diff --git a/Programming-in-Python.md b/Programming-in-Python.md index 239f3ad5..dc92dded 100644 --- a/Programming-in-Python.md +++ b/Programming-in-Python.md @@ -39,6 +39,10 @@ - [Scientific Python](https://github.com/Imperial-College-Data-Science-Society/Lecture-2-Scientific-Python) +## Courses + +See **Python: Best practices** and **Python: Testing** under [Courses](./courses.md#course) + ## Cheatsheets - [Python Cheatsheet](https://www.pythoncheatsheet.org/) - [PySheee: Python Cheatsheet](https://www.pythonsheets.com/) @@ -96,7 +100,6 @@ - [Python String Formatting Best Practices](https://realpython.com/python-string-formatting/) - [The Best of the Best Practices (BOBP) Guide for Python](https://gist.github.com/sloria/7001839) - [Dmitry Mugtasimov's Python software development practices](https://dmugtasimov-tech.blogspot.com/2016/12/my-python-software-development-practices.html) -- [Pluralsight: Python Best Practices for Code Quality](https://www.pluralsight.com/courses/python-best-practices-code-quality) - [SO: Python coding standards/best practices](https://stackoverflow.com/questions/356161/python-coding-standards-best-practices) - [Python Best Practices: 5 Tips For Better Code - Airbrake Blog](https://airbrake.io/blog/python/python-best-practices) - [Python tutorial: Best practices and common mistakes to avoid](https://jaxenter.com/python-tutorial-best-practices-145959.html) @@ -116,7 +119,6 @@ - [Testing Python Applications with Pytest](https://semaphoreci.com/community/tutorials/testing-python-applications-with-pytest) - [An Introduction to Mocking in Python](https://www.toptal.com/python/an-introduction-to-mocking-in-python) - [PyCharm: Testing Your First Python Application](https://www.jetbrains.com/help/pycharm/testing-your-first-python-application.html) -- [Udemy Course: Automated Software Testing with Python](https://www.udemy.com/automated-software-testing-with-python/) - [unittest — Unit testing framework](https://docs.python.org/2/library/unittest.html) ## Refactoring diff --git a/README.md b/README.md index 2703f329..a52383ea 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,7 @@ Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn - [Artificial Intelligence](README-details.md#artificial-intelligence) - [Automation](README-details.md#automation) - [Competitions](competitions.md) + - [Courses](courses.md) - [Ethics / altruistic motives](README-details.md#ethics--altruistic-motives) - [Java](./details/java-jvm.md#java) - [Business / General / Semi-technical](./details/java-jvm.md#business--general--semi-technical) diff --git a/cloud-devops-infra/README.md b/cloud-devops-infra/README.md index 83dfadd6..6a2eff78 100644 --- a/cloud-devops-infra/README.md +++ b/cloud-devops-infra/README.md @@ -67,18 +67,17 @@ - Intel - [Intel® Developer Zone](https://software.intel.com/en-us/home) - [Intel® AI Developer Home Page](https://software.intel.com/en-us/ai) - - [Intel® AI Courses](https://software.intel.com/en-us/ai/courses) - - [Featured Course: AI from the Data Center to the Edge – An Optimized Path using Intel® Architecture](https://software.seek.intel.com/DataCenter_to_Edge_REG) - [Intel® AI Developer Webinar Series](https://software.seek.intel.com/AIWebinarSeries?registration_source=IDZ) | [All webinars listing](https://intelvs.on24.com/vshow/IntelWebinarEvents/#content/2033414) - The PlaidML Tensor Compiler - [webinar](https://event.on24.com/eventRegistration/console/EventConsoleApollo.jsp?&eventid=2026509&sessionid=1&username=&partnerref=&format=fhaudio&mobile=false&flashsupportedmobiledevice=false&helpcenter=false&key=B27628973F7FA8B9758983E373E36ED1&text_language_id=en&playerwidth=1000&playerheight=700&overwritelobby=y&eventuserid=246511746&contenttype=A&mediametricsessionid=207230377&mediametricid=2857349&usercd=246511746&mode=launch) - nGraph - Unlocking next-generation performance with deep learning compilers: [webinar](https://intelvs.on24.com/vshow/IntelWebinarEvents/#content/2033414) | [slides](https://event.on24.com/event/20/33/41/2/rt/1/documents/resourceList1565185524584/s_ngraphwebinar1565185512750.pdf) | [homepage](https://www.ngraph.ai/) | [github](https://github.com/NervanaSystems/ngraph) + - Also see [Intel](../courses.md#intel) in [Courses](../courses.md#courses) _Thanks to the great minds on the [mechanical sympathy](https://groups.google.com/forum/#!forum/mechanical-sympathy) mailing list for their responses to my queries on CPU probing._ ## FPGA - - [Intel AI Developer Program - Deep Learning Inference With Intel® FPGAs](https://software.intel.com/en-us/ai/courses/deep-learning-inference-fpga) - [Using FPGAs for Datacenter Acceleration](https://event.on24.com/eventRegistration/EventLobbyServlet?target=lobby20.jsp&eventid=2033432&sessionid=1&eventuserid=246511756&key=8678836B54A84876D7338D7BF7F87B88) | [Windows AI](https://docs.microsoft.com/en-us/windows/ai/) | [Intel® Distribution of OpenVINO™ Toolkit: Develop Multiplatform Computer Vision Solutions](https://software.intel.com/en-us/openvino-toolkit) + - Also see [FPGA](../courses.md#fpga) in [Courses](../courses.md#courses) ## GPU diff --git a/courses.md b/courses.md new file mode 100644 index 00000000..ce79054a --- /dev/null +++ b/courses.md @@ -0,0 +1,120 @@ +# Courses + +## Algorithms + +- [Algorithms at Coursera by Wayne and Sedgewick](https://www.coursera.org/course/algs4partI) + +## Datacamp + +- [Recommended Courses by Datacamp](https://www.datacamp.com/courses/) + +## Dataiku + +- [Dataiku Teachable](http://dataiku.teachable.com/courses) + +## Data Science + +- [Data Science Primer](https://elitedatascience.com/primer) +- [Coursera course: Getting and Cleaning Data](https://www.coursera.org/learn/data-cleaning?recoOrder=20&utm_medium=email&utm_source=recommendations&utm_campaign=u0faoCsqEemEkbug8nMVQQ) +- [Data Science courses on Coursera](https://www.coursera.org/learn/competitive-data-science) +- [Data courses on Udemy](https://www.udemy.com/courses/search/?ref=home&src=ukw&q=data) +- [Data courses on Udacity](https://eu.udacity.com/courses/school-of-data-science) +- [Latest Machine learning, visualization, data mining techniques. Online Master’s in Data Analytic from Penn State](https://twitter.com/analyticbridge/status/1102667686302179336) +- [Coursera Course: Probability and distribution](https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8) [deadlink] +- [Coursera Data Science Methodology course](https://www.coursera.org/learn/data-science-methodology?aid=true) + - From Problem to Approach and From Requirements to Collection + - Business Understanding + - Analytic Approach + - Data Requirements + - Data Collection + - From Understanding to Preparation and From Modeling to Evaluation + - Data Understanding + - Data Preparation + - Modeling + - Model Evaluation + +## Computer Vision + +- [Introduction to Computer Vision, Udacity, GeorgiaTech](https://www.udacity.com/course/introduction-to-computer-vision--ud810) (free, paid for certification) + - [Stanford Computer Vision Lab : Teaching](http://vision.stanford.edu/teaching.html) - Contains publications other than courses (free) + - [Introduction to CV, IBM](https://www.coursera.org/learn/introduction-computer-vision-watson-opencv) (free, paid for certification) + - [Convolutional Neural Networks, Coursera](https://www.coursera.org/learn/convolutional-neural-networks) (free, paid for certification) + +### Image Processing + +- [Image and Video Processing course by Duke University, Coursera](https://www.coursera.org/learn/image-processing) (free, paid for certification) + +## Fast.ai + +- [Practical Deep Learning for Coders, v3](https://course.fast.ai/) +- [Part 2: Deep Learning from the Foundations](https://course.fast.ai/part2) +- [Introduction to Machine Learning for Coders](http://course18.fast.ai/ml) +- [Computational Linear Algebra](https://github.com/fastai/numerical-linear-algebra/blob/master/README.md) +- [Code-First Introduction to Natural Language Processing](https://www.fast.ai/2019/07/08/fastai-nlp/) + +## Intel + +- [Intel® AI Courses](https://software.intel.com/en-us/ai/courses) +- [Featured Course: AI from the Data Center to the Edge – An Optimized Path using Intel® Architecture](https://software.seek.intel.com/DataCenter_to_Edge_REG) + +### FPGA + +- [Intel AI Developer Program - Deep Learning Inference With Intel® FPGAs](https://software.intel.com/en-us/ai/courses/deep-learning-inference-fpga) + +## Machine Learning + +- ML course by [Weights & Biases | WandB](https://wandb.com) + - [Code from the class](https://github.com/lukas/ml-class) + - [Setup Instructions](https://github.com/lukas/ml-class) + - [Slides](https://storage.googleapis.com/wandb/Bloomberg%20Class%201.pdf) + - [Building and Debugging CNNs](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE9193U0/bloomberg_class_2.pdf) + - [Introduction to ML](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE3Q7NN7/bloomberg_class_3.pdf) +- [Course