[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9851":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":10,"languages":10,"totalLinesOfCode":10,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":15,"stars7d":16,"stars30d":17,"stars90d":15,"forks30d":15,"starsTrendScore":15,"compositeScore":18,"rankGlobal":10,"rankLanguage":10,"license":19,"archived":20,"fork":20,"defaultBranch":21,"hasWiki":22,"hasPages":22,"topics":23,"createdAt":10,"pushedAt":10,"updatedAt":44,"readmeContent":45,"aiSummary":46,"trendingCount":15,"starSnapshotCount":15,"syncStatus":47,"lastSyncTime":48,"discoverSource":49},9851,"Data-science-best-resources","tirthajyoti\u002FData-science-best-resources","tirthajyoti","Carefully curated resource links for data science in one place","",null,3208,1009,121,12,0,1,10,62.51,"MIT License",false,"master",true,[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"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","2026-06-12 04:00:47","![tdsp](https:\u002F\u002Fraw.githubusercontent.com\u002Ftirthajyoti\u002FData-science-best-resources\u002Fmaster\u002Fimages\u002Ftdsp-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:\u002F\u002Fwww.linkedin.com\u002Fin\u002Ftirthajyoti-sarkar-2127aa7\u002F) if you are interested in data science and would like to connect.\n\n### Please visit my [Medium profile](https:\u002F\u002Fmedium.com\u002F@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:\u002F\u002Fgithub.com\u002Ftirthajyoti\u002FMachine-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:\u002F\u002Fwww.montreal.ai\u002Fai4all.pdf)\n\n [OpenAI blog](https:\u002F\u002Fblog.openai.com\u002F)\n\n[AI thinks like a corporation—and that’s worrying - Open Voices](https:\u002F\u002Fwww.economist.com\u002Fopen-future\u002F2018\u002F11\u002F26\u002Fai-thinks-like-a-corporation-and-thats-worrying)\u003C\u002Fdt>\n\n [AITopics](https:\u002F\u002Faitopics.org\u002Fsearch)\u003C\u002Fdt>\n\n [Does the Brain Store Information in Discrete or Analog Form?](https:\u002F\u002Fmedium.com\u002Fmit-technology-review\u002Fdoes-the-brain-store-information-in-discrete-or-analog-form-f0e169361c99)\u003C\u002Fdt>\n\n [Explainable Artificial Intelligence (Part 1) — The Importance of Human Interpretable Machine…](https:\u002F\u002Ftowardsdatascience.com\u002Fhuman-interpretable-machine-learning-part-1-the-need-and-importance-of-model-interpretation-2ed758f5f476)\u003C\u002Fdt>\n\n [Is The Singularity Coming? – Arc Digital](https:\u002F\u002Farcdigital.media\u002Fis-the-singularity-coming-ef8580d4ce97)\u003C\u002Fdt>\n\n [Michael I. Jordan NYSE Machine Learning Presentation](https:\u002F\u002Fwww.youtube.com\u002Fwatch?time_continue=2&v=17cp8PLKvOc)\u003C\u002Fdt>\n\n [Some scientists fear superintelligent machines could pose a threat to humanity | The Washington Post](https:\u002F\u002Fwww.washingtonpost.com\u002Fsf\u002Fnational\u002F2015\u002F12\u002F27\u002Faianxiety\u002F?noredirect=on&utm_term=.c3ac6321c831)\u003C\u002Fdt>\n\n [The Four Waves of A.I. | LinkedIn](https:\u002F\u002Fwww.linkedin.com\u002Fpulse\u002Ffour-waves-ai-kai-fu-lee\u002F)\u003C\u002Fdt>\n\n [When algorithms go wrong we need power to fight back, say researchers - The Verge](https:\u002F\u002Fwww.theverge.com\u002F2018\u002F12\u002F8\u002F18131745\u002Fai-now-algorithmic-accountability-2018-report-facebook-microsoft-google)\u003C\u002Fdt>\n\n## AWS related\n\n [Amazon CloudWatch - Application and Infrastructure Monitoring](https:\u002F\u002Faws.amazon.com\u002Fcloudwatch\u002F)\n\n [Amazon DynamoDB - Overview](https:\u002F\u002Faws.amazon.com\u002Fdynamodb\u002F)\n\n [Amazon Elastic Block Store (EBS) - Amazon Web Services](https:\u002F\u002Faws.amazon.com\u002Febs\u002F)\n\n [Amazon Elastic File System (EFS) | Cloud File Storage](https:\u002F\u002Faws.amazon.com\u002Fefs\u002F)\n\n [AWS Concepts: Understanding AWS - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=qcY-uiEHhn0)\n\n [AWS Concepts: Understanding the Course Material & Features - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=LKStwibxbR0&list=PLv2a_5pNAko2Jl4Ks7V428ttvy-Fj4NKU)\n\n [AWS In 10 Minutes | AWS Tutorial For Beginners | AWS Training Video | AWS Tutorial | Simplilearn - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=r4YIdn2eTm4)\n\n [AWS re:Invent 2017: Building production apps easily with Amazon Lightsail (CMP212) - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=29_LqYnomdg)\n\n [Classless Inter-Domain Routing - Wikipedia](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FClassless_Inter-Domain_Routing)\n\n [Cloud Compute Products – Amazon Web Services (AWS)](https:\u002F\u002Faws.amazon.com\u002Fproducts\u002Fcompute\u002F)\n\n [Cloud Object Storage | Store & Retrieve Data Anywhere | Amazon Simple Storage Service](https:\u002F\u002Faws.amazon.com\u002Fs3\u002F)\n\n [Elastic Load Balancing - Amazon Web Services](https:\u002F\u002Faws.amazon.com\u002Felasticloadbalancing\u002F)\n\n [Getting Spark, Python, and Jupyter Notebook running on Amazon EC2](https:\u002F\u002Fmedium.com\u002F@josemarcialportilla\u002Fgetting-spark-python-and-jupyter-notebook-running-on-amazon-ec2-dec599e1c297)\n\n [Use PuTTY to access EC2 Linux Instances via SSH from Windows](https:\u002F\u002Flinuxacademy.com\u002Fhowtoguides\u002Fposts\u002Fshow\u002Ftopic\u002F17385-use-putty-to-access-ec2-linux-instances-via-ssh-from-windows)\n\n [What is Cloud Computing? - Amazon Web Services](https:\u002F\u002Faws.amazon.com\u002Fwhat-is-cloud-computing\u002F)\n \n## Blogs, StacksExchanges\n\n[7-Step Guide to Become a Machine Learning Engineer in 2021](https:\u002F\u002Fwww.dezyre.com\u002Farticle\u002F7-step-guide-to-become-a-machine-learning-engineer-in-2021\u002F409)\n\n[Reducing the Need for Labeled Data in Generative Adversarial Networks](https:\u002F\u002Fai.googleblog.com\u002F2019\u002F03\u002Freducing-need-for-labeled-data-in.html)\n\n[Jason's Google ML 101 deck](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1kSuQyW5DTnkVaZEjGYCkfOxvzCqGEFzWBy4e9Uedd9k\u002Fedit)\n\n [10 Free Must-Read Books for Machine Learning and Data Science](https:\u002F\u002Fwww.kdnuggets.com\u002F2017\u002F04\u002F10-free-must-read-books-machine-learning-data-science.html?utm_content=buffer5a67a&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)\n\n [Advice to aspiring data scientists: start a blog – Variance Explained](http:\u002F\u002Fvarianceexplained.org\u002Fr\u002Fstart-blog\u002F)\n\n [Brandon Roher Blog](https:\u002F\u002Fbrohrer.github.io\u002Fblog.html)\n\n [Chris Albon - Data Science, Machine Learning, and Artificial Intelligence](https:\u002F\u002Fchrisalbon.com\u002F#Python)\n\n [Data Science Stack Exchange](http:\u002F\u002Fdatascience.stackexchange.com\u002F)\n\n [Data Skeptic](https:\u002F\u002Fdataskeptic.com\u002F)\n\n [DataTau](http:\u002F\u002Fwww.datatau.com\u002F)\n \n [explained.ai - Deep explanations of machine learning and related topics](https:\u002F\u002Fexplained.ai\u002F)\n\n [FlowingData](http:\u002F\u002Fflowingdata.com\u002F)\n\n [Here Are (Approximately) 3000 Free Data Sources You Can Use Right Now](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fmetabrown\u002F2017\u002F06\u002F30\u002Fhere-are-approximately-3000-free-sources-for-data-you-can-use-right-now\u002Famp\u002F?utm_content=bufferef401&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)\n\n [If you want to learn Data Science, take a few of these statistics classes](https:\u002F\u002Fmedium.freecodecamp.com\u002Fif-you-want-to-learn-data-science-take-a-few-of-these-statistics-classes-9bbabab098b9)\n\n [Learn Data Science - Infographic (article) - DataCamp](https:\u002F\u002Fwww.datacamp.com\u002Fcommunity\u002Ftutorials\u002Flearn-data-science-infographic)\n\n [LIGO Gravity Wave GW150914_tutorial](https:\u002F\u002Flosc.ligo.org\u002Fs\u002Fevents\u002FGW150914\u002FGW150914_tutorial.html)\n\n [O.R. & Analytics Success Stories - INFORMS](https:\u002F\u002Fwww.informs.org\u002FImpact\u002FO.R.-Analytics-Success-Stories)\n\n [OpenAI Blog](https:\u002F\u002Fblog.openai.com\u002F)\n\n [Paul Ford: What Is Code? | Bloomberg](https:\u002F\u002Fwww.bloomberg.com\u002Fgraphics\u002F2015-paul-ford-what-is-code\u002F)\n\n [Science Isn’t Broken | FiveThirtyEight](https:\u002F\u002Ffivethirtyeight.com\u002Ffeatures\u002Fscience-isnt-broken\u002F#part1)\n\n [Scientifically Sound](https:\u002F\u002Fscientificallysound.org\u002F)\n\n [AIspace](http:\u002F\u002Faispace.org\u002F)\n\n [Top 28 Cheat Sheets for Machine Learning, Data Science, Probability, SQL & Big Data](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2017\u002F02\u002Ftop-28-cheat-sheets-for-machine-learning-data-science-probability-sql-big-data\u002F?utm_content=buffer9e308&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)\n\n [GitHub Python Data Science Spotlight: AutoML, NLP, Visualization, ML Workflows](https:\u002F\u002Fwww.kdnuggets.com\u002F2018\u002F08\u002Fgithub-python-data-science-spotlight.html)\n\n## Books, Courses, Repos\n\n [Solved end-to-end Data Science projects](https:\u002F\u002Fwww.dezyre.com\u002Fprojects\u002Fdata-science-projects)\n \n [Dive into Deep Learning (An interactive deep learning book with code, math, and discussions)](https:\u002F\u002Fd2l.ai\u002Findex.html)\n\n [Machine Learning Math book](https:\u002F\u002Fmml-book.github.io\u002F)\n\n [Learn to code | Codecademy](https:\u002F\u002Fwww.codecademy.com\u002F)\n\n [Lecture Notes | Introduction to MATLAB | Electrical Engineering and Computer Science | MIT OpenCourseWare](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-094-introduction-to-matlab-january-iap-2010\u002Flecture-notes\u002F)\n\n [60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more](http:\u002F\u002Fwww.kdnuggets.com\u002F2015\u002F09\u002Ffree-data-science-books.html)\n \n [Feature Engineering and Selection: A Practical Approach for Predictive Models](http:\u002F\u002Fwww.feat.engineering\u002F)\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:\u002F\u002Fhelp.github.com\u002Farticles\u002Fadding-an-existing-project-to-github-using-the-command-line\u002F)\n\n [An Intro to Git and GitHub for Beginners (Tutorial)](https:\u002F\u002Fproduct.hubspot.com\u002Fblog\u002Fgit-and-github-tutorial-for-beginners)\u003C\u002Fdt>\n\n [Follow these simple rules and you’ll become a Git and GitHub master](https:\u002F\u002Fmedium.freecodecamp.org\u002Ffollow-these-simple-rules-and-youll-become-a-git-and-github-master-e1045057468f)\n\n [Git - Book](https:\u002F\u002Fgit-scm.com\u002Fbook\u002Fen\u002Fv2)\n\n [git - the simple guide - no deep shit!](http:\u002F\u002Frogerdudler.github.io\u002Fgit-guide\u002F)\n\n [How not to be afraid of GIT anymore – freeCodeCamp.org](https:\u002F\u002Fmedium.freecodecamp.org\u002Fhow-not-to-be-afraid-of-git-anymore-fe1da7415286)\n\n [joshnh\u002FGit-Commands: A list of commonly used Git commands](https:\u002F\u002Fgithub.com\u002Fjoshnh\u002FGit-Commands)\n\n [The beginner’s guide to contributing to a GitHub project – Rob Allen's DevNotes](https:\u002F\u002Fakrabat.com\u002Fthe-beginners-guide-to-contributing-to-a-github-project\u002F)\n\n [Understanding the GitHub Flow · GitHub Guides](https:\u002F\u002Fguides.github.com\u002Fintroduction\u002Fflow\u002F)\n\n## Interesting Articles\n \n [Towards an anti-fascist AI (from opendemocracy.net)](https:\u002F\u002Fwww.opendemocracy.net\u002Fen\u002Fdigitaliberties\u002Ftowards-anti-fascist-ai\u002F)\n \n [Becoming a Level 3.0 Data Scientist](https:\u002F\u002Fwww.kdnuggets.com\u002F2019\u002F05\u002Fbecoming-a-level-3-data-scientist.html)\n\n [The Third-wave of Data Scientist](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-third-wave-data-scientist-1421df7433c9)\n \n [46 Most Intellectually Stimulating Sites That Will Spark Your Inner Genius in 10 Minutes a Day](https:\u002F\u002Fmedium.com\u002Fswlh\u002Fin-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:\u002F\u002Fwww.quantamagazine.org\u002Fartificial-intelligence-learns-to-learn-entirely-on-its-own-20171018\u002F?utm_content=buffer578b7&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)\n\n [Edward Witten Ponders the Nature of Reality | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fedward-witten-ponders-the-nature-of-reality-20171128\u002F)\n\n   [Engineers Shouldn’t Write ETL: A Guide to Building a High Functioning Data Science Department | Stitch Fix Technology – Multithreaded](https:\u002F\u002Fmultithreaded.stitchfix.com\u002Fblog\u002F2016\u002F03\u002F16\u002Fengineers-shouldnt-write-etl\u002F)\n   \n  [Foundations Built for a General Theory of Neural Networks - Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Ffoundations-built-for-a-general-theory-of-neural-networks-20190131)\n   \n [General Thinking Tools: 9 Mental Models to Solve Difficult Problems](https:\u002F\u002Fwww.fs.blog\u002Fgeneral-thinking-tools\u002F)\n\n [How Social Media Endangers Knowledge | WIRED](https:\u002F\u002Fwww.wired.com\u002Fstory\u002Fwikipedias-fate-shows-how-the-web-endangers-knowledge\u002F)\n\n [In These Small Cities, AI Advances Could Be Costly - MIT Technology Review](https:\u002F\u002Fwww.technologyreview.com\u002Fs\u002F609076\u002Fin-these-small-cities-ai-advances-could-be-costly\u002F?utm_campaign=Owned+Social&utm_source=Facebook&utm_medium=Owned+Social)\n\n [Machine Learning’s ‘Amazing’ Ability to Predict Chaos | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fmachine-learnings-amazing-ability-to-predict-chaos-20180418\u002F)\n\n [New Brain Maps With Unmatched Detail May Change Neuroscience | WIRED](https:\u002F\u002Fwww.wired.com\u002Fstory\u002Fnew-brain-maps-with-unmatched-detail-may-change-neuroscience\u002F)\n\n [Pedro Domingos on the Arms Race in Artificial Intelligence - SPIEGEL ONLINE](http:\u002F\u002Fwww.spiegel.de\u002Finternational\u002Fworld\u002Fpedro-domingos-on-the-arms-race-in-artificial-intelligence-a-1203132.html)\n\n [Quantum Leaps in Quantum Computing? - Scientific American](https:\u002F\u002Fwww.scientificamerican.com\u002Farticle\u002Fquantum-leaps-in-quantum-computing\u002F?utm_source=facebook&utm_medium=social&utm_campaign=sa-editorial-social&utm_content&utm_term=physics_sa-magazine_text_free)\n\n [The Fragile State of the Midwest’s Public Universities - The Atlantic](https:\u002F\u002Fwww.theatlantic.com\u002Fbusiness\u002Farchive\u002F2017\u002F10\u002Fmidwestern-public-research-universities-funding\u002F542889\u002F?utm_source=vxfb)\n\n [The Future of Human Work Is Imagination, Creativity, and Strategy](https:\u002F\u002Fhbr.org\u002F2018\u002F01\u002Fthe-future-of-human-work-is-imagination-creativity-and-strategy?utm_campaign=hbr&utm_source=linkedin&utm_medium=social)\n\n [The Quantum Thermodynamics Revolution | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fthe-quantum-thermodynamics-revolution-20170502?utm_content=buffere2607&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)\n\n [What Is Code? | Paul Ford| Bloomberg](https:\u002F\u002Fwww.bloomberg.com\u002Fgraphics\u002F2015-paul-ford-what-is-code\u002F)\n\n [The Economics Of Artificial Intelligence - How Cheaper Predictions Will Change The World](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fbernardmarr\u002F2018\u002F07\u002F10\u002Fthe-economics-of-artificial-intelligence-how-cheaper-predictions-will-change-the-world\u002F#5b3b146f5a0d)\n\n [OpenAI’s Dota 2 defeat is still a win for artificial intelligence  - The Verge](https:\u002F\u002Fwww.theverge.com\u002F2018\u002F8\u002F28\u002F17787610\u002Fopenai-dota-2-bots-ai-lost-international-reinforcement-learning)\n\n [Machine Learning Confronts the Elephant in the Room | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fmachine-learning-confronts-the-elephant-in-the-room-20180920\u002F)\n\n## MOOC related\n\n[Complete lecture notes of the Stanford\u002FCoursera Machine Learning class by Andrew Ng](http:\u002F\u002Fwww.holehouse.org\u002Fmlclass\u002F)\n\n[200 universities just launched 560 free online courses. Here’s the full list.](https:\u002F\u002Fmedium.freecodecamp.org\u002F200-universities-just-launched-560-free-online-courses-heres-the-full-list-d9dd13600b04)\n\n [Artificial Intelligence | MIT OpenCourseWare](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-034-artificial-intelligence-fall-2010\u002Findex.htm)\n\n [Dashboard | MIT Professional Education Digital Programs](https:\u002F\u002Fmitprofessionalx.mit.edu\u002Fdashboard)\n\n [Data Science A-Z™: Real-Life Data Science Exercises Included | Udemy](https:\u002F\u002Fwww.udemy.com\u002Fdatascience\u002F)\n\n [Data Science Essentials | edX](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fdata-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)\n\n [How to choose effective MOOCs for machine learning and data science?](https:\u002F\u002Fmedium.com\u002F@tirthajyoti\u002Fhow-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:\u002F\u002Fmedium.freecodecamp.org\u002Fcoursera-free-online-courses-6d84cdb30da)\n\n [Information and Entropy | MIT OpenCourseWare](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-050j-information-and-entropy-spring-2008\u002Findex.htm)\n\n [Introduction to Algorithms | MIT OpenCourseWare](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-006-introduction-to-algorithms-fall-2011\u002Findex.htm)\n\n [Introduction to Data Analysis using Excel | edX](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fintroduction-data-analysis-using-excel-microsoft-dat205x-0)\n\n [Introduction to Python for Data Science | edX](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fintroduction-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#!)\n\n [Introduction to R for Data Science | edX](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fintroduction-r-data-science-microsoft-dat204x-3)\n\n [Mathematics for Computer Science | MIT OpenCourseWare](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-042j-mathematics-for-computer-science-spring-2015\u002Findex.htm)\n\n [Programming with Python for Data Science!](https:\u002F\u002Fcourses.edx.org\u002Fcourses\u002Fcourse-v1:Microsoft+DAT210x+2T2017\u002Finfo)\n\n [Statistical Thinking for Data Science course](https:\u002F\u002Fcourses.edx.org\u002Fcourses\u002Fcourse-v1:ColumbiaX+DS101X+1T2016\u002Fcourseware\u002F83b2b74597c44d858f1cd81edef2faf2\u002F9ba7caa9efaf4b86b7521534f9c841d5\u002F)\n\n [Top Data Science Online Courses in 2017 – LearnDataSci](http:\u002F\u002Fwww.learndatasci.com\u002Fbest-data-science-online-courses\u002F)\n\n [U. Wash ML course Jupyter Home](https:\u002F\u002Fhub.coursera-notebooks.org\u002Fuser\u002Fhwxlouxsysrhhnfzzinqjs\u002Ftree)\n\n## SQL\n\n [A Visual Explanation of SQL Joins](https:\u002F\u002Fblog.codinghorror.com\u002Fa-visual-explanation-of-sql-joins\u002F)\n\n [Join (SQL) - Wikipedia](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FJoin_(SQL))\n\n [PostgreSQL: Mathematical Functions and Operators](https:\u002F\u002Fwww.postgresql.org\u002Fdocs\u002F9.5\u002Fstatic\u002Ffunctions-math.html)\n\n [PostgreSQL: String Functions and Operators](https:\u002F\u002Fwww.postgresql.org\u002Fdocs\u002F9.5\u002Fstatic\u002Ffunctions-string.html)\n\n [Psycopg2 Tutorial - PostgreSQL with Python](https:\u002F\u002Fwiki.postgresql.org\u002Fwiki\u002FPsycopg2_Tutorial)\n\n [SQL Joins