#!)\n\n [Introduction to R for Data Science | edX](https://www.edx.org/course/introduction-r-data-science-microsoft-dat204x-3)\n\n [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)\n\n [Programming with Python for Data Science!](https://courses.edx.org/courses/course-v1:Microsoft+DAT210x+2T2017/info)\n\n [Statistical Thinking for Data Science course](https://courses.edx.org/courses/course-v1:ColumbiaX+DS101X+1T2016/courseware/83b2b74597c44d858f1cd81edef2faf2/9ba7caa9efaf4b86b7521534f9c841d5/)\n\n [Top Data Science Online Courses in 2017 – LearnDataSci](http://www.learndatasci.com/best-data-science-online-courses/)\n\n [U. Wash ML course Jupyter Home](https://hub.coursera-notebooks.org/user/hwxlouxsysrhhnfzzinqjs/tree)\n\n## SQL\n\n [A Visual Explanation of SQL Joins](https://blog.codinghorror.com/a-visual-explanation-of-sql-joins/)\n\n [Join (SQL) - Wikipedia](https://en.wikipedia.org/wiki/Join_(SQL))\n\n [PostgreSQL: Mathematical Functions and Operators](https://www.postgresql.org/docs/9.5/static/functions-math.html)\n\n [PostgreSQL: String Functions and Operators](https://www.postgresql.org/docs/9.5/static/functions-string.html)\n\n [Psycopg2 Tutorial - PostgreSQL with Python](https://wiki.postgresql.org/wiki/Psycopg2_Tutorial)\n\n [SQL Joins Explained](http://www.sql-join.com/)\n\n [The SQL Tutorial for Data Analysis | SQL Tutorial - Mode Analytics](https://community.modeanalytics.com/sql/tutorial/introduction-to-sql/)\n \n  [SQL vs NoSQL or MySQL vs MongoDB - YouTube](https://www.youtube.com/watch?v=ZS_kXvOeQ5Y)\u003c/dt\u003e\n\n [Thinking in SQL vs Thinking in Python](https://blog.modeanalytics.com/learning-python-sql/)\n \n [Kaggle SQL course (including BigQuery topics)](https://www.kaggle.com/learn/sql)\n \n ## Statistics\n \n [Common statistical tests are linear models (or: how to teach stats)](https://lindeloev.github.io/tests-as-linear/)\n \n [Introductory statistics - OpenText Library](https://saylordotorg.github.io/text_introductory-statistics/index.html)\n\n [Common statistical tests are linear models (or: how to teach stats)](https://lindeloev.github.io/tests-as-linear/)\n \n [Background: Markov chains](https://d18ky98rnyall9.cloudfront.net/_adadc80290e52a99b282ca9d7c1a41ee_background_MarkovChains.html)\n\n [OpenIntro Stats](https://www.openintro.org/index.php)\n\n [Regression Analysis Tutorial and Examples | Minitab](http://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-tutorial-and-examples)\u003c/dt\u003e\n\n [The 10 Statistical Techniques Data Scientists Need to Master](https://towardsdatascience.com/the-10-statistical-techniques-data-scientists-need-to-master-1ef6dbd531f7)\n\n [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)\n\n [Thomas Bayes and the crisis in science – TheTLS](https://www.the-tls.co.uk/articles/public/thomas-bayes-science-crisis/)\u003c/dt\u003e\n\n [Welcome to STAT 505! | STAT 505](https://onlinecourses.science.psu.edu/stat505/node/1)\n\n [Introduction to Bayesian Linear Regression – Towards Data Science](https://towardsdatascience.com/introduction-to-bayesian-linear-regression-e66e60791ea7)\n\n [Regression Analysis Tutorial and Examples | Minitab](http://blog.minitab.com/blog/adventures-in-statistics-2/regression-analysis-tutorial-and-examples)\n\n [The 10 Statistical Techniques Data Scientists Need to Master](https://towardsdatascience.com/the-10-statistical-techniques-data-scientists-need-to-master-1ef6dbd531f7)\u003c/dt\u003e\n\n [Welcome to STAT 505! | STAT 505](https://onlinecourses.science.psu.edu/stat505/node/1)\n \n [Probability and Statistics Visually](https://seeing-theory.brown.edu)\n\n## Visualizations (and image processing related)\n\n[The paper describing Scikit-image from its core developers](https://peerj.com/articles/453/)\n\n[Full-screen interactive that lets you explore the first 300 years of Data Visualization](https://infowetrust.com/scroll/)\n\n[designing-great-visualizations.pdf](https://www.tableau.com/sites/default/files/media/designing-great-visualizations.pdf)\n\n[Gallery of Data Visualization - Missed Opportunities and Graphical Failures](http://www.datavis.ca/gallery/missed.php)\n\n [Lesson 1-4, first visualization data - Govind Acharya | Tableau Public](https://public.tableau.com/profile/govind.acharya#!/vizhome/Lesson1-4firstvisualizationdata/Sheet1)\n\n [Mapping the 1854 Cholera Outbreak | Tableau Public](https://public.tableau.com/s/gallery/mapping-1854-cholera-outbreak)\u003c/dt\u003e\n\n [Resources | Tableau Public](https://public.tableau.com/en-us/s/resources)\n\n [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\u0026utm_medium=social\u0026utm_source=linkedin.com\u0026utm_campaign=buffer)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Data Skeptic](https://dataskeptic.com/)\n\n [GGobi data visualization system.](http://www.ggobi.org/)\n\n [GitHub (Tirthajyoti Sarkar)](https://github.com/tirthajyoti)\n\n [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\u0026utm_medium=social\u0026utm_source=twitter.com\u0026utm_campaign=buffer)\n\n [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)\n\n [Learn to code | Codecademy](https://www.codecademy.com/)\n\n [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/)\n\n [Medium – Read, write and share stories that matter](https://medium.com/)\n\n [Scientifically Sound](https://scientificallysound.org/)\n\n [Top 28 Cheat Sheets for Machine Learning, Data Science, Probability, SQL \u0026 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\u0026utm_medium=social\u0026utm_source=linkedin.com\u0026utm_campaign=buffer)\n\n [Learn Data Science - Infographic (article) - DataCamp](https://www.datacamp.com/community/tutorials/learn-data-science-infographic)\n\n [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)\n\n ## Neural Network\n\n### Videos\n\n[Deep blueberry](https://mithi.github.io/deep-blueberry/ch0-introduction.html)\n\n [Brandon Rohrer - Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)](https://www.youtube.com/watch?v=WCUNPb-5EYI)\u003c/dt\u003e\n\n [CS231n Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM - YouTube](https://www.youtube.com/watch?v=iX5V1WpxxkY)\u003c/dt\u003e\n\n [Nuts and Bolts of Applying Deep Learning (Andrew Ng) - YouTube](https://www.youtube.com/watch?v=F1ka6a13S9I)\u003c/dt\u003e\n\n [Siraj Raval - LSTM Networks - The Math of Intelligence (Week 8) - YouTube](https://www.youtube.com/watch?v=9zhrxE5PQgY)\u003c/dt\u003e\n\n [Siraj Raval - Recurrent Neural Networks - The Math of Intelligence (Week 5) - YouTube](https://www.youtube.com/watch?v=BwmddtPFWtA)\n\n [Andrew Ng: Artificial Intelligence is the New Electricity - YouTube](https://www.youtube.com/watch?v=21EiKfQYZXc)\u003c/dt\u003e\n\n [A Neural Network Playground](http://playground.tensorflow.org/#activation=tanh\u0026batchSize=10\u0026dataset=circle\u0026regDataset=reg-plane\u0026learningRate=0.03\u0026regularizationRate=0\u0026noise=0\u0026networkShape=4,2\u0026seed=0.53044\u0026showTestData=false\u0026discretize=false\u0026percTrainData=50\u0026x=true\u0026y=true\u0026xTimesY=false\u0026xSquared=false\u0026ySquared=false\u0026cosX=false\u0026sinX=false\u0026cosY=false\u0026sinY=false\u0026collectStats=false\u0026problem=classification\u0026initZero=false\u0026hideText=false)\u003c/dt\u003e\n\n [But what *is* a Neural Network? | Deep learning, chapter 1](https://www.youtube.com/watch?time_continue=80\u0026v=aircAruvnKk)\n\n [Convolutional Networks in Java - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https://deeplearning4j.org/convolutionalnets.html)\u003c/dt\u003e\n\n [CS231n Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/convolutional-networks/)\n\n [Deep Learning Fundamentals - Cognitive Class](https://cognitiveclass.ai/courses/introduction-deep-learning/?utm_content=buffer3ab0d\u0026utm_medium=social\u0026utm_source=facebook.com\u0026utm_campaign=buffer)\n\n [Exploring LSTMs](http://blog.echen.me/2017/05/30/exploring-lstms/)\n\n [Feature Visualization](https://distill.pub/2017/feature-visualization/)\n\n [Neural networks and deep learning](http://neuralnetworksanddeeplearning.com/)\n\n [Understanding Hinton’s Capsule Networks. Part I: Intuition.](https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b)\n\n [Understanding LSTM Networks -- colah's blog](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)\n\n [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)\n\n [Andrej Carpathy blog - Hacker's guide to Neural Networks](http://karpathy.github.io/neuralnets/)\n\n [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)\u003c/dt\u003e\n\n [J Alammar – Explorations in touchable pixels and intelligent androids](http://jalammar.github.io/)\n\n### Keras\n\n [Guide to the Sequential model - Keras Documentation](https://keras.io/getting-started/sequential-model-guide/)\n\n [Keras Documentation](https://keras.io/)\n\n [How to Use Word Embedding Layers for Deep Learning with Keras - Machine Learning Mastery](https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/)\n \n### TensorFlow\n\n [Building Input Functions with tf.estimator  |  TensorFlow](https://www.tensorflow.org/get_started/input_fn)\n\n [Getting Started With TensorFlow  |  TensorFlow](https://www.tensorflow.org/get_started/get_started)\n\n [Installing TensorFlow on Windows  |  TensorFlow](https://www.tensorflow.org/install/install_windows)\n\n [TensorFlow](https://www.tensorflow.org/)\n\n [TensorFlow Linear Model Tutorial  |  TensorFlow](https://www.tensorflow.org/tutorials/wide)\n\n [TensorFlow Wide \u0026 Deep Learning Tutorial  |  TensorFlow](https://www.tensorflow.org/tutorials/wide_and_deep)\n\n [Using TensorFlow in Windows with a GPU | Heaton Research](http://www.heatonresearch.com/2017/01/01/tensorflow-windows-gpu.html)\u003c/dt\u003e\n\n [Installation Guide Windows :: CUDA Toolkit Documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/)\n\n [7 Steps to Mastering Machine Learning With Python](https://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html)\u003c/dt\u003e\n\n [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\u003c/dt\u003e\n\n [Berkeley AI Materials](http://ai.berkeley.edu/lecture_videos.html)\u003c/dt\u003e\n\n [Deep Learning For Coders fast.ai](http://course.fast.ai/)\u003c/dt\u003e\n\n [Lecture Collection | Machine Learning - Stanford course](https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599)\u003c/dt\u003e\n\n [Microsoft Azure ML Cheat sheet](https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice)\n\n [Pedro Domigos Machine Learning lectures](https://www.youtube.com/user/UWCSE/playlists?shelf_id=16\u0026sort=dd\u0026view=50)\n\n [The Hitchhiker’s Guide to Machine Learning in Python](https://medium.com/@conordewey3/the-hitchhikers-guide-to-machine-learning-algorithms-in-python-bfad66adb378)\u003c/dt\u003e\n\n [Top 10 Machine Learning Projects on Github](http://www.kdnuggets.com/2015/12/top-10-machine-learning-github.html)\n\n [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)\u003c/dt\u003e\n\n [ISLR class videos](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/\n\n [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)\u003c/dt\u003e\n\n [GOOGLE - Rules of Machine Learning:  |  Machine Learning Rules  |  Google Developers](https://developers.google.com/machine-learning/rules-of-ml/)\n\n [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)\u003c/dt\u003e\n\n [Python Generators Tutorial](https://www.dataquest.io/blog/python-generators-tutorial/)\u003c/dt\u003e\n\n [R Markdown: The Definitive Guide](https://bookdown.org/yihui/rmarkdown/)\u003c/dt\u003e\n\n [Understanding the GitHub Flow · GitHub Guides](https://guides.github.com/introduction/flow/)\u003c/dt\u003e\n\n [How to Prepare for a Machine Learning Interview - Semantic Bits](https://semanti.ca/blog/?how-to-prepare-for-a-machine-learning-interview)\u003c/dt\u003e\n\n [Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning \u0026 Big Data](https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n## Apache Spark\n\n [Building A Linear Regression with