material by Students of AI (Imperial College, London)](https://github.com/Students-for-AI/The-Academy-of-AI) +- [Comprehensive list of machine learning videos by Yaz](https://github.com/yazdotai/machine-learning-video-courses) + +### Java/JVM +- [ML for Java Developers Course](http://numahub.com/courses/machine-learning-java-developers) + +### Deep Learning + +- [Code examples for the Stanford's course: TensorFlow for Deep Learning Research](https://github.com/chiphuyen/stanford-tensorflow-tutorials) + +#### Reinforcement Learning + +- Reinforcement Learning Crash Course by Central London Data Science meetup - [GitHub repo](https://github.com/central-ldn-data-sci/CrashCourseRL) | [Slides](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/Crash%20Course%20in%20Reinforcement%20Learning.pdf) | Notebooks: [1](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/CrashCourseRL.ipynb) | [2](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/crash_course_reinforcement_learning.ipynb) | [3](https://www.kaggle.com/blairyoung/crash-course-in-reinforcement-learning) + +## Natural Language Processing (NLP) + +- [How to Get Started with Deep Learning for Natural Language Processing (7-Day Mini-Course)](https://machinelearningmastery.com/crash-course-deep-learning-natural-language-processing/) + +## Python: Best practices + +- [Pluralsight: Python Best Practices for Code Quality](https://www.pluralsight.com/courses/python-best-practices-code-quality) + +## Python: Testing + +- [Udemy Course: Automated Software Testing with Python](https://www.udemy.com/automated-software-testing-with-python/) + +## Statistics + +- Statistics courses at [Coursera](https://www.coursera.org/courses?query=statistics&) +- [Udemy](https://www.udemy.com/courses/search/?src=ukw&q=statistics) +- [Udacity](https://eu.udacity.com/courses/all) - search for `Statistics` +- Harvard University: [Statistics 110](https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo) | [more videos on their YouTube channel](https://www.youtube.com/user/Harvard/search?query=statistics) +- [Stanford University](https://online.stanford.edu/courses?keywords=statistics) +- [Statistical Inference [course]](https://www.coursera.org/learn/statistical-inference) + +## Misc + +- [Check out 50 most popular massive open online courses](https://www.onlinecoursereport.com/the-50-most-popular-moocs-of-all-time/) ([Tweet](https://twitter.com/java/status/984844161969983489)) + + +# Contributing + +Contributions are very welcome, please share back with the wider community (and get credited for it)! + +Please have a look at the [CONTRIBUTING](CONTRIBUTING.md) guidelines, also have a read about our [licensing](LICENSE.md) policy. + +--- + +Back to [main page (table of contents)](README.md) \ No newline at end of file diff --git a/data/about-Dataiku.md b/data/about-Dataiku.md index 999720d3..9cf6f045 100644 --- a/data/about-Dataiku.md +++ b/data/about-Dataiku.md @@ -15,7 +15,7 @@ - Example: [how Dataiku DSS can be run on GraalVM for performance benefits](../examples/data/dataiku#dataiku-data-science-studio-dss) | [folder](../examples/data/dataiku) - Additional useful resources for learning and exploration - [User Enablement resources](https://pages.dataiku.com/dataiku-dss-user-enablement) - - [Dataiku Teachable](http://dataiku.teachable.com/courses) + - See [Dataiku](../courses.md#dataiku) under [Courses](../courses.md#course) - [Dataiku YouTube Channel](https://www.youtube.com/channel/UCSMqVwPTmerMiCaL_zKRjBw) - [Dataiku Academy](https://academy.dataiku.com/5.1/) - [Dataiku Learn](https://www.dataiku.com/learn/) | [Tutorials](https://www.dataiku.com/learn/portals/tutorials.html) | [Dataiku ML: Your First Deep Learning Model](https://academy.dataiku.com/latest/tutorial/machine-learning/deep-learning-first.html) | [Dataiku: Machine Learning](https://academy.dataiku.com/latest/tutorial/machine-learning/skills.html) diff --git a/data/about-fast.ai.md b/data/about-fast.ai.md index 2368e8e4..996ae6a3 100644 --- a/data/about-fast.ai.md +++ b/data/about-fast.ai.md @@ -6,8 +6,7 @@ - Has a community forum and lots of resources on the internet, good feedback and posts on medium Additional references - -- https://course.fast.ai/ +- See [fast.ai](../courses.md#fastai) under [Courses](../courses.md#course) - https://docs.fast.ai/training.html - https://forums.fast.ai/t/how-should-i-get-started-with-fast-ai-library/17627 - https://forums.fast.ai/t/another-treat-early-access-to-intro-to-machine-learning-videos/6826 diff --git a/data/courses-books.md b/data/courses-books.md index 9b0df734..43d4d6fb 100644 --- a/data/courses-books.md +++ b/data/courses-books.md @@ -1,15 +1,13 @@ # Courses / books -- [Data Science Primer](https://elitedatascience.com/primer) +## Courses + +- See [Courses](../courses.md#courses) + +## Books + - [27 Amazing Data Science Books Every Data Scientist Should Read](https://www.analyticsvidhya.com/blog/2019/01/27-amazing-data-science-books-every-data-scientist-should-read/) -- [Coursera course: Getting and Cleaning Data](https://www.coursera.org/learn/data-cleaning?recoOrder=20&utm_medium=email&utm_source=recommendations&utm_campaign=u0faoCsqEemEkbug8nMVQQ) -- [Data Science courses on Coursera](https://www.coursera.org/learn/competitive-data-science) -- [Data courses on Udemy](https://www.udemy.com/courses/search/?ref=home&src=ukw&q=data) -- [Data courses on Udacity](https://eu.udacity.com/courses/school-of-data-science) -- [Latest Machine learning, visualization, data mining techniques. Online Master’s in Data Analytic from Penn State](https://twitter.com/analyticbridge/status/1102667686302179336) - [Data Science Handbook](https://github.com/RishiSankineni/Data-Science-Swag/blob/master/The%20Data%20Science%20Handbook.pdf) -- [Coursera Course: Probability and distribution](https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1571785200&v=beta&t=XyEEqUgi3y4L1hiZ7CxlxbAXyZmM_zcCCdn-Lr04ns8) [deadlink] - # Contributing diff --git a/data/datasets.md b/data/datasets.md index 60c623f8..57034ddb 100644 --- a/data/datasets.md +++ b/data/datasets.md @@ -26,6 +26,9 @@ - [UC Irvine Machine Learning Repository](https://archive.ics.uci.edu/ml/index.php) - [Google Research: A large-scale dataset of manually annotated audio events](https://research.google.com/audioset/index.html) +## Courses + +See [Courses](../courses.md#courses) # Contributing diff --git a/data/frameworks-checklists.md b/data/frameworks-checklists.md index dd5f1095..ce9fd713 100644 --- a/data/frameworks-checklists.md +++ b/data/frameworks-checklists.md @@ -6,19 +6,7 @@ - [The KDD process for extracting useful knowledge from volumes of data](http://shawndra.pbworks.com/f/The%20KDD%20process%20for%20extracting%20useful%20knowledge%20from%20volumes%20of%20data.pdf) - [Data Mining: Practical ML Tools and Techniques by Witten, Frank and Mark 3rd edition](https://www.wi.hs-wismar.de/~cleve/vorl/projects/dm/ss13/HierarClustern/Literatur/WittenFrank-DM-3rd.pdf) - [Foundational Methodology for Data Science - IBM Analytics Whitepaper](https://tdwi.org/~/media/64511A895D86457E964174EDC5C4C7B1.PDF) -- [Coursera Data Science Methodology course](https://www.coursera.org/learn/data-science-methodology?aid=true) - - From Problem to Approach and From Requirements to Collection - - Business Understanding - - Analytic Approach - - Data Requirements - - Data Collection - - From Understanding to Preparation and From Modeling to Evaluation - - Data Understanding - - Data Preparation - - Modeling - - Model Evaluation - - +- See [Courses](../courses.md#courses) # Contributing diff --git a/data/statistics.md b/data/statistics.md index 063a512b..5960c741 100644 --- a/data/statistics.md +++ b/data/statistics.md @@ -5,7 +5,7 @@ - [Understanding statistical inference [video]](https://www.youtube.com/watch?v=tFRXsngz4UQ) - [Four ideas of Statistical Inference](http://www.bristol.ac.uk/medical-school/media/rms/red/4_ideas_of_statistical_inference.html) - [An Introduction to Statistical Learning [book]](http://www-bcf.usc.edu/~gareth/ISL/) - - [Statistical Inference [course]](https://www.coursera.org/learn/statistical-inference) + - See [Statistics courses](../courses.md#statistics) under [Courses](../courses.md#courses) - [Understand Your Machine Learning Data With Descriptive Statistics in Python](https://machinelearningmastery.com/understand-machine-learning-data-descriptive-statistics-python/) - [How to Use Statistics to Identify Outliers in Data](https://machinelearningmastery.com/how-to-use-statistics-to-identify-outliers-in-data/) - [Applying Physics functions](../presentations/data/Trackener-physics-functions-usage-example.pptx) @@ -13,7 +13,7 @@ - [Naked statistics](http://www.nakedstatistics.com/) | [Book on Amazon](https://www.amazon.com/Naked-Statistics-Stripping-Dread-Data/dp/1480590185) [Naked statistics flash