Explained](http:\u002F\u002Fwww.sql-join.com\u002F)\n\n [The SQL Tutorial for Data Analysis | SQL Tutorial - Mode Analytics](https:\u002F\u002Fcommunity.modeanalytics.com\u002Fsql\u002Ftutorial\u002Fintroduction-to-sql\u002F)\n \n  [SQL vs NoSQL or MySQL vs MongoDB - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZS_kXvOeQ5Y)\u003C\u002Fdt>\n\n [Thinking in SQL vs Thinking in Python](https:\u002F\u002Fblog.modeanalytics.com\u002Flearning-python-sql\u002F)\n \n [Kaggle SQL course (including BigQuery topics)](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fsql)\n \n ## Statistics\n \n [Common statistical tests are linear models (or: how to teach stats)](https:\u002F\u002Flindeloev.github.io\u002Ftests-as-linear\u002F)\n \n [Introductory statistics - OpenText Library](https:\u002F\u002Fsaylordotorg.github.io\u002Ftext_introductory-statistics\u002Findex.html)\n\n [Common statistical tests are linear models (or: how to teach stats)](https:\u002F\u002Flindeloev.github.io\u002Ftests-as-linear\u002F)\n \n [Background: Markov chains](https:\u002F\u002Fd18ky98rnyall9.cloudfront.net\u002F_adadc80290e52a99b282ca9d7c1a41ee_background_MarkovChains.html)\n\n [OpenIntro Stats](https:\u002F\u002Fwww.openintro.org\u002Findex.php)\n\n [Regression Analysis Tutorial and Examples | Minitab](http:\u002F\u002Fblog.minitab.com\u002Fblog\u002Fadventures-in-statistics-2\u002Fregression-analysis-tutorial-and-examples)\u003C\u002Fdt>\n\n [The 10 Statistical Techniques Data Scientists Need to Master](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-10-statistical-techniques-data-scientists-need-to-master-1ef6dbd531f7)\n\n [The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes)](https:\u002F\u002Fmedium.com\u002Fanalytics-vidhya\u002Fthe-ultimate-guide-to-12-dimensionality-reduction-techniques-with-python-codes-2c2afdbc09e3)\n\n [Thomas Bayes and the crisis in science – TheTLS](https:\u002F\u002Fwww.the-tls.co.uk\u002Farticles\u002Fpublic\u002Fthomas-bayes-science-crisis\u002F)\u003C\u002Fdt>\n\n [Welcome to STAT 505! | STAT 505](https:\u002F\u002Fonlinecourses.science.psu.edu\u002Fstat505\u002Fnode\u002F1)\n\n [Introduction to Bayesian Linear Regression – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fintroduction-to-bayesian-linear-regression-e66e60791ea7)\n\n [Regression Analysis Tutorial and Examples | Minitab](http:\u002F\u002Fblog.minitab.com\u002Fblog\u002Fadventures-in-statistics-2\u002Fregression-analysis-tutorial-and-examples)\n\n [The 10 Statistical Techniques Data Scientists Need to Master](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-10-statistical-techniques-data-scientists-need-to-master-1ef6dbd531f7)\u003C\u002Fdt>\n\n [Welcome to STAT 505! | STAT 505](https:\u002F\u002Fonlinecourses.science.psu.edu\u002Fstat505\u002Fnode\u002F1)\n \n [Probability and Statistics Visually](https:\u002F\u002Fseeing-theory.brown.edu)\n\n## Visualizations (and image processing related)\n\n[The paper describing Scikit-image from its core developers](https:\u002F\u002Fpeerj.com\u002Farticles\u002F453\u002F)\n\n[Full-screen interactive that lets you explore the first 300 years of Data Visualization](https:\u002F\u002Finfowetrust.com\u002Fscroll\u002F)\n\n[designing-great-visualizations.pdf](https:\u002F\u002Fwww.tableau.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fmedia\u002Fdesigning-great-visualizations.pdf)\n\n[Gallery of Data Visualization - Missed Opportunities and Graphical Failures](http:\u002F\u002Fwww.datavis.ca\u002Fgallery\u002Fmissed.php)\n\n [Lesson 1-4, first visualization data - Govind Acharya | Tableau Public](https:\u002F\u002Fpublic.tableau.com\u002Fprofile\u002Fgovind.acharya#!\u002Fvizhome\u002FLesson1-4firstvisualizationdata\u002FSheet1)\n\n [Mapping the 1854 Cholera Outbreak | Tableau Public](https:\u002F\u002Fpublic.tableau.com\u002Fs\u002Fgallery\u002Fmapping-1854-cholera-outbreak)\u003C\u002Fdt>\n\n [Resources | Tableau Public](https:\u002F\u002Fpublic.tableau.com\u002Fen-us\u002Fs\u002Fresources)\n\n [10 Free Must-Read Books for Machine Learning and Data Science](https:\u002F\u002Fwww.kdnuggets.com\u002F2017\u002F04\u002F10-free-must-read-books-machine-learning-data-science.html?utm_content=buffer5a67a&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)\u003C\u002Fdt>\n\n [60+ Free Books on Big Data, Data Science, Data Mining, Machine Learning, Python, R, and more](http:\u002F\u002Fwww.kdnuggets.com\u002F2015\u002F09\u002Ffree-data-science-books.html)\u003C\u002Fdt>\n\n [Data Skeptic](https:\u002F\u002Fdataskeptic.com\u002F)\n\n [GGobi data visualization system.](http:\u002F\u002Fwww.ggobi.org\u002F)\n\n [GitHub (Tirthajyoti Sarkar)](https:\u002F\u002Fgithub.com\u002Ftirthajyoti)\n\n [Here Are (Approximately) 3000 Free Data Sources You Can Use Right Now](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fmetabrown\u002F2017\u002F06\u002F30\u002Fhere-are-approximately-3000-free-sources-for-data-you-can-use-right-now\u002Famp\u002F?utm_content=bufferef401&utm_medium=social&utm_source=twitter.com&utm_campaign=buffer)\n\n [If you want to learn Data Science, take a few of these statistics classes](https:\u002F\u002Fmedium.freecodecamp.com\u002Fif-you-want-to-learn-data-science-take-a-few-of-these-statistics-classes-9bbabab098b9)\n\n [Learn to code | Codecademy](https:\u002F\u002Fwww.codecademy.com\u002F)\n\n [Lecture Notes | Introduction to MATLAB | Electrical Engineering and Computer Science | MIT OpenCourseWare](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-094-introduction-to-matlab-january-iap-2010\u002Flecture-notes\u002F)\n\n [Medium – Read, write and share stories that matter](https:\u002F\u002Fmedium.com\u002F)\n\n [Scientifically Sound](https:\u002F\u002Fscientificallysound.org\u002F)\n\n [Top 28 Cheat Sheets for Machine Learning, Data Science, Probability, SQL & Big Data](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2017\u002F02\u002Ftop-28-cheat-sheets-for-machine-learning-data-science-probability-sql-big-data\u002F?utm_content=buffer9e308&utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)\n\n [Learn Data Science - Infographic (article) - DataCamp](https:\u002F\u002Fwww.datacamp.com\u002Fcommunity\u002Ftutorials\u002Flearn-data-science-infographic)\n\n [Homework 3](file:\u002F\u002F\u002FC:\u002FUsers\u002FTirtha\u002FDocuments\u002FPersonal\u002FGaTech%20OMSA\u002FCourses\u002FFall%202018\u002FISYE%206501%20-%20Introduction%20to%20Analytics%20Modeling\u002FHW\u002FHW-3\u002FPeer%20Review\u002F2\u002FHomework_3.html)\n\n ## Neural Network\n\n### Videos\n\n[Deep blueberry](https:\u002F\u002Fmithi.github.io\u002Fdeep-blueberry\u002Fch0-introduction.html)\n\n [Brandon Rohrer - Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=WCUNPb-5EYI)\u003C\u002Fdt>\n\n [CS231n Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=iX5V1WpxxkY)\u003C\u002Fdt>\n\n [Nuts and Bolts of Applying Deep Learning (Andrew Ng) - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=F1ka6a13S9I)\u003C\u002Fdt>\n\n [Siraj Raval - LSTM Networks - The Math of Intelligence (Week 8) - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9zhrxE5PQgY)\u003C\u002Fdt>\n\n [Siraj Raval - Recurrent Neural Networks - The Math of Intelligence (Week 5) - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BwmddtPFWtA)\n\n [Andrew Ng: Artificial Intelligence is the New Electricity - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=21EiKfQYZXc)\u003C\u002Fdt>\n\n [A Neural Network Playground](http:\u002F\u002Fplayground.tensorflow.org\u002F#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)\u003C\u002Fdt>\n\n [But what *is* a Neural Network? | Deep learning, chapter 1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?time_continue=80&v=aircAruvnKk)\n\n [Convolutional Networks in Java - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https:\u002F\u002Fdeeplearning4j.org\u002Fconvolutionalnets.html)\u003C\u002Fdt>\n\n [CS231n Convolutional Neural Networks for Visual Recognition](http:\u002F\u002Fcs231n.github.io\u002Fconvolutional-networks\u002F)\n\n [Deep Learning Fundamentals - Cognitive Class](https:\u002F\u002Fcognitiveclass.ai\u002Fcourses\u002Fintroduction-deep-learning\u002F?utm_content=buffer3ab0d&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)\n\n [Exploring LSTMs](http:\u002F\u002Fblog.echen.me\u002F2017\u002F05\u002F30\u002Fexploring-lstms\u002F)\n\n [Feature Visualization](https:\u002F\u002Fdistill.pub\u002F2017\u002Ffeature-visualization\u002F)\n\n [Neural networks and deep learning](http:\u002F\u002Fneuralnetworksanddeeplearning.com\u002F)\n\n [Understanding Hinton’s Capsule Networks. Part I: Intuition.](https:\u002F\u002Fmedium.com\u002F@pechyonkin\u002Funderstanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b)\n\n [Understanding LSTM Networks -- colah's blog](http:\u002F\u002Fcolah.github.io\u002Fposts\u002F2015-08-Understanding-LSTMs\u002F)\n\n [The Unreasonable Effectiveness of Recurrent Neural Networks](http:\u002F\u002Fkarpathy.github.io\u002F2015\u002F05\u002F21\u002Frnn-effectiveness\u002F)\n\n [Andrej Carpathy blog - Hacker's guide to Neural Networks](http:\u002F\u002Fkarpathy.github.io\u002Fneuralnets\u002F)\n\n [A Beginner's Guide to Recurrent Networks and LSTMs - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https:\u002F\u002Fdeeplearning4j.org\u002Flstm.html#a-beginners-guide-to-recurrent-networks-and-lstms)\u003C\u002Fdt>\n\n [J Alammar – Explorations in touchable pixels and intelligent androids](http:\u002F\u002Fjalammar.github.io\u002F)\n\n### Keras\n\n [Guide to the Sequential model - Keras Documentation](https:\u002F\u002Fkeras.io\u002Fgetting-started\u002Fsequential-model-guide\u002F)\n\n [Keras Documentation](https:\u002F\u002Fkeras.io\u002F)\n\n [How to Use Word Embedding Layers for Deep Learning with Keras - Machine Learning Mastery](https:\u002F\u002Fmachinelearningmastery.com\u002Fuse-word-embedding-layers-deep-learning-keras\u002F)\n \n### TensorFlow\n\n [Building Input Functions with tf.estimator  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Fget_started\u002Finput_fn)\n\n [Getting Started With TensorFlow  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Fget_started\u002Fget_started)\n\n [Installing TensorFlow on Windows  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Finstall\u002Finstall_windows)\n\n [TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002F)\n\n [TensorFlow Linear Model Tutorial  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Ftutorials\u002Fwide)\n\n [TensorFlow Wide & Deep Learning Tutorial  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Ftutorials\u002Fwide_and_deep)\n\n [Using TensorFlow in Windows with a GPU | Heaton Research](http:\u002F\u002Fwww.heatonresearch.com\u002F2017\u002F01\u002F01\u002Ftensorflow-windows-gpu.html)\u003C\u002Fdt>\n\n [Installation Guide Windows :: CUDA Toolkit Documentation](http:\u002F\u002Fdocs.nvidia.com\u002Fcuda\u002Fcuda-installation-guide-microsoft-windows\u002F)\n\n [7 Steps to Mastering Machine Learning With Python](https:\u002F\u002Fwww.kdnuggets.com\u002F2015\u002F11\u002Fseven-steps-machine-learning-python.html)\u003C\u002Fdt>\n\n [A visual introduction to machine learning](http:\u002F\u002Fwww.r2d3.us\u002Fvisual-intro-to-machine-learning-part-1\u002F)\u003C\u002Fdt>\n\n [Berkeley AI Materials](http:\u002F\u002Fai.berkeley.edu\u002Flecture_videos.html)\u003C\u002Fdt>\n\n [Deep Learning For Coders fast.ai](http:\u002F\u002Fcourse.fast.ai\u002F)\u003C\u002Fdt>\n\n [Lecture Collection | Machine Learning - Stanford course](https:\u002F\u002Fwww.youtube.com\u002Fview_play_list?p=A89DCFA6ADACE599)\u003C\u002Fdt>\n\n [Microsoft Azure ML Cheat sheet](https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmachine-learning-algorithm-choice)\n\n [Pedro Domigos Machine Learning lectures](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FUWCSE\u002Fplaylists?shelf_id=16&sort=dd&view=50)\n\n [The Hitchhiker’s Guide to Machine Learning in Python](https:\u002F\u002Fmedium.com\u002F@conordewey3\u002Fthe-hitchhikers-guide-to-machine-learning-algorithms-in-python-bfad66adb378)\u003C\u002Fdt>\n\n [Top 10 Machine Learning Projects on Github](http:\u002F\u002Fwww.kdnuggets.com\u002F2015\u002F12\u002Ftop-10-machine-learning-github.html)\n\n [UCI Machine Learning Repository](http:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002F)\u003C\u002Fdt>\n\n [ISLR class videos](https:\u002F\u002Fwww.r-bloggers.com\u002Fin-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos\u002F\n\n [Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first…](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning-zero-to-hero-everything-you-need-in-order-to-compete-on-kaggle-for-the-first-time-18644e701cf1)\u003C\u002Fdt>\n\n [GOOGLE - Rules of Machine Learning:  |  Machine Learning Rules  |  Google Developers](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Frules-of-ml\u002F)\n\n [PySpark ML tutorial example](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fanindya-saha\u002FData-Science-with-Spark\u002Fblob\u002Fmaster\u002Fpredict-us-census-income-classification\u002Fpredict-us-census-income.ipynb)\u003C\u002Fdt>\n\n [Python Generators Tutorial](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Fpython-generators-tutorial\u002F)\u003C\u002Fdt>\n\n [R Markdown: The Definitive Guide](https:\u002F\u002Fbookdown.org\u002Fyihui\u002Frmarkdown\u002F)\u003C\u002Fdt>\n\n [Understanding the GitHub Flow · GitHub Guides](https:\u002F\u002Fguides.github.com\u002Fintroduction\u002Fflow\u002F)\u003C\u002Fdt>\n\n [How to Prepare for a Machine Learning Interview - Semantic Bits](https:\u002F\u002Fsemanti.ca\u002Fblog\u002F?how-to-prepare-for-a-machine-learning-interview)\u003C\u002Fdt>\n\n [Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data](https:\u002F\u002Fbecominghuman.ai\u002Fcheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463)\u003C\u002Fdt>\n\n [AI Knowledge Map: How To Classify AI Technologies](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fcognitiveworld\u002F2018\u002F08\u002F22\u002Fai-knowledge-map-how-to-classify-ai-technologies\u002F#5878d667773f)\u003C\u002Fdt>\n\n## Apache Spark\n\n [Building A Linear Regression with PySpark and MLlib](https:\u002F\u002Ftowardsdatascience.com\u002Fbuilding-a-linear-regression-with-pyspark-and-mllib-d065c3ba246a)\u003C\u002Fdt>\n\n [Complete Guide on DataFrame Operations in PySpark](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2016\u002F10\u002Fspark-dataframe-and-operations\u002F)\u003C\u002Fdt>\n\n [Install_Spark_on_Windows10.pdf](https:\u002F\u002Fwww.ics.uci.edu\u002F~shantas\u002FInstall_Spark_on_Windows10.pdf)\u003C\u002Fdt>\n\n [Introduction · Mastering Apache Spark](https:\u002F\u002Fjaceklaskowski.gitbooks.io\u002Fmastering-apache-spark\u002Fcontent\u002F)\u003C\u002Fdt>\n\n [MLlib: Main Guide - Spark 2.3.1 Documentation](http:\u002F\u002Fspark.apache.org\u002Fdocs\u002Flatest\u002Fml-guide.html)\u003C\u002Fdt>\n\n [Overview - Spark 2.3.1 Documentation](https:\u002F\u002Fspark.apache.org\u002Fdocs\u002Flatest\u002F)\u003C\u002Fdt>\n\n [RDD Programming Guide - Spark 2.3.1 Documentation](https:\u002F\u002Fspark.apache.org\u002Fdocs\u002Flatest\u002Frdd-programming-guide.html)\u003C\u002Fdt>\n\n [rdflib 5.0.0-dev — rdflib 5.0.0-dev documentation](https:\u002F\u002Frdflib3.readthedocs.io\u002Fen\u002Flatest\u002Findex.html)\u003C\u002Fdt>\n\n [Spark SQL and DataFrames - Spark 2.3.1 Documentation](http:\u002F\u002Fspark.apache.org\u002Fdocs\u002Flatest\u002Fsql-programming-guide.html)\u003C\u002Fdt>\n\n [Welcome to Spark Python API Docs! — PySpark 2.3.1 documentation](http:\u002F\u002Fspark.apache.org\u002Fdocs\u002Flatest\u002Fapi\u002Fpython\u002F)\u003C\u002Fdt>\n\n## Cloud computing\n\n [Why You Should Consider Google AI Platform For Your Machine Learning Projects](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fjanakirammsv\u002F2019\u002F04\u002F16\u002Fwhy-you-should-consider-google-ai-platform-for-your-machine-learning-projects\u002Famp\u002F)\n \n [Cloud Computing Tutorial for Beginners | Cloud Computing Explained | Cloud Computing | Simplilearn - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=RWgW-CgdIk0)\u003C\u002Fdt>\n\n### Computation, Computing\n\n [A Short Guide to Hard Problems | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fa-short-guide-to-hard-problems-20180716\u002F?fbclid=IwAR2Yz76T1uE835BC7STAdIZUA-xR4cPUI2BeC-yS7Bwkk96fUPOePeyNCZg)\u003C\u002Fdt>\n\n\n### Data Mining\n\n [The 10 Mining Techniques Data Scientists Need for Their Toolbox](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-10-mining-techniques-data-scientists-need-for-their-toolbox-ae15a5733b02)\u003C\u002Fdt>\n\n [Wikipedia Data Science: Working with the World’s Largest Encyclopedia](https:\u002F\u002Ftowardsdatascience.com\u002Fwikipedia-data-science-working-with-the-worlds-largest-encyclopedia-c08efbac5f5c)\u003C\u002Fdt>\n\n\n### Data wrangling related\n\n  [A Brief Overview of Outlier Detection Techniques – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fa-brief-overview-of-outlier-detection-techniques-1e0b2c19e561)\u003C\u002Fdt>\n\n## Docker, Containers\n\n [A Beginner-Friendly Introduction to Containers, VMs and Docker](https:\u002F\u002Fmedium.freecodecamp.org\u002Fa-beginner-friendly-introduction-to-containers-vms-and-docker-79a9e3e119b)\u003C\u002Fdt>\n\n [A fast and easy Docker tutorial for beginners (video series)](https:\u002F\u002Fmedium.freecodecamp.org\u002Fdocker-quick-start-video-tutorials-1dfc575522a0)\u003C\u002Fdt>\n\n [Docker Compose in 12 Minutes - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Qw9zlE3t8Ko)\u003C\u002Fdt>\n\n [How to Install and Use Docker on Ubuntu 18.04 | DigitalOcean](https:\u002F\u002Fwww.digitalocean.com\u002Fcommunity\u002Ftutorials\u002Fhow-to-install-and-use-docker-on-ubuntu-18-04)\u003C\u002Fdt>\n\n [How to Install Docker On Ubuntu 18.04 Bionic Beaver - LinuxConfig.org](https:\u002F\u002Flinuxconfig.org\u002Fhow-to-install-docker-on-ubuntu-18-04-bionic-beaver)\u003C\u002Fdt>\n\n [Learn Docker in 12 Minutes 🐳 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YFl2mCHdv24)\u003C\u002Fdt>\n\n [What is a Container? - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=EnJ7qX9fkcU)\u003C\u002Fdt>\n\n [What is Docker | Docker Tutorial for Beginners | Docker Container | DevOps Tools | Edureka - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=lcQfQRDAMpQ)\u003C\u002Fdt>\n\n [Building Your Own Data Science Platform With Python & Docker - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=NC2wXYHBrL0)\u003C\u002Fdt>\n\n### Interview related\n\n [50+ Data Structure and Algorithms Interview Questions for Programmers](https:\u002F\u002Fhackernoon.com\u002F50-data-structure-and-algorithms-interview-questions-for-programmers-b4b1ac61f5b0)\u003C\u002Fdt>\n\n\n## Web Technologies\n\n### REST, API, Microservice\n\n  [GraphQL vs. REST – Apollo GraphQL](https:\u002F\u002Fblog.apollographql.com\u002Fgraphql-vs-rest-5d425123e34b)\u003C\u002Fdt>\n\n [Microservices, APIs, and Swagger: How They Fit Together | Swagger](https:\u002F\u002Fswagger.io\u002Fblog\u002Fapi-strategy\u002Fmicroservices-apis-and-swagger\u002F)\u003C\u002Fdt>\n\n [REST API concepts and examples - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7YcW25PHnAA)\u003C\u002Fdt>\n\n [Web Architecture 101 – VideoBlocks Product & Engineering](https:\u002F\u002Fengineering.videoblocks.com\u002Fweb-architecture-101-a3224e126947)\u003C\u002Fdt>\n\n [REST API & RESTful Web Services Explained - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=LooL6_chvN4)\u003C\u002Fdt>\n\n [Our Collections – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Four-collections-3920888f831c)\u003C\u002Fdt>\n\n### JSON, XML, HTML\n\n[JSON Crash Course - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wI1CWzNtE-M)\n[Can I use... Support tables for HTML5, CSS3, etc](https:\u002F\u002Fcaniuse.com\u002F)\n[HTML5 Form Validation Examples \u003C HTML | The Art of Web](http:\u002F\u002Fwww.the-art-of-web.com\u002Fhtml\u002Fhtml5-form-validation\u002F)\n \n### CSS\n\n [The CSS Handbook: a handy guide to CSS for developers](https:\u002F\u002Fmedium.freecodecamp.org\u002Fthe-css-handbook-a-handy-guide-to-css-for-developers-b56695917d11)\n \n [Creating a Simple Website with HTML and CSS - Part 1 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=A3Xgz9PHGuA)\u003C\u002Fdt>\n\n [CSS Introduction - W3Schools](https:\u002F\u002Fwww.w3schools.com\u002Fcss\u002Fcss_intro.asp)\u003C\u002Fdt>\n\n [Learn CSS in 12 Minutes - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0afZj1G0BIE)\u003C\u002Fdt>\n\n### JavaScript\n\n [Beginner JavaScript Tutorial - 1 - Introduction to JavaScript - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yQaAGmHNn9s)\u003C\u002Fdt>\n\n [Eloquent JavaScript](http:\u002F\u002Feloquentjavascript.net\u002F) \n\n [Form Validation with JavaScript - Check for an Empty Text Field - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Pc2e2YpKArg) \n\n [JavaScript Basics Part 1](https:\u002F\u002Fwww.htmlgoodies.com\u002Fprimers\u002Fjsp\u002Farticle.php\u002F3586411) \n\n [JavaScript beginner tutorial 30 - form validation text boxes and passwords - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=y5UEXujzSag) \n\n [JavaScript: Simple Form Validation - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_Z-0cwONN6c) \n\n [Learn JavaScript in 12 Minutes - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Ukg_U3CnJWI) \n\n [Machine Learning with JavaScript : Part 1 – Hacker Noon](https:\u002F\u002Fhackernoon.com\u002Fmachine-learning-with-javascript-part-1-9b97f3ed4fe5) \n\n [Machine Learning with JavaScript : Part 2 – Hacker Noon](https:\u002F\u002Fhackernoon.com\u002Fmachine-learning-with-javascript-part-2-da994c17d483) \n\n [W3School - JavaScript Form Validation](https:\u002F\u002Fwww.w3schools.com\u002Fjs\u002Fjs_validation.asp) \n\n [W3schools - JavaScript Tutorial](https:\u002F\u002Fwww.w3schools.com\u002Fjs\u002F) \n\n [ClearlyDecoded.com - Yaakov Chaikin](https:\u002F\u002Fclearlydecoded.com\u002F) \n\n [GoDaddy Hosting Account Getting Started Guide](https:\u002F\u002Fwww.godaddy.com\u002Fhelp\u002Fhosting-account-getting-started-guide-1361) \n\n [How to Make A Website in 2018 - Web Hosting Guide | WHSR](https:\u002F\u002Fwww.webhostingsecretrevealed.net\u002Fweb-hosting-beginner-guide\u002F) \n\n [jhu-ep-coursera\u002Ffullstack-course4: Example code for HTML, CSS, and Javascript for Web Developers Coursera Course](https:\u002F\u002Fgithub.com\u002Fjhu-ep-coursera\u002Ffullstack-course4)\n \n [Free JavaScript Tutorial - Scaler](https:\u002F\u002Fwww.scaler.com\u002Ftopics\u002Fjavascript\u002F) \n\n## LaTeX, Markdown, reST\n\n [Art of Problem Solving - LaTeX symbols](https:\u002F\u002Fartofproblemsolving.com\u002Fwiki\u002Findex.php\u002FLaTeX:Symbols)\u003C\u002Fdt>\n\n [Detexify LaTeX handwritten symbol recognition](http:\u002F\u002Fdetexify.kirelabs.org\u002Fclassify.html)\u003C\u002Fdt>\n\n [http:\u002F\u002Fquicklatex.com\u002F](http:\u002F\u002Fquicklatex.com\u002F)\u003C\u002Fdt>\n \n [LaTeX symbol Wiki](https:\u002F\u002Foeis.org\u002Fwiki\u002FList_of_LaTeX_mathematical_symbols#Set_and.2For_logic_notation)\n\n [The Comprehensive LaTeX Symbol ListThe Comprehensive LaTeX Symbol List - symbols-a4.pdf](http:\u002F\u002Fctan.math.illinois.edu\u002Finfo\u002Fsymbols\u002Fcomprehensive\u002Fsymbols-a4.pdf)\u003C\u002Fdt>\n\n [Pandoc - Pandoc User’s Guide](https:\u002F\u002Fpandoc.org\u002FMANUAL.html#pandocs-markdown)\u003C\u002Fdt>\n\n [MathJax Documentation — MathJax 2.7 documentation](http:\u002F\u002Fdocs.mathjax.org\u002Fen\u002Flatest\u002F)\u003C\u002Fdt>\n\n [TeX Commands available in MathJax](http:\u002F\u002Fwww.onemathematicalcat.org\u002FMathJaxDocumentation\u002FTeXSyntax.htm)\u003C\u002Fdt>\n\n## Linux, OS\n \n [How to Install Ubuntu Linux on VirtualBox on Windows 10 [Step by Step Guide] | It's FOSS](https:\u002F\u002Fitsfoss.com\u002Finstall-linux-in-virtualbox\u002F)\u003C\u002Fdt>\n\n [Microsoft PowerShell Tutorial & Training Course – Microsoft Virtual Academy](https:\u002F\u002Fmva.microsoft.com\u002Fen-us\u002Ftraining-courses\u002Fgetting-started-with-microsoft-powershell-8276?l=r54IrOWy_2304984382)\u003C\u002Fdt>\n\n [Most Popular Linux Distributions and Why They Dominate the Market](https:\u002F\u002Fblog.storagecraft.com\u002Fpopular-linux-distributions-dominate-market\u002F)\u003C\u002Fdt>\n\n [The Dead-Simple Guide to Installing a Linux Virtual Machine on Windows - StorageCraft Technology Corporation](https:\u002F\u002Fblog.storagecraft.com\u002Fthe-dead-simple-guide-to-installing-a-linux-virtual-machine-on-windows\u002F)\u003C\u002Fdt>\n\n [[Solved] Could not get lock \u002Fvar\u002Flib\u002Fdpkg\u002Flock Error in Ubuntu | It's FOSS](https:\u002F\u002Fitsfoss.com\u002Fcould-not-get-lock-error\u002F)\u003C\u002Fdt>\n\n## Time series \n\n [Time Series Analysis in Python: An Introduction – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Ftime-series-analysis-in-python-an-introduction-70d5a5b1d52a)\u003C\u002Fdt>\n\n [RJT1990\u002Fpyflux: Open source time series library for Python](https:\u002F\u002Fgithub.com\u002FRJT1990\u002Fpyflux)\u003C\u002Fdt>\n\n [MaxBenChrist\u002Fawesome_time_series_in_python: This curated list contains python packages for time series analysis](https:\u002F\u002Fgithub.com\u002FMaxBenChrist\u002Fawesome_time_series_in_python)\u003C\u002Fdt>\n\n [Getting Started with Time Series — PyFlux 0.4.7 documentation](http:\u002F\u002Fpyflux.readthedocs.io\u002Fen\u002Flatest\u002Fgetting_started.html)\u003C\u002Fdt>\n\n [Introduction to ARIMA models](https:\u002F\u002Fpeople.duke.edu\u002F~rnau\u002F411arim.htm)\u003C\u002Fdt>\n\n [Complete guide to create a Time Series Forecast (with Codes in Python)](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2016\u002F02\u002Ftime-series-forecasting-codes-python\u002F)\u003C\u002Fdt>\n\n [How to Create an ARIMA Model for Time Series Forecasting with Python](https:\u002F\u002Fmachinelearningmastery.com\u002Farima-for-time-series-forecasting-with-python\u002F)\n \n [Time series with Siraj course by Kaggle](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Ftime-series-with-siraj)\n\n## Interesting Articles\n\n [Debunking The Myths And Reality Of Artificial Intelligence - Forbes](https:\u002F\u002Fwww.forbes.com\u002Fsites\u002Fcognitiveworld\u002F2019\u002F04\u002F22\u002Fdebunking-the-myths-and-reality-of-artificial-intelligence\u002F#614c70e743b5)\n \n [Artificial Intelligence — The Revolution Hasn’t Happened Yet](https:\u002F\u002Fmedium.com\u002F@mijordan3\u002Fartificial-intelligence-the-revolution-hasnt-happened-yet-5e1d5812e1e7)\u003C\u002Fdt>\n\n [Artificial Intelligence Learns to Learn Entirely on Its Own | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fartificial-intelligence-learns-to-learn-entirely-on-its-own-20171018\u002F?utm_content=buffer578b7&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)\u003C\u002Fdt>\n\n [Can Buddhist philosophy explain what came before the Big Bang? | Aeon Essays](https:\u002F\u002Faeon.co\u002Fessays\u002Fcan-buddhist-philosophy-explain-what-came-before-the-big-bang)\u003C\u002Fdt>\n\n [Coming to Grips with the Implications of Quantum Mechanics - Scientific American Blog Network](https:\u002F\u002Fblogs.scientificamerican.com\u002Fobservations\u002Fcoming-to-grips-with-the-implications-of-quantum-mechanics\u002F)\u003C\u002Fdt>\n\n [Did Toolmaking Pave the Road for Human Language? - The Atlantic](https:\u002F\u002Fwww.theatlantic.com\u002Fscience\u002Farchive\u002F2018\u002F06\u002Ftoolmaking-language-brain\u002F562385\u002F)\u003C\u002Fdt>\n\n [Edward Witten Ponders the Nature of Reality | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fedward-witten-ponders-the-nature-of-reality-20171128\u002F)\u003C\u002Fdt>\n\n[Gatekeeping and Elitism in Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fgatekeeping-and-elitism-in-data-science-74cf19cd5744)\n\n [How Do Aliens Solve Climate Change? - The Atlantic](https:\u002F\u002Fwww.theatlantic.com\u002Fscience\u002Farchive\u002F2018\u002F05\u002Fhow-do-aliens-solve-climate-change\u002F561479\u002F)\u003C\u002Fdt>\n\n [How I Learned to Stop Worrying About the LHC’s Missing New Physics](http:\u002F\u002Fnautil.us\u002Fissue\u002F64\u002Fthe-unseen\u002Ffine-tuning-is-just-fine)\u003C\u002Fdt>\n\n [How Information Got Re-Invented – Limits – Medium](https:\u002F\u002Fmedium.com\u002Fs\u002Fnautilus-limits\u002Fhow-information-got-re-invented-888fea36c4a5)\u003C\u002Fdt>\n\n [How Social Media Endangers Knowledge | WIRED](https:\u002F\u002Fwww.wired.com\u002Fstory\u002Fwikipedias-fate-shows-how-the-web-endangers-knowledge\u002F)\u003C\u002Fdt>\n\n [In These Small Cities, AI Advances Could Be Costly - MIT Technology Review](https:\u002F\u002Fwww.technologyreview.com\u002Fs\u002F609076\u002Fin-these-small-cities-ai-advances-could-be-costly\u002F?utm_campaign=Owned+Social&utm_source=Facebook&utm_medium=Owned+Social)\u003C\u002Fdt>\n\n [Inside Amazon’s $3.5 million competition to make Alexa chat like a human - The Verge](https:\u002F\u002Fwww.theverge.com\u002F2018\u002F6\u002F13\u002F17453994\u002Famazon-alexa-prize-2018-competition-conversational-ai-chatbots)\u003C\u002Fdt>\n\n [Let’s make private data into a public good - MIT Technology Review](https:\u002F\u002Fwww.technologyreview.com\u002Fs\u002F611489\u002Flets-make-private-data-into-a-public-good\u002F)\u003C\u002Fdt>\n\n [On Chomsky and the Two Cultures of Statistical Learning](http:\u002F\u002Fnorvig.com\u002Fchomsky.html)\u003C\u002Fdt>\n\n [Quantum Leaps in Quantum Computing? - Scientific American](https:\u002F\u002Fwww.scientificamerican.com\u002Farticle\u002Fquantum-leaps-in-quantum-computing\u002F?utm_source=facebook&utm_medium=social&utm_campaign=sa-editorial-social&utm_content&utm_term=physics_sa-magazine_text_free)\u003C\u002Fdt>\n\n [Strategy vs. Tactics: What's the Difference and Why Does it Matter?](https:\u002F\u002Ffs.blog\u002F2018\u002F08\u002Fstrategy-vs-tactics\u002F)\u003C\u002Fdt>\n\n [The case for genetically engineering a smarter human-cyborg population to avoid the threat of existential catastrophe.](https:\u002F\u002Fslate.com\u002Ftechnology\u002F2018\u002F09\u002Fgenetic-engineering-to-stop-doomsday.html)\u003C\u002Fdt>\n\n [The Fragile State of the Midwest’s Public Universities - The Atlantic](https:\u002F\u002Fwww.theatlantic.com\u002Fbusiness\u002Farchive\u002F2017\u002F10\u002Fmidwestern-public-research-universities-funding\u002F542889\u002F?utm_source=vxfb)\u003C\u002Fdt>\n\n [The Quantum Thermodynamics Revolution | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fthe-quantum-thermodynamics-revolution-20170502?utm_content=buffere2607&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)\u003C\u002Fdt>\n\n [The Way You Read Books Says A Lot About Your Intelligence, Here’s Why](https:\u002F\u002Fmedium.com\u002Fthe-mission\u002Fthe-way-you-read-books-says-a-lot-about-your-intelligence-find-out-why-c2127b00eb03)\u003C\u002Fdt>\n\n [To Build Truly Intelligent Machines, Teach Them Cause and Effect | Quanta Magazine](https:\u002F\u002Fwww.quantamagazine.org\u002Fto-build-truly-intelligent-machines-teach-them-cause-and-effect-20180515\u002F)\u003C\u002Fdt>\n\n [Why Is American Mass Transit So Bad? It's a Long Story. - CityLab](https:\u002F\u002Fwww.citylab.com\u002Ftransportation\u002F2018\u002F08\u002Fhow-america-killed-transit\u002F568825\u002F)\u003C\u002Fdt>\n\n [Yuval Noah Harari on what 2050 has in store for humankind | WIRED UK](https:\u002F\u002Fwww.wired.co.uk\u002Farticle\u002Fyuval-noah-harari-extract-21-lessons-for-the-21st-century)\u003C\u002Fdt>\n\n [Yuval Noah Harari on Why Technology Favors Tyranny - The Atlantic](https:\u002F\u002Fwww-theatlantic-com.cdn.ampproject.org\u002Fc\u002Fs\u002Fwww.theatlantic.com\u002Famp\u002Farticle\u002F568330\u002F)\u003C\u002Fdt>\n\n [Yuval Noah Harari: ‘The idea of free information is extremely dangerous’ | Culture | The Guardian](https:\u002F\u002Fwww.theguardian.com\u002Fculture\u002F2018\u002Faug\u002F05\u002Fyuval-noah-harari-free-information-extremely-dangerous-interview-21-lessons)\u003C\u002Fdt>\n\n [Beyond Weird: Decoherence, Quantum Weirdness, and Schrödinger's Cat - The Atlantic](https:\u002F\u002Fwww.theatlantic.com\u002Fscience\u002Farchive\u002F2018\u002F10\u002Fbeyond-weird-decoherence-quantum-weirdness-schrodingers-cat\u002F573448\u002F)\u003C\u002Fdt>\n\n [Life Is a Braid in Spacetime – Time – Medium](https:\u002F\u002Fmedium.com\u002Fs\u002Fnautilus-time\u002Flife-is-a-braid-in-spacetime-16dbf74d105f)\u003C\u002Fdt>\n\n [Mental Models: How to Train Your Brain to Think in New Ways - James Clear - Pocket](https:\u002F\u002Fgetpocket.com\u002Fexplore\u002Fitem\u002Fmental-models-how-to-train-your-brain-to-think-in-new-ways-820549098)\u003C\u002Fdt>\n\n [Don’t Compete. Create! - Darius Foroux - Pocket](https:\u002F\u002Fgetpocket.com\u002Fexplore\u002Fitem\u002Fdon-t-compete-create-2068896981)\u003C\u002Fdt>\n\n [Tesla will live and die by the Gigafactory - The Verge](https:\u002F\u002Fwww.theverge.com\u002Ftransportation\u002F2018\u002F11\u002F30\u002F18118451\u002Ftesla-gigafactory-nevada-video-elon-musk-jobs-model-3)\u003C\u002Fdt>\n\n [So you want to be a Research Scientist – Vincent Vanhoucke – Medium](https:\u002F\u002Fmedium.com\u002F@vanhoucke\u002Fso-you-want-to-be-a-research-scientist-363c075d3d4c)\u003C\u002Fdt>\n\n [Homeland Security Will Let Software Flag Potential Terrorists](https:\u002F\u002Ftheintercept.com\u002F2018\u002F12\u002F03\u002Fair-travel-surveillance-homeland-security\u002F)\u003C\u002Fdt>\n\n [What Happens When a World Order Ends](https:\u002F\u002Fwww.foreignaffairs.com\u002Farticles\u002F2018-12-11\u002Fhow-world-order-ends)\u003C\u002Fdt>\n\n [Kevin Slavin: How algorithms shape our world | TED Talk](https:\u002F\u002Fwww.ted.com\u002Ftalks\u002Fkevin_slavin_how_algorithms_shape_our_world?language=en#t-229771)\u003C\u002Fdt>\n\n [The Brain's Autopilot Mechanism Steers Consciousness - Scientific American](https:\u002F\u002Fwww.scientificamerican.com\u002Farticle\u002Fthe-brains-autopilot-mechanism-steers-consciousness\u002F)\u003C\u002Fdt>\n\n [What is Intelligence? – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fwhat-is-intelligence-a69cbd8bb1b4)\u003C\u002Fdt>\n\n [This Is Exactly How You Should Train Yourself To Be Smarter - Michael Simmons - Pocket](https:\u002F\u002Fgetpocket.com\u002Fexplore\u002Fitem\u002Fthis-is-exactly-how-you-should-train-yourself-to-be-smarter)\u003C\u002Fdt>\n\n [How to be More Productive and Eliminate Time Wasting Activities by Using the “Eisenhower Box” - James Clear - Pocket](https:\u002F\u002Fgetpocket.com\u002Fexplore\u002Fitem\u002Fhow-to-be-more-productive-and-eliminate-time-wasting-activities-by-using-the-eisenhower-box)\u003C\u002Fdt>\n\n [The blind spot of science is the neglect of lived experience | Aeon Essays](https:\u002F\u002Faeon.co\u002Fessays\u002Fthe-blind-spot-of-science-is-the-neglect-of-lived-experience)\u003C\u002Fdt>\n\n ## Julia\n\n [A Complete Tutorial to Learn Data Science with Julia from Scratch](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2017\u002F10\u002Fcomprehensive-tutorial-learn-data-science-julia-from-scratch\u002F)\u003C\u002Fdt>\n\n## Machine Learning\n\n### Experiment tracking\n[ML Experiment Tracking: What It Is, Why It Matters, and How to Implement It](https:\u002F\u002Fneptune.ai\u002Fblog\u002Fml-experiment-tracking)\n\n### Fairness and bias\n[Evaluating machine learning models for fairness and bias](https:\u002F\u002Ftowardsdatascience.com\u002Fevaluating-machine-learning-models-fairness-and-bias-4ec82512f7c3)\n\n### Deployment of ML\n\n[Creating data science APIs with Flask](https:\u002F\u002Ffaculty.ai\u002Fblog\u002Fcreating-data-science-apis-with-flask\u002F)\n\n[Flask and Heroku for online Machine Learning deployment](https:\u002F\u002Ftowardsdatascience.com\u002Fflask-and-heroku-for-online-machine-learning-deployment-425beb54a274)\n\n[Overview of the different approaches to putting Machine Learning (ML) models in production](https:\u002F\u002Fmedium.com\u002Fanalytics-and-data\u002Foverview-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:\u002F\u002Ftowardsdatascience.com\u002Fguide-building-data-science-web-application-with-react-nodejs-and-mysql-1c55416ff0fb)\u003C\u002Fdt>\n\n [A beginner’s guide to training and deploying machine learning models using Python](https:\u002F\u002Fmedium.freecodecamp.org\u002Fa-beginners-guide-to-training-and-deploying-machine-learning-models-using-python-48a313502e5a)\u003C\u002Fdt>\n\n [A Guide to Scaling Machine Learning Models in Production](https:\u002F\u002Fhackernoon.com\u002Fa-guide-to-scaling-machine-learning-models-in-production-aa8831163846)\u003C\u002Fdt>\n\n [Deploying Keras Deep Learning Models with Flask – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fdeploying-keras-deep-learning-models-with-flask-5da4181436a2)\u003C\u002Fdt>\n\n [Deploying Machine Learning at Scale - Algorithmia Blog](https:\u002F\u002Fblog.algorithmia.com\u002Fdeploying-machine-learning-at-scale\u002F)\u003C\u002Fdt>\n\n [Deploying Machine Learning has never been so easy – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fhttps-towardsdatascience-com-deploying-machine-learning-has-never-been-so-easy-bbdb500a39a)\u003C\u002Fdt>\n\n [Quora - How do you take a machine learning model to production?](https:\u002F\u002Fwww.quora.com\u002FHow-do-you-take-a-machine-learning-model-to-production)\u003C\u002Fdt>\n\n [Tutorial to deploy Machine Learning model in Production as API with Flask](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2017\u002F09\u002Fmachine-learning-models-as-apis-using-flask\u002F)\n\n [From Big Data to micro-services: how to serve Spark-trained models through AWS lambdas](https:\u002F\u002Ftowardsdatascience.com\u002Ffrom-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:\u002F\u002Fblog.insightdatascience.com\u002Fhow-to-deliver-on-machine-learning-projects-c8d82ce642b0)\n\n [Deploying a Keras Deep Learning Model as a Web Application in P](https:\u002F\u002Ftowardsdatascience.com\u002Fdeploying-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:\u002F\u002Ftowardsdatascience.com\u002Fgenetic-algorithm-implementation-in-python-5ab67bb124a6)\n\n [Introduction to Optimization with Genetic Algorithm](https:\u002F\u002Ftowardsdatascience.com\u002Fintroduction-to-optimization-with-genetic-algorithm-2f5001d9964b)\n\n [A tutorial on Differential Evolution with Python · Pablo