PySpark and MLlib](https://towardsdatascience.com/building-a-linear-regression-with-pyspark-and-mllib-d065c3ba246a)\u003c/dt\u003e\n\n [Complete Guide on DataFrame Operations in PySpark](https://www.analyticsvidhya.com/blog/2016/10/spark-dataframe-and-operations/)\u003c/dt\u003e\n\n [Install_Spark_on_Windows10.pdf](https://www.ics.uci.edu/~shantas/Install_Spark_on_Windows10.pdf)\u003c/dt\u003e\n\n [Introduction · Mastering Apache Spark](https://jaceklaskowski.gitbooks.io/mastering-apache-spark/content/)\u003c/dt\u003e\n\n [MLlib: Main Guide - Spark 2.3.1 Documentation](http://spark.apache.org/docs/latest/ml-guide.html)\u003c/dt\u003e\n\n [Overview - Spark 2.3.1 Documentation](https://spark.apache.org/docs/latest/)\u003c/dt\u003e\n\n [RDD Programming Guide - Spark 2.3.1 Documentation](https://spark.apache.org/docs/latest/rdd-programming-guide.html)\u003c/dt\u003e\n\n [rdflib 5.0.0-dev — rdflib 5.0.0-dev documentation](https://rdflib3.readthedocs.io/en/latest/index.html)\u003c/dt\u003e\n\n [Spark SQL and DataFrames - Spark 2.3.1 Documentation](http://spark.apache.org/docs/latest/sql-programming-guide.html)\u003c/dt\u003e\n\n [Welcome to Spark Python API Docs! — PySpark 2.3.1 documentation](http://spark.apache.org/docs/latest/api/python/)\u003c/dt\u003e\n\n## Cloud computing\n\n [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/)\n \n [Cloud Computing Tutorial for Beginners | Cloud Computing Explained | Cloud Computing | Simplilearn - YouTube](https://www.youtube.com/watch?v=RWgW-CgdIk0)\u003c/dt\u003e\n\n### Computation, Computing\n\n [A Short Guide to Hard Problems | Quanta Magazine](https://www.quantamagazine.org/a-short-guide-to-hard-problems-20180716/?fbclid=IwAR2Yz76T1uE835BC7STAdIZUA-xR4cPUI2BeC-yS7Bwkk96fUPOePeyNCZg)\u003c/dt\u003e\n\n\n### Data Mining\n\n [The 10 Mining Techniques Data Scientists Need for Their Toolbox](https://towardsdatascience.com/the-10-mining-techniques-data-scientists-need-for-their-toolbox-ae15a5733b02)\u003c/dt\u003e\n\n [Wikipedia Data Science: Working with the World’s Largest Encyclopedia](https://towardsdatascience.com/wikipedia-data-science-working-with-the-worlds-largest-encyclopedia-c08efbac5f5c)\u003c/dt\u003e\n\n\n### Data wrangling related\n\n  [A Brief Overview of Outlier Detection Techniques – Towards Data Science](https://towardsdatascience.com/a-brief-overview-of-outlier-detection-techniques-1e0b2c19e561)\u003c/dt\u003e\n\n## Docker, Containers\n\n [A Beginner-Friendly Introduction to Containers, VMs and Docker](https://medium.freecodecamp.org/a-beginner-friendly-introduction-to-containers-vms-and-docker-79a9e3e119b)\u003c/dt\u003e\n\n [A fast and easy Docker tutorial for beginners (video series)](https://medium.freecodecamp.org/docker-quick-start-video-tutorials-1dfc575522a0)\u003c/dt\u003e\n\n [Docker Compose in 12 Minutes - YouTube](https://www.youtube.com/watch?v=Qw9zlE3t8Ko)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Learn Docker in 12 Minutes 🐳 - YouTube](https://www.youtube.com/watch?v=YFl2mCHdv24)\u003c/dt\u003e\n\n [What is a Container? - YouTube](https://www.youtube.com/watch?v=EnJ7qX9fkcU)\u003c/dt\u003e\n\n [What is Docker | Docker Tutorial for Beginners | Docker Container | DevOps Tools | Edureka - YouTube](https://www.youtube.com/watch?v=lcQfQRDAMpQ)\u003c/dt\u003e\n\n [Building Your Own Data Science Platform With Python \u0026 Docker - YouTube](https://www.youtube.com/watch?v=NC2wXYHBrL0)\u003c/dt\u003e\n\n### Interview related\n\n [50+ Data Structure and Algorithms Interview Questions for Programmers](https://hackernoon.com/50-data-structure-and-algorithms-interview-questions-for-programmers-b4b1ac61f5b0)\u003c/dt\u003e\n\n\n## Web Technologies\n\n### REST, API, Microservice\n\n  [GraphQL vs. REST – Apollo GraphQL](https://blog.apollographql.com/graphql-vs-rest-5d425123e34b)\u003c/dt\u003e\n\n [Microservices, APIs, and Swagger: How They Fit Together | Swagger](https://swagger.io/blog/api-strategy/microservices-apis-and-swagger/)\u003c/dt\u003e\n\n [REST API concepts and examples - YouTube](https://www.youtube.com/watch?v=7YcW25PHnAA)\u003c/dt\u003e\n\n [Web Architecture 101 – VideoBlocks Product \u0026 Engineering](https://engineering.videoblocks.com/web-architecture-101-a3224e126947)\u003c/dt\u003e\n\n [REST API \u0026 RESTful Web Services Explained - YouTube](https://www.youtube.com/watch?v=LooL6_chvN4)\u003c/dt\u003e\n\n [Our Collections – Towards Data Science](https://towardsdatascience.com/our-collections-3920888f831c)\u003c/dt\u003e\n\n### JSON, XML, HTML\n\n[JSON Crash Course - YouTube](https://www.youtube.com/watch?v=wI1CWzNtE-M)\n[Can I use... Support tables for HTML5, CSS3, etc](https://caniuse.com/)\n[HTML5 Form Validation Examples \u003c HTML | The Art of Web](http://www.the-art-of-web.com/html/html5-form-validation/)\n \n### CSS\n\n [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)\n \n [Creating a Simple Website with HTML and CSS - Part 1 - YouTube](https://www.youtube.com/watch?v=A3Xgz9PHGuA)\u003c/dt\u003e\n\n [CSS Introduction - W3Schools](https://www.w3schools.com/css/css_intro.asp)\u003c/dt\u003e\n\n [Learn CSS in 12 Minutes - YouTube](https://www.youtube.com/watch?v=0afZj1G0BIE)\u003c/dt\u003e\n\n### JavaScript\n\n [Beginner JavaScript Tutorial - 1 - Introduction to JavaScript - YouTube](https://www.youtube.com/watch?v=yQaAGmHNn9s)\u003c/dt\u003e\n\n [Eloquent JavaScript](http://eloquentjavascript.net/) \n\n [Form Validation with JavaScript - Check for an Empty Text Field - YouTube](https://www.youtube.com/watch?v=Pc2e2YpKArg) \n\n [JavaScript Basics Part 1](https://www.htmlgoodies.com/primers/jsp/article.php/3586411) \n\n [JavaScript beginner tutorial 30 - form validation text boxes and passwords - YouTube](https://www.youtube.com/watch?v=y5UEXujzSag) \n\n [JavaScript: Simple Form Validation - YouTube](https://www.youtube.com/watch?v=_Z-0cwONN6c) \n\n [Learn JavaScript in 12 Minutes - YouTube](https://www.youtube.com/watch?v=Ukg_U3CnJWI) \n\n [Machine Learning with JavaScript : Part 1 – Hacker