cards](https://quizlet.com/90835490/naked-statistics-flash-cards/) | [Summary by Daniel Miessler](https://danielmiessler.com/projects/reading/summary-naked-statistics/) - [Cartoon Guide to Statistics (Cartoon Guide Series)](https://www.amazon.co.uk/Cartoon-Guide-Statistics/dp/0062731025/ref=sr_1_1?hvadid=80814136501810&hvbmt=bb&hvdev=c&hvqmt=b&keywords=cartoon+guide+statistics&qid=1556047351&s=gateway&sr=8-1) - [Journal of Statistical Software - TidyData](https://www.jstatsoft.org/article/view/v059i10/) -- Statistics courses at [Coursera](https://www.coursera.org/courses?query=statistics&) | [Udemy](https://www.udemy.com/courses/search/?src=ukw&q=statistics) | [Udacity](https://eu.udacity.com/courses/all) - search for `Statistics` | Harvard University: [Statistics 110](https://www.youtube.com/watch?v=KbB0FjPg0mw&list=PL2SOU6wwxB0uwwH80KTQ6ht66KWxbzTIo) | [more videos on their YouTube channel](https://www.youtube.com/user/Harvard/search?query=statistics) | [Stanford University](https://online.stanford.edu/courses?keywords=statistics) +- See [Statistics courses](../courses.md#statistics) under [Courses](../courses.md#courses) - [15 Statistical Hypothesis Tests in Python (Cheat Sheet)](https://machinelearningmastery.com/statistical-hypothesis-tests-in-python-cheat-sheet/?fbclid=IwAR102PXBzIdx8g8zejg9ssE7at8jrnyfAtiT95Rp8flo98p8qEFBho5HOG0) - [Statistics cheatsheet by Nabih Ibrahim Bawazir](https://media.licdn.com/dms/document/C511FAQF31AWGmSTzMQ/feedshare-document-pdf-analyzed/0?e=1573030800&v=beta&t=11ugKu44wK--uA9WG98V_r6_LY_xu6I8Y-YSaM1BOsQ) - [Statistics by Chris Albon](https://chrisalbon.com/#statistics) - covering Frequentist topics diff --git a/details/articles-papers-code-data-courses.md b/details/articles-papers-code-data-courses.md index deb51f3e..b684c846 100644 --- a/details/articles-papers-code-data-courses.md +++ b/details/articles-papers-code-data-courses.md @@ -5,17 +5,11 @@ - [Papers and code](https://paperswithcode.com) - [Awesome DL papers](https://github.com/terryum/awesome-deep-learning-papers) - [List of articles related to deep learning applied to music](https://github.com/ybayle/awesome-deep-learning-music) - - [Course material by Students of AI (Imperial College, London)](https://github.com/Students-for-AI/The-Academy-of-AI) - [Data.world's open data - catalog your data, wake up your hidden data workforce, and build a data-driven culture—faster](https://data.world/) - [Browse state-of-the-art](https://paperswithcode.com/sota) - [ML/DL/Data Science resources (scattered across the page)](https://github.com/ayonroy2000/100DaysOfML_TelegramGroup/blob/master/Resources.md) - [Papers by Google X](../papers/google-x/README.md#papers-by-members-of-google-and-google-x-aka-x-team) - - ML course by [Weights & Biases | WandB](https://wandb.com) - - [Code from the class](https://github.com/lukas/ml-class) - - [Setup Instructions](https://github.com/lukas/ml-class) - - [Slides](https://storage.googleapis.com/wandb/Bloomberg%20Class%201.pdf) - - [Building and Debugging CNNs](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE9193U0/bloomberg_class_2.pdf) - - [Introduction to ML](https://wb-ml.slack.com/files/UN2SL6G7Q/FNE3Q7NN7/bloomberg_class_3.pdf) + - See [courses](../courses.md) # Contributing diff --git a/details/java-jvm.md b/details/java-jvm.md index 6a6ed9ac..deba90cf 100644 --- a/details/java-jvm.md +++ b/details/java-jvm.md @@ -109,7 +109,6 @@ Dataiku DSS: [![Dataiku DSS](https://img.shields.io/docker/pulls/neomatrix369/da ## Machine Learning - [ML for Java Developers](https://www.itworld.com/article/3224505/application-development/machine-learning-for-java-developers.html) -- [ML for Java Developers Course](http://numahub.com/courses/machine-learning-java-developers) - [Java ML Dev Videos on YouTube: Naive Bayes w/ JAVA - Tutorial 01](https://www.youtube.com/watch?v=mrP4CyW4tKA&frags=pl%2Cwn) - Java ML Framework: [1](https://github.com/datumbox/datumbox-framework) [2](http://blog.datumbox.com/developing-a-naive-bayes-text-classifier-in-java/) - [Weka 3: Machine Learning Software in Java](https://www.cs.waikato.ac.nz/ml/weka) @@ -117,6 +116,7 @@ Dataiku DSS: [![Dataiku DSS](https://img.shields.io/docker/pulls/neomatrix369/da - [A visual introduction to machine learning](http://www.r2d3.us/visual-intro-to-machine-learning-part-1/) - Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED) - [Overview of AI Libraries in Java](https://www.baeldung.com/java-ai) +- See [Java/JVM](../courses.md#javajvm) in [Courses](../courses.md) - See [ML on Code/Programm/Source Code](../ML-on-code-programming-source-code.md) - See [Cloud/DevOps/Infra > Performance](../cloud-devops-infra/README.md#performance) - to find various ML performance benchmarking suites - Also see [Post model-creation analysis, ML interpretation/explainability](../data/README.md#post-model-creation-analysis-ml-interpretationexplainability) @@ -145,7 +145,6 @@ Dataiku DSS: [![Dataiku DSS](https://img.shields.io/docker/pulls/neomatrix369/da - [Best AI tools and libraries](https://skymind.ai/wiki/automl-automated-machine-learning-ai) ([Tweet](https://twitter.com/java/status/1069459966740836352)) - [Overview of AI Libraries in Java](https://www.baeldung.com/java-ai) ([Tweet](https://twitter.com/java/status/931070584896741377)) - [Free AI Learning Resources For Beginners](https://www.analyticsindiamag.com/here-are-free-ai-learning-resources-for-beginners/) ([Twitter](https://twitter.com/java/status/1013776554868891648)) - - [Check out 50 most popular massive open online courses](https://www.onlinecoursereport.com/the-50-most-popular-moocs-of-all-time/) ([Tweet](https://twitter.com/java/status/984844161969983489)) - [Learn about Marvin AI a set of tools, libraries, an embedded server that exposes microservices](https://www.youtube.com/watch?v=M5_yQCRIftw&feature=youtu.be&t=2m31s) ([Tweet](https://twitter.com/java/status/1040980810231250944)) - [Apache Zeppelin: stairway to notes* haven!](https://medium.com/@neomatrix369/apache-zeppelin-stairway-to-notes-haven-28ec413a185a) - [Teaching Java with Jupyter notebooks](https://blog.frankel.ch/teaching-java-jupyter-notebooks/) diff --git a/details/julia-python-and-r.md b/details/julia-python-and-r.md index 20d492cc..2d09251b 100644 --- a/details/julia-python-and-r.md +++ b/details/julia-python-and-r.md @@ -68,18 +68,16 @@ - [Digital Image Processing Basics](https://www.geeksforgeeks.org/digital-image-processing-basics/) - [Theory for Image Processing](http://web.ipac.caltech.edu/staff/fmasci/home/astro_refs/Digital_Image_Processing_2ndEd.pdf) - - [Image and Video Processing course by Duke University, Coursera](https://www.coursera.org/learn/image-processing) (free, paid for certification) + - See [Image Processing](../courses.md#image-processing) in [Courses](../courses.md) ### OpenCV and tutorials - [Documentation and examples for each topic](https://opencv-python-tutroals.readthedocs.io/en/latest/py_tutorials/py_imgproc/py_table_of_contents_imgproc/py_table_of_contents_imgproc.html) ### Courses - - - [Introduction to Computer Vision, Udacity, GeorgiaTech](https://www.udacity.com/course/introduction-to-computer-vision--ud810) (free, paid for certification) - - [Stanford Computer Vision Lab : Teaching](http://vision.stanford.edu/teaching.html) - Contains publications other than courses (free) - - [Introduction to CV, IBM](https://www.coursera.org/learn/introduction-computer-vision-watson-opencv) (free, paid for certification) - - [Convolutional Neural Networks, Coursera](https://www.coursera.org/learn/convolutional-neural-networks) (free, paid for certification) + + See [Computer Vision](../courses.md#computer-vision) + ### Conferences to follow @@ -98,10 +96,10 @@ - [Data science intro for math/phys background by Poitr Migdal](https://p.migdal.pl/2016/03/15/data-science-intro-for-math-phys-background.html) - [What I do or: science to data science by Poitr Migdal](https://p.migdal.pl/2015/12/14/sci-to-data-sci.html) - - [Algorithms at Coursera by Wayne and Sedgewick](https://www.coursera.org/course/algs4partI) - [T. Cormen, C. Leiserson, R. Rivest and C. Stein, Introduction to Algorithms](https://en.wikipedia.org/wiki/Introduction_to_Algorithms) - [David MacKay, Information Theory, Inference, and Learning Algorithms](http://www.inference.phy.cam.ac.uk/itila/book.html) - [Virgilio - Your new Mentor for Data Science E-Learning](https://github.com/clone95/Virgilio) + - See [Algorithms](../courses.md#algorithms) under [Courses](../courses.md#courses) ## Machine Learning @@ -114,7 +112,6 @@ - [Dive into Machine Learning with Python Jupyter notebook and scikit-learn!](https://github.com/hangtwenty/dive-into-machine-learning) - [Machine learning and deep learning tutorials, articles and other resources](https://github.com/ujjwalkarn/Machine-Learning-Tutorials) - [Machine Learning From Scratch | NumPy | Aims to cover everything from data mining to deep