R. Mier](https:\u002F\u002Fpablormier.github.io\u002F2017\u002F09\u002F05\u002Fa-tutorial-on-differential-evolution-with-python\u002F)\n\n### Keras\n\n [Guide to the Sequential model - Keras Documentation](https:\u002F\u002Fkeras.io\u002Fgetting-started\u002Fsequential-model-guide\u002F)\u003C\u002Fdt>\n\n [Keras Documentation](https:\u002F\u002Fkeras.io\u002F)\u003C\u002Fdt>\n\n [How to Use Word Embedding Layers for Deep Learning with Keras - Machine Learning Mastery](https:\u002F\u002Fmachinelearningmastery.com\u002Fuse-word-embedding-layers-deep-learning-keras\u002F)\u003C\u002Fdt>\n\n### Neural Network\n\n### Videos\n\n [Brandon Rohrer - Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=WCUNPb-5EYI)\u003C\u002Fdt>\n\n [CS231n Lecture 10 - Recurrent Neural Networks, Image Captioning, LSTM - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=iX5V1WpxxkY)\u003C\u002Fdt>\n\n [Nuts and Bolts of Applying Deep Learning (Andrew Ng) - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=F1ka6a13S9I)\u003C\u002Fdt>\n\n [Siraj Raval - LSTM Networks - The Math of Intelligence (Week 8) - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9zhrxE5PQgY)\u003C\u002Fdt>\n\n [Siraj Raval - Recurrent Neural Networks - The Math of Intelligence (Week 5) - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BwmddtPFWtA)\u003C\u002Fdt>\n\n [Andrew Ng: Artificial Intelligence is the New Electricity - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=21EiKfQYZXc)\u003C\u002Fdt>\n\n [A Beginner's Guide to Recurrent Networks and LSTMs - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https:\u002F\u002Fdeeplearning4j.org\u002Flstm.html#a-beginners-guide-to-recurrent-networks-and-lstms)\u003C\u002Fdt>\n\n [A Neural Network Playground](http:\u002F\u002Fplayground.tensorflow.org\u002F#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)\u003C\u002Fdt>\n\n [A Visual Guide to Evolution Strategies](http:\u002F\u002Fblog.otoro.net\u002F2017\u002F10\u002F29\u002Fvisual-evolution-strategies\u002F)\u003C\u002Fdt>\n\n [Andrej Carpathy blog - Hacker's guide to Neural Networks](http:\u002F\u002Fkarpathy.github.io\u002Fneuralnets\u002F)\u003C\u002Fdt>\n\n [Best (and Free!!) Resources to understand Nuts and Bolts of Deep learning](https:\u002F\u002Fhackernoon.com\u002Fbest-and-free-resources-to-understand-nuts-and-bolts-of-deep-learning-9c51166ffdf5)\u003C\u002Fdt>\n\n [But what *is* a Neural Network? | Deep learning, chapter 1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?time_continue=80&v=aircAruvnKk)\u003C\u002Fdt>\n\n [Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data](https:\u002F\u002Fbecominghuman.ai\u002Fcheat-sheets-for-ai-neural-networks-machine-learning-deep-learning-big-data-678c51b4b463)\u003C\u002Fdt>\n\n [Convolutional Networks in Java - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM](https:\u002F\u002Fdeeplearning4j.org\u002Fconvolutionalnets.html)\u003C\u002Fdt>\n\n [CS231n Convolutional Neural Networks for Visual Recognition](http:\u002F\u002Fcs231n.github.io\u002Fconvolutional-networks\u002F)\u003C\u002Fdt>\n\n [Deep Dive into Math Behind Deep Networks – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fhttps-medium-com-piotr-skalski92-deep-dive-into-deep-networks-math-17660bc376ba)\u003C\u002Fdt>\n\n [Deep Learning Fundamentals - Cognitive Class](https:\u002F\u002Fcognitiveclass.ai\u002Fcourses\u002Fintroduction-deep-learning\u002F?utm_content=buffer3ab0d&utm_medium=social&utm_source=facebook.com&utm_campaign=buffer)\u003C\u002Fdt>\n\n [Exploring LSTMs](http:\u002F\u002Fblog.echen.me\u002F2017\u002F05\u002F30\u002Fexploring-lstms\u002F)\u003C\u002Fdt>\n\n [Feature Visualization](https:\u002F\u002Fdistill.pub\u002F2017\u002Ffeature-visualization\u002F)\u003C\u002Fdt>\n\n [J Alammar – Explorations in touchable pixels and intelligent androids](http:\u002F\u002Fjalammar.github.io\u002F)\u003C\u002Fdt>\n\n [Learning without Backpropagation: Intuition and Ideas (Part 1) – Tom Breloff](http:\u002F\u002Fwww.breloff.com\u002Fno-backprop\u002F)\u003C\u002Fdt>\n\n [Must know Information Theory concepts in Deep Learning (AI)](https:\u002F\u002Ftowardsdatascience.com\u002Fmust-know-information-theory-concepts-in-deep-learning-ai-e54a5da9769d)\u003C\u002Fdt>\n\n [Neural networks and deep learning](http:\u002F\u002Fneuralnetworksanddeeplearning.com\u002F)\u003C\u002Fdt>\n\n [Neural Style Transfer: Creating Art with Deep Learning using tf.keras and eager execution](https:\u002F\u002Fmedium.com\u002Ftensorflow\u002Fneural-style-transfer-creating-art-with-deep-learning-using-tf-keras-and-eager-execution-7d541ac31398)\u003C\u002Fdt>\n\n [The Unreasonable Effectiveness of Recurrent Neural Networks](http:\u002F\u002Fkarpathy.github.io\u002F2015\u002F05\u002F21\u002Frnn-effectiveness\u002F)\u003C\u002Fdt>\n\n [Understanding Hinton’s Capsule Networks. Part I: Intuition.](https:\u002F\u002Fmedium.com\u002F@pechyonkin\u002Funderstanding-hintons-capsule-networks-part-i-intuition-b4b559d1159b)\u003C\u002Fdt>\n\n [Understanding LSTM Networks -- colah's blog](http:\u002F\u002Fcolah.github.io\u002Fposts\u002F2015-08-Understanding-LSTMs\u002F)\u003C\u002Fdt>\n\n [A Neural Network in 13 lines of Python (Part 2 - Gradient Descent) - i am trask](https:\u002F\u002Fiamtrask.github.io\u002F2015\u002F07\u002F27\u002Fpython-network-part2\u002F)\u003C\u002Fdt>\n\n [How Do Artificial Neural Networks Learn? – Towards Data Science](https:\u002F\u002Ftowardsdatascience.com\u002Fhow-do-artificial-neural-networks-learn-773e46399fc7)\u003C\u002Fdt>\n\n [The Neural Network Zoo - The Asimov Institute](http:\u002F\u002Fwww.asimovinstitute.org\u002Fneural-network-zoo\u002F)\u003C\u002Fdt>\n\n [A History of Deep Learning | Import.io](https:\u002F\u002Fwww.import.io\u002Fpost\u002Fhistory-of-deep-learning\u002F)\u003C\u002Fdt>\n\n [The Ultimate NanoBook to understand Deep Learning based Image Classifier](https:\u002F\u002Ftowardsdatascience.com\u002Fhttps-medium-com-rishabh-grg-the-ultimate-nanobook-to-understand-deep-learning-based-image-classifier-33f43fea8327)\u003C\u002Fdt>\n\n### NLP\n\n [How to solve 90% of NLP problems: a step-by-step guide](https:\u002F\u002Fblog.insightdatascience.com\u002Fhow-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e)\u003C\u002Fdt>\n\n [Coding & English Lit: Natural Language Processing in Python](https:\u002F\u002Fmedium.com\u002F@kellylougheed\u002Fcoding-english-lit-natural-language-processing-in-python-ba8ebae4dde3)\u003C\u002Fdt>\n\n [TextBlob: Simplified Text Processing — TextBlob 0.15.1 documentation](https:\u002F\u002Ftextblob.readthedocs.io\u002Fen\u002Fdev\u002F)\u003C\u002Fdt>\n\n [Python Regular Expression Tutorial (article) - DataCamp](https:\u002F\u002Fwww.datacamp.com\u002Fcommunity\u002Ftutorials\u002Fpython-regular-expression-tutorial)\u003C\u002Fdt>\n \n [Stanford NLP](https:\u002F\u002Fstanfordnlp.github.io\u002Fstanfordnlp\u002F)\n\n### Reinforcement Learning\n\n[Reinforcement Learning Course - Full Machine Learning Tutorial](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ELE2_Mftqoc)\n\n [A brief introduction to reinforcement learning – freeCodeCamp.org](https:\u002F\u002Fmedium.freecodecamp.org\u002Fa-brief-introduction-to-reinforcement-learning-7799af5840db)\u003C\u002Fdt>\n\n [An introduction to Reinforcement Learning – freeCodeCamp.org](https:\u002F\u002Fmedium.freecodecamp.org\u002Fan-introduction-to-reinforcement-learning-4339519de419)\u003C\u002Fdt>\n\n [Key Papers in Deep RL — Spinning Up documentation](https:\u002F\u002Fspinningup.openai.com\u002Fen\u002Flatest\u002Fspinningup\u002Fkeypapers.html#model-free-rl)\u003C\u002Fdt>\n\n [Nuts & Bolts of Reinforcement Learning: Model Based Planning using Dynamic Programming](https:\u002F\u002Fmedium.com\u002Fanalytics-vidhya\u002Fnuts-bolts-of-reinforcement-learning-model-based-planning-using-dynamic-programming-d71d52011b53)\u003C\u002Fdt>\n\n [Reinforcement Learning: A Deep Dive | Toptal](https:\u002F\u002Fwww.toptal.com\u002Fmachine-learning\u002Fdeep-dive-into-reinforcement-learning)\u003C\u002Fdt>\n\n [Part 1: Key Concepts in RL — Spinning Up documentation](https:\u002F\u002Fspinningup.openai.com\u002Fen\u002Flatest\u002Fspinningup\u002Frl_intro.html)\u003C\u002Fdt>\n\n [Dissecting Reinforcement Learning-Part.1](https:\u002F\u002Fmpatacchiola.github.io\u002Fblog\u002F2016\u002F12\u002F09\u002Fdissecting-reinforcement-learning.html)\u003C\u002Fdt>\n\n [Reinforcement Q-Learning from Scratch in Python with OpenAI Gym – LearnDataSci](https:\u002F\u002Fwww.learndatasci.com\u002Ftutorials\u002Freinforcement-q-learning-scratch-python-openai-gym\u002F)\u003C\u002Fdt>\n\n [Google AI Blog: Curiosity and Procrastination in Reinforcement Learning](https:\u002F\u002Fai.googleblog.com\u002F2018\u002F10\u002Fcuriosity-and-procrastination-in.html)\u003C\u002Fdt>\n\n [Reinforcement Learning: Monte Carlo Learning using OpenAI Gym](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2018\u002F11\u002Freinforcement-learning-introduction-monte-carlo-learning-openai-gym\u002F?utm_source=linkedin.com)\u003C\u002Fdt>\n\n### TensorFlow\n\n [Building Input Functions with tf.estimator  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Fget_started\u002Finput_fn)\u003C\u002Fdt>\n\n [Getting Started With TensorFlow  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Fget_started\u002Fget_started)\u003C\u002Fdt>\n\n [Installing TensorFlow on Windows  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Finstall\u002Finstall_windows)\u003C\u002Fdt>\n\n [TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002F)\u003C\u002Fdt>\n\n [TensorFlow Linear Model Tutorial  