Noon](https://hackernoon.com/machine-learning-with-javascript-part-1-9b97f3ed4fe5) \n\n [Machine Learning with JavaScript : Part 2 – Hacker Noon](https://hackernoon.com/machine-learning-with-javascript-part-2-da994c17d483) \n\n [W3School - JavaScript Form Validation](https://www.w3schools.com/js/js_validation.asp) \n\n [W3schools - JavaScript Tutorial](https://www.w3schools.com/js/) \n\n [ClearlyDecoded.com - Yaakov Chaikin](https://clearlydecoded.com/) \n\n [GoDaddy Hosting Account Getting Started Guide](https://www.godaddy.com/help/hosting-account-getting-started-guide-1361) \n\n [How to Make A Website in 2018 - Web Hosting Guide | WHSR](https://www.webhostingsecretrevealed.net/web-hosting-beginner-guide/) \n\n [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)\n \n [Free JavaScript Tutorial - Scaler](https://www.scaler.com/topics/javascript/) \n\n## LaTeX, Markdown, reST\n\n [Art of Problem Solving - LaTeX symbols](https://artofproblemsolving.com/wiki/index.php/LaTeX:Symbols)\u003c/dt\u003e\n\n [Detexify LaTeX handwritten symbol recognition](http://detexify.kirelabs.org/classify.html)\u003c/dt\u003e\n\n [http://quicklatex.com/](http://quicklatex.com/)\u003c/dt\u003e\n \n [LaTeX symbol Wiki](https://oeis.org/wiki/List_of_LaTeX_mathematical_symbols#Set_and.2For_logic_notation)\n\n [The Comprehensive LaTeX Symbol ListThe Comprehensive LaTeX Symbol List - symbols-a4.pdf](http://ctan.math.illinois.edu/info/symbols/comprehensive/symbols-a4.pdf)\u003c/dt\u003e\n\n [Pandoc - Pandoc User’s Guide](https://pandoc.org/MANUAL.html#pandocs-markdown)\u003c/dt\u003e\n\n [MathJax Documentation — MathJax 2.7 documentation](http://docs.mathjax.org/en/latest/)\u003c/dt\u003e\n\n [TeX Commands available in MathJax](http://www.onemathematicalcat.org/MathJaxDocumentation/TeXSyntax.htm)\u003c/dt\u003e\n\n## Linux, OS\n \n [How to Install Ubuntu Linux on VirtualBox on Windows 10 [Step by Step Guide] | It's FOSS](https://itsfoss.com/install-linux-in-virtualbox/)\u003c/dt\u003e\n\n [Microsoft PowerShell Tutorial \u0026 Training Course – Microsoft Virtual Academy](https://mva.microsoft.com/en-us/training-courses/getting-started-with-microsoft-powershell-8276?l=r54IrOWy_2304984382)\u003c/dt\u003e\n\n [Most Popular Linux Distributions and Why They Dominate the Market](https://blog.storagecraft.com/popular-linux-distributions-dominate-market/)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [[Solved] Could not get lock /var/lib/dpkg/lock Error in Ubuntu | It's FOSS](https://itsfoss.com/could-not-get-lock-error/)\u003c/dt\u003e\n\n## Time series \n\n [Time Series Analysis in Python: An Introduction – Towards Data Science](https://towardsdatascience.com/time-series-analysis-in-python-an-introduction-70d5a5b1d52a)\u003c/dt\u003e\n\n [RJT1990/pyflux: Open source time series library for Python](https://github.com/RJT1990/pyflux)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Getting Started with Time Series — PyFlux 0.4.7 documentation](http://pyflux.readthedocs.io/en/latest/getting_started.html)\u003c/dt\u003e\n\n [Introduction to ARIMA models](https://people.duke.edu/~rnau/411arim.htm)\u003c/dt\u003e\n\n [Complete guide to create a Time Series Forecast (with Codes in Python)](https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/)\u003c/dt\u003e\n\n [How to Create an ARIMA Model for Time Series Forecasting with Python](https://machinelearningmastery.com/arima-for-time-series-forecasting-with-python/)\n \n [Time series with Siraj course by Kaggle](https://www.kaggle.com/learn/time-series-with-siraj)\n\n## Interesting Articles\n\n [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)\n \n [Artificial Intelligence — The Revolution Hasn’t Happened Yet](https://medium.com/@mijordan3/artificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7)\u003c/dt\u003e\n\n [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\u0026utm_medium=social\u0026utm_source=facebook.com\u0026utm_campaign=buffer)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [Did Toolmaking Pave the Road for Human Language? - The Atlantic](https://www.theatlantic.com/science/archive/2018/06/toolmaking-language-brain/562385/)\u003c/dt\u003e\n\n [Edward Witten Ponders the Nature of Reality | Quanta Magazine](https://www.quantamagazine.org/edward-witten-ponders-the-nature-of-reality-20171128/)\u003c/dt\u003e\n\n[Gatekeeping and Elitism in Data Science](https://towardsdatascience.com/gatekeeping-and-elitism-in-data-science-74cf19cd5744)\n\n [How Do Aliens Solve Climate Change? - The Atlantic](https://www.theatlantic.com/science/archive/2018/05/how-do-aliens-solve-climate-change/561479/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [How Information Got Re-Invented – Limits – Medium](https://medium.com/s/nautilus-limits/how-information-got-re-invented-888fea36c4a5)\u003c/dt\u003e\n\n [How Social Media Endangers Knowledge | WIRED](https://www.wired.com/story/wikipedias-fate-shows-how-the-web-endangers-knowledge/)\u003c/dt\u003e\n\n [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\u0026utm_source=Facebook\u0026utm_medium=Owned+Social)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [On Chomsky and the Two Cultures of Statistical Learning](http://norvig.com/chomsky.html)\u003c/dt\u003e\n\n [Quantum Leaps in Quantum Computing? - Scientific American](https://www.scientificamerican.com/article/quantum-leaps-in-quantum-computing/?utm_source=facebook\u0026utm_medium=social\u0026utm_campaign=sa-editorial-social\u0026utm_content\u0026utm_term=physics_sa-magazine_text_free)\u003c/dt\u003e\n\n [Strategy vs. Tactics: What's the Difference and Why Does it Matter?](https://fs.blog/2018/08/strategy-vs-tactics/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [The Quantum Thermodynamics Revolution | Quanta Magazine](https://www.quantamagazine.org/the-quantum-thermodynamics-revolution-20170502?utm_content=buffere2607\u0026utm_medium=social\u0026utm_source=facebook.com\u0026utm_campaign=buffer)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [Life Is a Braid