learning. ](https://github.com/eriklindernoren/ML-From-Scratch) - - [Comprehensive list of machine learning videos by Yaz](https://github.com/yazdotai/machine-learning-video-courses) - [Machine Learning for Forecasting Chaos by Dr. Edward Ott](https://www.youtube.com/watch?v=ZFYdZWadyD4&feature=youtu.be) - [Curriculum – Machine Learning Sabbatical](https://coxy1989.com/curriculum.html) - [Curriculum & Log - ML](https://www.alanmartyn.com/) @@ -138,6 +135,7 @@ - [ML Blogs by faculty.ai](https://faculty.ai/blog/) - [ML topics expanded by Chris Albon](https://chrisalbon.com/#machine_learning) - topics covered: Vectors, Matrices, And Arrays • ML Basics • Preprocessing Structured Data • Preprocessing Images • Preprocessing Text • Preprocessing Dates And Times • Feature Engineering • Feature Selection • Model Evaluation • Model Selection • Linear Regression • Logistic Regression • Trees And Forests • Nearest Neighbors • Support Vector Machines • Naive Bayes • Clustering - [Claoudml](https://www.claoudml.com/) - Free Data Science & Machine Learning Resources + - See [Machine Learning](../courses.md#machine-learning) in [Courses](../courses.md#courses) - See [ML on Code/Programm/Source Code](../ML-on-code-programming-source-code.md) - See [Cloud/DevOps/Infra > Performance](../cloud-devops-infra/README.md#performance) - to find various ML performance benchmarking suites - Also see [Post model-creation analysis, ML interpretation/explainability](../data/README.md#post-model-creation-analysis-ml-interpretationexplainability) @@ -148,7 +146,6 @@ - [A curated list of awesome Deep Learning tutorials, projects and communities](https://github.com/josephmisiti/awesome-deep-learning) - [Deep learning library featuring a higher-level API for TensorFlow](https://github.com/tflearn/tflearn) - [A series of Docker images (and their generator) that allows you to quickly set up your deep learning research environment](https://github.com/ufoym/deepo) - - [Code examples for the Stanford's course: TensorFlow for Deep Learning Research](https://github.com/chiphuyen/stanford-tensorflow-tutorials) - [TensorLayer - DL and RL library for Data Scientists](https://github.com/tensorlayer/tensorlayer) | [Docs](https://tensorlayer.readthedocs.io/en/stable/) - [H2O's Deep Learning tutorial](https://github.com/h2oai/h2o-tutorials/blob/master/tutorials/deeplearning/deeplearning.py) - [Interactive Machine Learning, Deep Learning and Statistics websites](https://p.migdal.pl/interactive-machine-learning-list/) @@ -157,7 +154,8 @@ - [Starting deep learning hands-on: image classification on CIFAR-10](https://blog.deepsense.ai/deep-learning-hands-on-image-classification/) - [Checkout 'Deep Learning with Tensorflow 2.0 (MNIST)' Notebooks](../notebooks/README.md#notebooks) - [Deep learning for 3D printing manufacturing](https://www.youtube.com/watch?v=jAQSM2dhDV4) by [Benjamin Schrauwen](https://www.linkedin.com/in/benjaminschrauwen) - - [DL topics expanded by Chris Albon](https://chrisalbon.com/#deep_learning) - topics covered: Keras + - [DL topics expanded by Chris Albon](https://chrisalbon.com/#deep_learning) - topics covered: Keras + - See [Deep Learning](../courses.md#deep-learning) in [Courses](../courses.md#courses) #### Reinforcement Learning @@ -176,11 +174,11 @@ - [AlphaGo Zero Cheatsheet: AlphaGo Zero Explained In One Diagram](https://medium.com/applied-data-science/alphago-zero-explained-in-one-diagram-365f5abf67e0) | [AlphaGo Zero Cheatsheet](https://adspassets.blob.core.windows.net/website/content/alpha_go_zero_cheat_sheet.png) - [How to build your own AlphaZero AI using Python and Keras](https://medium.com/applied-data-science/how-to-build-your-own-alphazero-ai-using-python-and-keras-7f664945c188) - [Reinforcement Learning with DNNs](https://biostat.wisc.edu/~craven/cs760/lectures/AlphaZero.pdf) - - Reinforcement Learning Crash Course by Central London Data Science meetup - [GitHub repo](https://github.com/central-ldn-data-sci/CrashCourseRL) | [Slides](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/Crash%20Course%20in%20Reinforcement%20Learning.pdf) | Notebooks: [1](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/CrashCourseRL.ipynb) | [2](https://github.com/central-ldn-data-sci/CrashCourseRL/blob/master/crash_course_reinforcement_learning.ipynb) | [3](https://www.kaggle.com/blairyoung/crash-course-in-reinforcement-learning) - [TensorLayer - DL and RL library for Data