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Ftutorials\u002Fwide)\u003C\u002Fdt>\n\n [TensorFlow Wide & Deep Learning Tutorial  |  TensorFlow](https:\u002F\u002Fwww.tensorflow.org\u002Ftutorials\u002Fwide_and_deep)\u003C\u002Fdt>\n\n [Using TensorFlow in Windows with a GPU | Heaton Research](http:\u002F\u002Fwww.heatonresearch.com\u002F2017\u002F01\u002F01\u002Ftensorflow-windows-gpu.html)\u003C\u002Fdt>\n\n [Installation Guide Windows :: CUDA Toolkit Documentation](http:\u002F\u002Fdocs.nvidia.com\u002Fcuda\u002Fcuda-installation-guide-microsoft-windows\u002F)\n\n [7 Steps to Mastering Machine Learning With Python](https:\u002F\u002Fwww.kdnuggets.com\u002F2015\u002F11\u002Fseven-steps-machine-learning-python.html)\u003C\u002Fdt>\n\n [A visual introduction to machine learning](http:\u002F\u002Fwww.r2d3.us\u002Fvisual-intro-to-machine-learning-part-1\u002F)\u003C\u002Fdt>\n\n [Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur | No Free Hunch](http:\u002F\u002Fblog.kaggle.com\u002F2016\u002F07\u002F21\u002Fapproaching-almost-any-machine-learning-problem-abhishek-thakur\u002F)\u003C\u002Fdt>\n\n [Automated Machine Learning Hyperparameter Tuning in Python](https:\u002F\u002Ftowardsdatascience.com\u002Fautomated-machine-learning-hyperparameter-tuning-in-python-dfda59b72f8a)\u003C\u002Fdt>\n\n [Berkeley AI Materials](http:\u002F\u002Fai.berkeley.edu\u002Flecture_videos.html)\u003C\u002Fdt>\n\n [Deep Learning For Coders fast.ai](http:\u002F\u002Fcourse.fast.ai\u002F)\u003C\u002Fdt>\n\n [Essentials of Machine Learning Algorithms (with Python and R Codes)](https:\u002F\u002Fwww.analyticsvidhya.com\u002Fblog\u002F2017\u002F09\u002Fcommon-machine-learning-algorithms\u002F?utm_medium=social&utm_source=linkedin.com&utm_campaign=buffer)\u003C\u002Fdt>\n\n [GOOGLE - Rules of Machine Learning:  |  Machine Learning Rules  |  Google Developers](https:\u002F\u002Fdevelopers.google.com\u002Fmachine-learning\u002Frules-of-ml\u002F)\u003C\u002Fdt>\n\n [http:\u002F\u002Fwww.r2d3.us\u002Fvisual-intro-to-machine-learning-part-2\u002F](http:\u002F\u002Fwww.r2d3.us\u002Fvisual-intro-to-machine-learning-part-2\u002F)\u003C\u002Fdt>\n\n [ISLR class videos](https:\u002F\u002Fwww.r-bloggers.com\u002Fin-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos\u002F)\u003C\u002Fdt>\n\n [Lecture Collection | Machine Learning - Stanford course](https:\u002F\u002Fwww.youtube.com\u002Fview_play_list?p=A89DCFA6ADACE599)\u003C\u002Fdt>\n\n [Machine Learning Zero-to-Hero: Everything you need in order to compete on Kaggle for the first…](https:\u002F\u002Ftowardsdatascience.com\u002Fmachine-learning-zero-to-hero-everything-you-need-in-order-to-compete-on-kaggle-for-the-first-time-18644e701cf1)\u003C\u002Fdt>\n\n [Microsoft Azure ML Cheat sheet](https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fazure\u002Fmachine-learning\u002Fmachine-learning-algorithm-choice)\u003C\u002Fdt>\n\n [Open Machine Learning Course (beta) • mlcourse.ai](https:\u002F\u002Fmlcourse.ai\u002F)\u003C\u002Fdt>\n\n [Pedro Domigos Machine Learning lectures](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002FUWCSE\u002Fplaylists?shelf_id=16&sort=dd&view=50)\u003C\u002Fdt>\n\n [The Hitchhiker’s Guide to Machine Learning in Python](https:\u002F\u002Fmedium.com\u002F@conordewey3\u002Fthe-hitchhikers-guide-to-machine-learning-algorithms-in-python-bfad66adb378)\u003C\u002Fdt>\n\n [Top 10 Machine Learning Projects on Github](http:\u002F\u002Fwww.kdnuggets.com\u002F2015\u002F12\u002Ftop-10-machine-learning-github.html)\u003C\u002Fdt>\n\n [UCI Machine Learning Repository](http:\u002F\u002Farchive.ics.uci.edu\u002Fml\u002F)\u003C\u002Fdt>\n\n ### Optimization and ML\n\n [Learning to Optimize with Reinforcement Learning – The Berkeley Artificial Intelligence Research Blog](https:\u002F\u002Fbair.berkeley.edu\u002Fblog\u002F2017\u002F09\u002F12\u002Flearning-to-optimize-with-rl\u002F)\u003C\u002Fdt>\n\n### Kaggle\n\n[Hello Kaggle! - A Kaggle Guide for someone who is new at Kaggle](https:\u002F\u002Fgithub.com\u002Fstevekwon211\u002FHello-Kaggle)\u003C\u002Fdt>\n\n## Python\n\n### Tutorials\n\n[Everything About Python — Beginner To Advanced](https:\u002F\u002Fmedium.com\u002Ffintechexplained\u002Feverything-about-python-from-beginner-to-advance-level-227d52ef32d2)\n\n### Jupyter and IDE related\n\n[Interactive spreadsheets in Jupyter](https:\u002F\u002Ftowardsdatascience.com\u002Finteractive-spreadsheets-in-jupyter-32ab6ec0f4ff)\n\n[PyCharm for data scientists](https:\u002F\u002Fwww.kdnuggets.com\u002F2019\u002F05\u002Fpycharm-data-scientists.html)\n\n[Built-in magic commands — IPython 6.2.1 documentation](http:\u002F\u002Fipython.readthedocs.io\u002Fen\u002Fstable\u002Finteractive\u002Fmagics.html)\u003C\u002Fdt>\n\n[Concrete Statistics Jupyter Notebook Peter Norvig](http:\u002F\u002Fnbviewer.jupyter.org\u002Furl\u002Fnorvig.com\u002Fipython\u002FProbability.ipynb)\u003C\u002Fdt>\n\n[Economics simulation Jupyter Notebook Peter Norvig](http:\u002F\u002Fnbviewer.jupyter.org\u002Furl\u002Fnorvig.com\u002Fipython\u002FEconomics.ipynb)\u003C\u002Fdt>\n\n[Markdown Cheatsheet](https:\u002F\u002Fgithub.com\u002Fadam-p\u002Fmarkdown-here\u002Fwiki\u002FMarkdown-Cheatsheet)\u003C\u002Fdt>\n\n[Using Interact — Jupyter Widgets 7.0.3 documentation](http:\u002F\u002Fipywidgets.readthedocs.io\u002Fen\u002Fstable\u002Fexamples\u002FUsing%20Interact.html)\n \n[Pixie - visual Python debugger for Jupyter notebook](https:\u002F\u002Fmedium.com\u002Fibm-watson-data-lab\u002Fthe-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:\u002F\u002Fmatplotlib.org\u002Fexamples\u002Fcolor\u002Fcolormaps_reference.html)\u003C\u002Fdt>\n\n [ggplot | Home](http:\u002F\u002Fggplot.yhathq.com\u002F)\u003C\u002Fdt>\n\n [Matplotlib 1.5.1](http:\u002F\u002Fmatplotlib.org\u002F1.5.1\u002Findex.html)\u003C\u002Fdt>\n\n [Matplotlib Plotting commands summary —](http:\u002F\u002Fmatplotlib.org\u002F1.5.1\u002Fapi\u002Fpyplot_summary.html)\u003C\u002Fdt>\n\n [Matplotlib tutorial](http:\u002F\u002Fwww.labri.fr\u002Fperso\u002Fnrougier\u002Fteaching\u002Fmatplotlib\u002F)\u003C\u002Fdt>\n\n [Seaborn tutorial — seaborn 0.7.1 documentation](http:\u002F\u002Fseaborn.pydata.org\u002Ftutorial.html)\u003C\u002Fdt>\n\n### MOOC courses\n\n[Github\u002Fjmportilla\u002FComplete-Python-Bootcamp: Lectures](https:\u002F\u002Fgithub.com\u002Fjmportilla\u002FComplete-Python-Bootcamp)\u003C\u002Fdt>\n\n [Jupyter Notebook - Udemy Complete Python Bootcamp course](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fjmportilla\u002FComplete-Python-Bootcamp\u002Ftree\u002Fmaster\u002F)\u003C\u002Fdt>\n\n [Python for Data Science and Machine Learning Bootcamp | Udemy](https:\u002F\u002Fwww.udemy.com\u002Fpython-for-data-science-and-machine-learning-bootcamp\u002Flearn\u002Fv4\u002Foverview)\u003C\u002Fdt>\n\n [Computational Science and Engineering I | Mathematics | MIT OpenCourseWare](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fmathematics\u002F18-085-computational-science-and-engineering-i-fall-2008\u002Findex.htm)\u003C\u002Fdt>\n\n [Foundations of Machine Learning (A course by Bloomberg)](https:\u002F\u002Fwww.techleer.com\u002Farticles\u002F536-foundations-of-machine-learning-a-course-by-bloomberg\u002F)\u003C\u002Fdt>\n\n### NumPy and SciPy\n \n [Linear algebra (numpy.linalg) — NumPy v1.12 Manual](https:\u002F\u002Fdocs.scipy.org\u002Fdoc\u002Fnumpy\u002Freference\u002Froutines.linalg.html)\u003C\u002Fdt>\n\n [NumPy v1.12 Universal functions](https:\u002F\u002Fdocs.scipy.org\u002Fdoc\u002Fnumpy\u002Freference\u002Fufuncs.html)\u003C\u002Fdt>\n\n [NumPy v1.13.dev0 Manual](https:\u002F\u002Fdocs.scipy.org\u002Fdoc\u002Fnumpy-dev\u002Fuser\u002Fquickstart.html)\u003C\u002Fdt>\n\n [Random sampling (numpy.random) — NumPy v1.13 Manual](https:\u002F\u002Fdocs.scipy.org\u002Fdoc\u002Fnumpy-1.13.0\u002Freference\u002Froutines.random.html)\u003C\u002Fdt>\n\n [SciPy — SciPy v0.19.0 Reference Guide](https:\u002F\u002Fdocs.scipy.org\u002Fdoc\u002Fscipy\u002Freference\u002F?v=20170402183812)\u003C\u002Fdt>\n\n [From Python to Numpy](https:\u002F\u002Fwww.labri.fr\u002Fperso\u002Fnrougier\u002Ffrom-python-to-numpy\u002F#id7)\u003C\u002Fdt>\n\n [numpy-100\u002F100 Numpy exercises with hint.md at master · rougier\u002Fnumpy-100](https:\u002F\u002Fgithub.com\u002Frougier\u002Fnumpy-100\u002Fblob\u002Fmaster\u002F100%20Numpy%20exercises%20with%20hint.md)\u003C\u002Fdt>\n\n### Pandas\n\n [Pandas 0.20.3 documentation](http:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002F)\u003C\u002Fdt>\n\n [Pandas: Python Data Analysis Library](http:\u002F\u002Fpandas.pydata.org\u002F)\u003C\u002Fdt>\n\n\n### Setup, PyPi, Creating your own packages\n [Home | Read the Docs](https:\u002F\u002Freadthedocs.org\u002F)\u003C\u002Fdt>\n\n [How to publish your own Python Package on PyPi – freeCodeCamp](https:\u002F\u002Fmedium.freecodecamp.org\u002Fhow-to-publish-a-pyton-package-on-pypi-a89e9522ce24)\u003C\u002Fdt>\n \n [Step-by-Step Guide to Creating R and Python Libraries (in JupyterLab)](https:\u002F\u002Ftowardsdatascience.com\u002Fstep-by-step-guide-to-creating-r-and-python-libraries-e81bbea87911)\n\n [How to submit a package to PyPI — Peter Downs](http:\u002F\u002Fpeterdowns.com\u002Fposts\u002Ffirst-time-with-pypi.html)\u003C\u002Fdt>\n\n [Packaging and Distributing Projects — Python Packaging User Guide](https:\u002F\u002Fpackaging.python.org\u002Ftutorials\u002Fdistributing-packages\u002F#setup-args)\u003C\u002Fdt>","该项目是一个精心整理的数据科学资源集合，涵盖了软件、平台、语言、技术等多方面的链接。其核心功能在于为数据科学从业者提供一站式的高质量学习资料和技术文档，包括但不限于数据分析、机器学习、深度学习等领域的内容。项目采用MIT许可证开放源代码，适合任何希望深入学习或提升自己在数据科学领域技能的人士使用，无论是初学者还是有经验的专业人士都能从中受益。",2,"2026-06-11 03:25:02","top_topic"]