in Spacetime – Time – Medium](https://medium.com/s/nautilus-time/life-is-a-braid-in-spacetime-16dbf74d105f)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Don’t Compete. Create! - Darius Foroux - Pocket](https://getpocket.com/explore/item/don-t-compete-create-2068896981)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [So you want to be a Research Scientist – Vincent Vanhoucke – Medium](https://medium.com/@vanhoucke/so-you-want-to-be-a-research-scientist-363c075d3d4c)\u003c/dt\u003e\n\n [Homeland Security Will Let Software Flag Potential Terrorists](https://theintercept.com/2018/12/03/air-travel-surveillance-homeland-security/)\u003c/dt\u003e\n\n [What Happens When a World Order Ends](https://www.foreignaffairs.com/articles/2018-12-11/how-world-order-ends)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [The Brain's Autopilot Mechanism Steers Consciousness - Scientific American](https://www.scientificamerican.com/article/the-brains-autopilot-mechanism-steers-consciousness/)\u003c/dt\u003e\n\n [What is Intelligence? – Towards Data Science](https://towardsdatascience.com/what-is-intelligence-a69cbd8bb1b4)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n ## Julia\n\n [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/)\u003c/dt\u003e\n\n## Machine Learning\n\n### Experiment tracking\n[ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It](https://neptune.ai/blog/ml-experiment-tracking)\n\n### Fairness and bias\n[Evaluating machine learning models for fairness and bias](https://towardsdatascience.com/evaluating-machine-learning-models-fairness-and-bias-4ec82512f7c3)\n\n### Deployment of ML\n\n[Creating data science APIs with Flask](https://faculty.ai/blog/creating-data-science-apis-with-flask/)\n\n[Flask and Heroku for online Machine Learning deployment](https://towardsdatascience.com/flask-and-heroku-for-online-machine-learning-deployment-425beb54a274)\n\n[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)\n\n [[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)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [A Guide to Scaling Machine Learning Models in Production](https://hackernoon.com/a-guide-to-scaling-machine-learning-models-in-production-aa8831163846)\u003c/dt\u003e\n\n [Deploying Keras Deep Learning Models with Flask – Towards Data Science](https://towardsdatascience.com/deploying-keras-deep-learning-models-with-flask-5da4181436a2)\u003c/dt\u003e\n\n [Deploying Machine Learning at Scale - Algorithmia Blog](https://blog.algorithmia.com/deploying-machine-learning-at-scale/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [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/)\n\n [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)\n\n [How to deliver on Machine Learning projects – Insight Data](https://blog.insightdatascience.com/how-to-deliver-on-machine-learning-projects-c8d82ce642b0)\n\n [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)\n\n### Genetic Algorithm\n\n [Genetic Algorithm Implementation in Python – Towards Data Science](https://towardsdatascience.com/genetic-algorithm-implementation-in-python-5ab67bb124a6)\n\n [Introduction to Optimization with Genetic Algorithm](https://towardsdatascience.com/introduction-to-optimization-with-genetic-algorithm-2f5001d9964b)\n\n [A tutorial on Differential Evolution with Python · Pablo R. Mier](https://pablormier.github.io/2017/09/05/a-tutorial-on-differential-evolution-with-python/)\n\n### Keras\n\n [Guide to the Sequential model - Keras Documentation](https://keras.io/getting-started/sequential-model-guide/)\u003c/dt\u003e\n\n [Keras Documentation](https://keras.io/)\u003c/dt\u003e\n\n [How to Use Word Embedding Layers for Deep Learning with Keras - Machine Learning Mastery](https://machinelearningmastery.com/use-word-embedding-layers-deep-learning-keras/)\u003c/dt\u003e\n\n### Neural Network\n\n### Videos\n\n [Brandon Rohrer - Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)](https://www.youtube.com/watch?v=WCUNPb-5EYI)\u003c/dt\u003e\n\n [CS231n Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM - YouTube](https://www.youtube.com/watch?v=iX5V1WpxxkY)\u003c/dt\u003e\n\n [Nuts and Bolts of Applying Deep Learning (Andrew Ng) - YouTube](https://www.youtube.com/watch?v=F1ka6a13S9I)\u003c/dt\u003e\n\n [Siraj Raval - LSTM Networks - The Math of Intelligence (Week 8) - YouTube](https://www.youtube.com/watch?v=9zhrxE5PQgY)\u003c/dt\u003e\n\n [Siraj Raval - Recurrent Neural Networks - The Math of Intelligence (Week 5) - YouTube](https://www.youtube.com/watch?v=BwmddtPFWtA)\u003c/dt\u003e\n\n [Andrew Ng: Artificial Intelligence is the New Electricity - YouTube](https://www.youtube.com/watch?v=21EiKfQYZXc)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [A Neural Network Playground](http://playground.tensorflow.org/#activation=tanh\u0026batchSize=10\u0026dataset=circle\u0026regDataset=reg-plane\u0026learningRate=0.03\u0026regularizationRate=0\u0026noise=0\u0026networkShape=4,2\u0026seed=0.53044\u0026showTestData=false\u0026discretize=false\u0026percTrainData=50\u0026x=true\u0026y=true\u0026xTimesY=false\u0026xSquared=false\u0026ySquared=false\u0026cosX=false\u0026sinX=false\u0026cosY=false\u0026sinY=false\u0026collectStats=false\u0026problem=classification\u0026initZero=false\u0026hideText=false)\u003c/dt\u003e\n\n [A Visual Guide to Evolution Strategies](http://blog.otoro.net/2017/10/29/visual-evolution-strategies/)\u003c/dt\u003e\n\n [Andrej Carpathy blog - Hacker's guide to Neural Networks](http://karpathy.github.io/neuralnets/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [But what *is* a Neural Network? | Deep learning, chapter 1](https://www.youtube.com/watch?time_continue=80\u0026v=aircAruvnKk)\u003c/dt\u003e\n\n [Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning \u0026 Big Data](https://becominghuman.ai/cheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463)\u003c/dt\u003e\n\n [Convolutional Networks in Java - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https://deeplearning4j.org/convolutionalnets.html)\u003c/dt\u003e\n\n [CS231n Convolutional Neural Networks for Visual Recognition](http://cs231n.github.io/convolutional-networks/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Deep Learning Fundamentals - Cognitive Class](https://cognitiveclass.ai/courses/introduction-deep-learning/?utm_content=buffer3ab0d\u0026utm_medium=social\u0026utm_source=facebook.com\u0026utm_campaign=buffer)\u003c/dt\u003e\n\n [Exploring LSTMs](http://blog.echen.me/2017/05/30/exploring-lstms/)\u003c/dt\u003e\n\n [Feature Visualization](https://distill.pub/2017/feature-visualization/)\u003c/dt\u003e\n\n [J Alammar – Explorations in touchable pixels and intelligent androids](http://jalammar.github.io/)\u003c/dt\u003e\n\n [Learning without Backpropagation: Intuition and Ideas (Part 1) – Tom Breloff](http://www.breloff.com/no-backprop/)\u003c/dt\u003e\n\n [Must know Information Theory concepts in Deep Learning (AI)](https://towardsdatascience.com/must-know-information-theory-concepts-in-deep-learning-ai-e54a5da9769d)\u003c/dt\u003e\n\n [Neural networks and deep learning](http://neuralnetworksanddeeplearning.com/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [The Unreasonable Effectiveness of Recurrent Neural Networks](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)\u003c/dt\u003e\n\n [Understanding Hinton’s Capsule Networks. Part I: Intuition.](https://medium.com/@pechyonkin/understanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b)\u003c/dt\u003e\n\n [Understanding LSTM Networks -- colah's blog](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [How Do Artificial Neural Networks Learn? – Towards Data Science](https://towardsdatascience.com/how-do-artificial-neural-networks-learn-773e46399fc7)\u003c/dt\u003e\n\n [The Neural Network Zoo - The Asimov Institute](http://www.asimovinstitute.org/neural-network-zoo/)\u003c/dt\u003e\n\n [A History of Deep Learning | Import.io](https://www.import.io/post/history-of-deep-learning/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n### NLP\n\n [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)\u003c/dt\u003e\n\n [Coding \u0026 English Lit: Natural Language Processing in Python](https://medium.com/@kellylougheed/coding-english-lit-natural-language-processing-in-python-ba8ebae4dde3)\u003c/dt\u003e\n\n [TextBlob: Simplified Text Processing — TextBlob 0.15.1 documentation](https://textblob.readthedocs.io/en/dev/)\u003c/dt\u003e\n\n [Python Regular Expression Tutorial (article) - DataCamp](https://www.datacamp.com/community/tutorials/python-regular-expression-tutorial)\u003c/dt\u003e\n \n [Stanford NLP](https://stanfordnlp.github.io/stanfordnlp/)\n\n### Reinforcement Learning\n\n[Reinforcement Learning Course - Full Machine Learning Tutorial](https://www.youtube.com/watch?v=ELE2_Mftqoc)\n\n [A brief introduction to reinforcement learning – freeCodeCamp.org](https://medium.freecodecamp.org/a-brief-introduction-to-reinforcement-learning-7799af5840db)\u003c/dt\u003e\n\n [An introduction to Reinforcement Learning – freeCodeCamp.org](https://medium.freecodecamp.org/an-introduction-to-reinforcement-learning-4339519de419)\u003c/dt\u003e\n\n [Key Papers in Deep RL — Spinning Up documentation](https://spinningup.openai.com/en/latest/spinningup/keypapers.html#model-free-rl)\u003c/dt\u003e\n\n [Nuts \u0026 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)\u003c/dt\u003e\n\n [Reinforcement Learning: A Deep Dive | Toptal](https://www.toptal.com/machine-learning/deep-dive-into-reinforcement-learning)\u003c/dt\u003e\n\n [Part 1: Key Concepts in RL — Spinning Up documentation](https://spinningup.openai.com/en/latest/spinningup/rl_intro.html)\u003c/dt\u003e\n\n [Dissecting Reinforcement Learning-Part.1](https://mpatacchiola.github.io/blog/2016/12/09/dissecting-reinforcement-learning.html)\u003c/dt\u003e\n\n [Reinforcement Q-Learning from Scratch in Python with OpenAI Gym – LearnDataSci](https://www.learndatasci.com/tutorials/reinforcement-q-learning-scratch-python-openai-gym/)\u003c/dt\u003e\n\n [Google AI Blog: Curiosity and Procrastination in Reinforcement Learning](https://ai.googleblog.com/2018/10/curiosity-and-procrastination-in.html)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n### TensorFlow\n\n [Building Input Functions with tf.estimator  |  TensorFlow](https://www.tensorflow.org/get_started/input_fn)\u003c/dt\u003e\n\n [Getting Started With TensorFlow  |  TensorFlow](https://www.tensorflow.org/get_started/get_started)\u003c/dt\u003e\n\n [Installing TensorFlow on Windows  |  TensorFlow](https://www.tensorflow.org/install/install_windows)\u003c/dt\u003e\n\n [TensorFlow](https://www.tensorflow.org/)\u003c/dt\u003e\n\n [TensorFlow Linear Model Tutorial  |  TensorFlow](https://www.tensorflow.org/tutorials/wide)\u003c/dt\u003e\n\n [TensorFlow Wide \u0026 Deep Learning Tutorial  |  TensorFlow](https://www.tensorflow.org/tutorials/wide_and_deep)\u003c/dt\u003e\n\n [Using TensorFlow in Windows with a GPU | Heaton Research](http://www.heatonresearch.com/2017/01/01/tensorflow-windows-gpu.html)\u003c/dt\u003e\n\n [Installation Guide Windows :: CUDA Toolkit Documentation](http://docs.nvidia.com/cuda/cuda-installation-guide-microsoft-windows/)\n\n [7 Steps to Mastering Machine Learning With Python](https://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html)\u003c/dt\u003e\n\n [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [Automated Machine Learning Hyperparameter Tuning in Python](https://towardsdatascience.com/automated-machine-learning-hyperparameter-tuning-in-python-dfda59b72f8a)\u003c/dt\u003e\n\n [Berkeley AI Materials](http://ai.berkeley.edu/lecture_videos.html)\u003c/dt\u003e\n\n [Deep Learning For Coders fast.ai](http://course.fast.ai/)\u003c/dt\u003e\n\n [Essentials of Machine Learning Algorithms (with Python and R Codes)](https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/?utm_medium=social\u0026utm_source=linkedin.com\u0026utm_campaign=buffer)\u003c/dt\u003e\n\n [GOOGLE - Rules of Machine Learning:  |  