Scientists](https://github.com/tensorlayer/tensorlayer) | [Docs](https://tensorlayer.readthedocs.io/en/stable/) - [Reinforcement Learning using PyTorch by Kai Arulkumaran](https://www.dropbox.com/sh/q0v0k3ida37thyn/AAB6wXMge7C6fvqKIZmGFXVQa?dl=0&preview=2019_06_26_Kai_Arulkumaran.pdf) - Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) second edition Edition: [PDF book](http://incompleteideas.net/book/RLbook2018.pdf) by [Richard S. Sutton](http://incompleteideas.net/index.html) and [Andrew G. Barto](http://www-anw.cs.umass.edu/%7Ebarto/) | [Website and RL resources](http://incompleteideas.net/book/the-book-2nd.html) - [Teaching Artificial Agents to Understand Language by Modelling Reward](https://www.researchgate.net/publication/328437364_Teaching_Artificial_Agents_to_Understand_Language_by_Modelling_Reward) by [Edward Grefenstette](https://egrefen.github.io/) | [Video](https://www.youtube.com/watch?v=JCIIeDL9840) + - [Reinforcement Learning](../courses.md#reinforcement-learning) in [Courses](../courses.md#courses) ## Programming in Python diff --git a/details/maths-stats-probability.md b/details/maths-stats-probability.md index 415cc994..43dd412b 100644 --- a/details/maths-stats-probability.md +++ b/details/maths-stats-probability.md @@ -50,8 +50,7 @@ - [Probability Learning II: How Bayes’ Theorem is applied in Machine Learning](https://towardsdatascience.com/probability-learning-ii-how-bayes-theorem-is-applied-in-machine-learning-bd747a960962) - [Learning & Reasoning in Artificial Intelligence](https://www.youtube.com/watch?v=K_GOHepjY2o) by [Thomas Lukasiewicz](http://www.cs.ox.ac.uk/thomas.lukasiewicz/) - Abductive Learning: Towards Bridging Machine Learning and Logical Reasoning: [Slides](http://daiwz.net/org/slides/ABL-meetup.html) | [Video](https://www.youtube.com/watch?v=ETHrFxiFIUM) | [GitHub](https://github.com/AbductiveLearning/ABL-HED) -- [Coursera Course: Probability and distribution by Nabih Bawazir](https://media.licdn.com/dms/document/C511FAQGFKgIKuW_EEA/feedshare-document-pdf-analyzed/0?e=1573030800&v=beta&t=A635jsWDOhmhQgv5SqvIEKPgLTwEtjFt_-3EGOHFysc) [deadlink] - +- [Data Science](../courses.md#data-science) in [Courses](../courses.md#courses) # Contributing diff --git a/natural-language-processing/course-tutorial-learning-resources.md b/natural-language-processing/course-tutorial-learning-resources.md index 88f6a3f3..f06a0260 100644 --- a/natural-language-processing/course-tutorial-learning-resources.md +++ b/natural-language-processing/course-tutorial-learning-resources.md @@ -3,11 +3,10 @@ - [Introductory: NLP for hackers](https://nlpforhackers.io/deep-learning-introduction/) - [Intermediate (by Jason Brownlee): Applications of Deep Learning for NLP](https://machinelearningmastery.com/applications-of-deep-learning-for-natural-language-processing/) - [Learn Natural Language Processing: From Beginner to Expert](https://www.commonlounge.com/discussion/c1f472553ece4d68bad9bd423fb775cf) -- [How to Get Started with Deep Learning for Natural Language Processing (7-Day Mini-Course)](https://machinelearningmastery.com/crash-course-deep-learning-natural-language-processing/) - [Understanding Convolutional Neural Networks for NLP](http://www.wildml.com/2015/11/understanding-convolutional-neural-networks-for-nlp/) - [How to solve 90% of NLP problems: a step-by-step guide](https://blog.insightdatascience.com/how-to-solve-90-of-nlp-problems-a-step-by-step-guide-fda605278e4e) - [A framework for dialog research + datasets](https://parl.ai/) - +- See [Natural Language Processing (NLP)](../courses.md#naturallanguageprocessing-nlp) in [Courses](../courses.md#courses) # Contributing diff --git a/things-to-know.md b/things-to-know.md index 82475a59..ec181151 100644 --- a/things-to-know.md +++ b/things-to-know.md @@ -8,6 +8,7 @@ As a (to-be) or (current) data scientist, data engineer, data analyst, machine l - [Media sources](#media-sources) - [Useful blogs to read](#useful-blogs-to-read) - [Course providers](#course-providers) +- [Courses](#courses) - [Primary tools to analyse data](#primary-tools-to-analyse-data) - [IDEs](#ides) - [Hosted Notebook products](#hosted-notebook-products) @@ -76,6 +77,10 @@ As a (to-be) or (current) data scientist, data engineer, data analyst, machine l - University Courses - [Kaggle Courses (Kaggle Learn)](https://www.kaggle.com/learn/overview) +## Courses + +See [Courses](./courses.md#courses) + ## Primary tools to analyse data - Basic statistical software (Microsoft Excel, Google Sheets, etc.)