Machine Learning Rules  |  Google Developers](https://developers.google.com/machine-learning/rules-of-ml/)\u003c/dt\u003e\n\n [http://www.r2d3.us/visual-intro-to-machine-learning-part-2/](http://www.r2d3.us/visual-intro-to-machine-learning-part-2/)\u003c/dt\u003e\n\n [ISLR class videos](https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/)\u003c/dt\u003e\n\n [Lecture Collection | Machine Learning - Stanford course](https://www.youtube.com/view_play_list?p=A89DCFA6ADACE599)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Microsoft Azure ML Cheat sheet](https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-algorithm-choice)\u003c/dt\u003e\n\n [Open Machine Learning Course (beta) • mlcourse.ai](https://mlcourse.ai/)\u003c/dt\u003e\n\n [Pedro Domigos Machine Learning lectures](https://www.youtube.com/user/UWCSE/playlists?shelf_id=16\u0026sort=dd\u0026view=50)\u003c/dt\u003e\n\n [The Hitchhiker’s Guide to Machine Learning in Python](https://medium.com/@conordewey3/the-hitchhikers-guide-to-machine-learning-algorithms-in-python-bfad66adb378)\u003c/dt\u003e\n\n [Top 10 Machine Learning Projects on Github](http://www.kdnuggets.com/2015/12/top-10-machine-learning-github.html)\u003c/dt\u003e\n\n [UCI Machine Learning Repository](http://archive.ics.uci.edu/ml/)\u003c/dt\u003e\n\n ### Optimization and ML\n\n [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/)\u003c/dt\u003e\n\n### Kaggle\n\n[Hello Kaggle! - A Kaggle Guide for someone who is new at Kaggle](https://github.com/stevekwon211/Hello-Kaggle)\u003c/dt\u003e\n\n## Python\n\n### Tutorials\n\n[Everything About Python — Beginner To Advanced](https://medium.com/fintechexplained/everything-about-python-from-beginner-to-advance-level-227d52ef32d2)\n\n### Jupyter and IDE related\n\n[Interactive spreadsheets in Jupyter](https://towardsdatascience.com/interactive-spreadsheets-in-jupyter-32ab6ec0f4ff)\n\n[PyCharm for data scientists](https://www.kdnuggets.com/2019/05/pycharm-data-scientists.html)\n\n[Built-in magic commands — IPython 6.2.1 documentation](http://ipython.readthedocs.io/en/stable/interactive/magics.html)\u003c/dt\u003e\n\n[Concrete Statistics Jupyter Notebook Peter Norvig](http://nbviewer.jupyter.org/url/norvig.com/ipython/Probability.ipynb)\u003c/dt\u003e\n\n[Economics simulation Jupyter Notebook Peter Norvig](http://nbviewer.jupyter.org/url/norvig.com/ipython/Economics.ipynb)\u003c/dt\u003e\n\n[Markdown Cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet)\u003c/dt\u003e\n\n[Using Interact — Jupyter Widgets 7.0.3 documentation](http://ipywidgets.readthedocs.io/en/stable/examples/Using%20Interact.html)\n \n[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)\n\n### Matplotlib, Seaborn, Visualization\n\n [color example code: colormaps_reference.py — Matplotlib 2.0.2 documentation](https://matplotlib.org/examples/color/colormaps_reference.html)\u003c/dt\u003e\n\n [ggplot | Home](http://ggplot.yhathq.com/)\u003c/dt\u003e\n\n [Matplotlib 1.5.1](http://matplotlib.org/1.5.1/index.html)\u003c/dt\u003e\n\n [Matplotlib Plotting commands summary —](http://matplotlib.org/1.5.1/api/pyplot_summary.html)\u003c/dt\u003e\n\n [Matplotlib tutorial](http://www.labri.fr/perso/nrougier/teaching/matplotlib/)\u003c/dt\u003e\n\n [Seaborn tutorial — seaborn 0.7.1 documentation](http://seaborn.pydata.org/tutorial.html)\u003c/dt\u003e\n\n### MOOC courses\n\n[Github/jmportilla/Complete-Python-Bootcamp: Lectures](https://github.com/jmportilla/Complete-Python-Bootcamp)\u003c/dt\u003e\n\n [Jupyter Notebook - Udemy Complete Python Bootcamp course](http://nbviewer.jupyter.org/github/jmportilla/Complete-Python-Bootcamp/tree/master/)\u003c/dt\u003e\n\n [Python for Data Science and Machine Learning Bootcamp | Udemy](https://www.udemy.com/python-for-data-science-and-machine-learning-bootcamp/learn/v4/overview)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Foundations of Machine Learning (A course by Bloomberg)](https://www.techleer.com/articles/536-foundations-of-machine-learning-a-course-by-bloomberg/)\u003c/dt\u003e\n\n### NumPy and SciPy\n \n [Linear algebra (numpy.linalg) — NumPy v1.12 Manual](https://docs.scipy.org/doc/numpy/reference/routines.linalg.html)\u003c/dt\u003e\n\n [NumPy v1.12 Universal functions](https://docs.scipy.org/doc/numpy/reference/ufuncs.html)\u003c/dt\u003e\n\n [NumPy v1.13.dev0 Manual](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html)\u003c/dt\u003e\n\n [Random sampling (numpy.random) — NumPy v1.13 Manual](https://docs.scipy.org/doc/numpy-1.13.0/reference/routines.random.html)\u003c/dt\u003e\n\n [SciPy — SciPy v0.19.0 Reference Guide](https://docs.scipy.org/doc/scipy/reference/?v=20170402183812)\u003c/dt\u003e\n\n [From Python to Numpy](https://www.labri.fr/perso/nrougier/from-python-to-numpy/#id7)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n### Pandas\n\n [Pandas 0.20.3 documentation](http://pandas.pydata.org/pandas-docs/stable/)\u003c/dt\u003e\n\n [Pandas: Python Data Analysis Library](http://pandas.pydata.org/)\u003c/dt\u003e\n\n\n### Setup, PyPi, Creating your own packages\n [Home | Read the Docs](https://readthedocs.org/)\u003c/dt\u003e\n\n [How to publish your own Python Package on PyPi – freeCodeCamp](https://medium.freecodecamp.org/how-to-publish-a-pyton-package-on-pypi-a89e9522ce24)\u003c/dt\u003e\n \n [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)\n\n [How to submit a package to PyPI — Peter Downs](http://peterdowns.com/posts/first-time-with-pypi.html)\u003c/dt\u003e\n\n [Packaging and Distributing Projects — Python Packaging User Guide](https://packaging.python.org/tutorials/distributing-packages/#setup-args)\u003c/dt\u003e\n\n [reStructuredText Primer — Sphinx 1.8.0+ documentation](http://www.sphinx-doc.org/en/master/rest.html#rst-primer)\u003c/dt\u003e\n\n [Using TestPyPI — Python Packaging User Guide](https://packaging.python.org/guides/using-testpypi/)\u003c/dt\u003e\n\n [How to open source your Python library | Opensource.com](https://opensource.com/article/18/12/tips-open-sourcing-python-libraries?utm_medium=Email\u0026utm_campaign=weekly\u0026sc_cid=701f2000000RRCwAAO)\u003c/dt\u003e\n\n### Spark and AWS\n\n [Amazon Web Services (AWS) - Cloud Computing Services](https://aws.amazon.com/)\u003c/dt\u003e\n\n [Connecting to Your Linux Instance from Windows Using PuTTY - Amazon Elastic Compute Cloud](http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/putty.html)\u003c/dt\u003e\n\n [Install Spark on Windows (PySpark) – Michael Galarnyk – Medium](https://medium.com/@GalarnykMichael/install-spark-on-windows-pyspark-4498a5d8d66c)\u003c/dt\u003e\n\n### Projects\n[10 Steps to Set Up Your Python Project for Success](https://towardsdatascience.com/10-steps-to-set-up-your-python-project-for-success-14ff88b5d13)\n\n### Tools and Utilities\n\n [itertools — Functions creating iterators for efficient looping — Python 3.6.3 documentation](https://docs.python.org/3/library/itertools.html)\u003c/dt\u003e\n\n### Web Data Analytics\n\n [Processing XML in Python with ElementTree - Eli Bendersky's website](https://eli.thegreenplace.net/2012/03/15/processing-xml-in-python-with-elementtree)\u003c/dt\u003e\n\n [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/)\u003c/dt\u003e\n\n [28 Jupyter Notebook tips, tricks and shortcuts](https://www.dataquest.io/blog/jupyter-notebook-tips-tricks-shortcuts/)\u003c/dt\u003e\n\n [A curated list of awesome Python frameworks, libraries, software and resources](https://github.com/vinta/awesome-python)\u003c/dt\u003e\n\n [Archived Problems - Project Euler](https://projecteuler.net/archives)\u003c/dt\u003e\n\n [Choosing the right estimator — scikit-learn 0.18.1 documentation](http://scikit-learn.org/stable/tutorial/machine_learning_map/)\u003c/dt\u003e\n\n [CodeSkulptor](http://www.codeskulptor.org/)\u003c/dt\u003e\n\n [CodeSkulptor](http://py3.codeskulptor.org/)\u003c/dt\u003e\n\n [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)\u003c/dt\u003e\n\n [Pandas 0.20.3 - API Reference](http://pandas.pydata.org/pandas-docs/stable/api.html)\u003c/dt\u003e\n\n [Pandas 0.20.3 Cookbook](http://pandas.pydata.org/pandas-docs/stable/cookbook.html)\u003c/dt\u003e\n\n [PostgreSQL + Python | Psycopg](http://initd.org/psycopg/)\u003c/dt\u003e\n\n [Problems - CodeAbbey](http://www.codeabbey.com/index/task_list)\u003c/dt\u003e\n\n [Project Jupyter | Home](http://jupyter.org/)\u003c/dt\u003e\n\n [PY4E - Python for Everybody](https://www.py4e.com/)\u003c/dt\u003e\n\n [Python 2.7.13 documentation](https://docs.python.org/2/tutorial/index.html)\u003c/dt\u003e\n\n [Python Conquers The Universe | Adventures across space and time with the Python programming language](https://pythonconquerstheuniverse.wordpress.com/)\u003c/dt\u003e\n\n [Python Flask From Scratch - YouTube](https://www.youtube.com/playlist?list=PLillGF-RfqbbbPz6GSEM9hLQObuQjNoj_)\u003c/dt\u003e\n\n [Python Tricks 101 – Hacker Noon](https://hackernoon.com/python-tricks-101-2836251922e0)\u003c/dt\u003e\n\n [Python tutorial - TutorialsPoint](https://www.tutorialspoint.com/python/index.htm)\u003c/dt\u003e\n\n [Regular Expressions for Data Scientists](https://www.dataquest.io/blog/regular-expressions-data-scientists/)\n\n [Simple Linear Regression Analysis - ReliaWiki](http://reliawiki.org/index.php/Simple_Linear_Regression_Analysis#Fitted_Regression_Line)\n\n [Introduction — Python 101 1.0 documentation](https://python101.pythonlibrary.org/intro.html)\n\n [Documenting Python Code: A Complete Guide – Real Python](https://realpython.com/documenting-python-code/)\n\n [MIT AI: Python (Guido van Rossum) - YouTube](https://www.youtube.com/watch?v=ghwaIiE3Nd8\u0026feature=youtu.be\u0026fbclid=IwAR1xgzx1qvHu9b8YweZDO9s4868iH27gPPlBWlPiNA9IjxLbNjjwEOCghxo)\n\n [Python IDEs and Code Editors (Guide) – Real Python](https://realpython.com/python-ides-code-editors-guide/)\n \n [Advanced Python web scraping tricks and tips](https://www.codementor.io/blog/python-web-scraping-63l2v9sf2q)\n\n## R related\n\n [A Beginner’s Guide to Neural Networks with R](http://www.kdnuggets.com/2016/08/begineers-guide-neural-networks-r.html)\n \n [A Comprehensive Guide to Data Visualisation in R for Beginners](https://towardsdatascience.com/a-guide-to-data-visualisation-in-r-for-beginners-ef6d41a34174)\n\n [An R Introduction to Statistics | R Tutorial](http://www.r-tutor.com/)\n\n [Data Manipulation with dplyr | R-bloggers](https://www.r-bloggers.com/data-manipulation-with-dplyr/)\n\n [Data Science and Machine Learning Bootcamp with R | Udemy](https://www.udemy.com/data-science-and-machine-learning-bootcamp-with-r/learn/v4/overview)\n \n [Explore R | Discover libraries, source code, top authors, trending discussions | kandi](https://kandi.openweaver.com/explore/r)\n \n [ggplot2-cheatsheet.pdf](https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf)\n\n [Machine Learning A-Z™: Download Practice Datasets - SuperDataScience - Big Data | Analytics Careers | Mentors | Success](https://www.superdatascience.com/machine-learning/)\n\n [Quick-R: Home Page](http://www.statmethods.net/index.html)\n\n [R mailing lists archive](http://tolstoy.newcastle.edu.au/R/)\n\n [R Tutorial Series - Statistical Tests | Saranya Anandh | Pulse | LinkedIn](https://www.linkedin.com/pulse/r-tutorial-series-statistical-tests-saranya-anandh?trk=v-feed\u0026lipi=urn%3Ali%3Apage%3Ad_flagship3_feed%3BDlqO%2B6C4r4CsNhYU4n9rsw%3D%3D)\n\n [R: Control for Rpart Fits](https://stat.ethz.ch/R-manual/R-devel/library/rpart/html/rpart.control.html)\n\n [R: Recursive Partitioning and Regression Trees](https://stat.ethz.ch/R-manual/R-devel/library/rpart/html/rpart.html)\n\n [Short-refcard.pdf](https://cran.r-project.org/doc/contrib/Short-refcard.pdf)\n\n [Theme • ggplot2](http://ggplot2.tidyverse.org/reference/theme.html)\n \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftirthajyoti%2Fdata-science-best-resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftirthajyoti%2Fdata-science-best-resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftirthajyoti%2Fdata-science-best-resources/lists"}