Data-Science-and-Machine-Learning-Resources

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Data-Science-and-Machine-Learning-Resources

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A list containing resources for Data Science and Machine Learning with Python.

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# Data Science and Machine Learning Resources (Python)
## Important
* [Machine Learning Glossary](https://ml-cheatsheet.readthedocs.io/en/latest/)
* [Machine Learning Glossary by Google](https://developers.google.com/machine-learning/glossary)
* [BOOK Mathematics for Machine Learning](https://mml-book.github.io/)
* [Sklearn User Guide](https://scikit-learn.org/stable/user_guide.html)
* [Linear Algebra Visually Explained](http://immersivemath.com/ila/tableofcontents.html?)
* [Linear Algebra Notes from the Deep Learning book from Ian Goodfellow](https://github.com/hadrienj/deepLearningBook-Notes)
* [Linear Algebra Khan Academy](https://www.khanacademy.org/math/linear-algebra)
* [Kaggle Courses](https://www.kaggle.com/learn/overview)
* [Visualizing Machine Learning](http://jalammar.github.io/)

### Youtube Channels
* [3Blue1Brown](https://www.youtube.com/channel/UCYO_jab_esuFRV4b17AJtAw/featured)
* [Siraj Raval](https://www.youtube.com/channel/UCWN3xxRkmTPmbKwht9FuE5A/playlists)

## Other
* [The best Data Science courses on the internet, ranked by your reviews](https://medium.freecodecamp.org/the-best-data-science-courses-on-the-internet-ranked-by-your-reviews-6dc5b910ea40)
* https://ai.google/
* [Guide to Reading Academic Research Papers](https://towardsdatascience.com/guide-to-reading-academic-research-papers-c69c21619de6)
* [Must read papers on machine learning for a beginner](https://www.quora.com/What-are-some-must-read-papers-on-machine-learning-for-a-beginner)
* [Heroes of Deep Learning: Top Takeaways for Aspiring Data Scientists from Andrew Ng’s Interview Series](https://www.analyticsvidhya.com/blog/2018/09/heroes-deep-learning-top-takeaways-andrew-ng-interview-series/)
## Python
* https://realpython.com/ - tutorials and blog posts
## Kaggle
* [Kaggle Blog](http://blog.kaggle.com/)
* [Public dataset about Kaggle, Meta Kaggle](https://www.kaggle.com/kaggle/meta-kaggle/kernels)
* [Kaggle Learn Platform](https://www.kaggle.com/learn/overview)
## Books
* [Python 101 book](http://python101.pythonlibrary.org/)
## Blogs
* [Practical Business Python](http://pbpython.com/)
* [A list of 200+ data science blogs](https://www.cybrhome.com/topic/data-science-blogs)
* https://activewizards.com/blog/ - data science and machine learning blog
## Data Science
* [Box Plot Explanation](http://www.physics.csbsju.edu/stats/box2.html)
### Exercises
* [101 NumPy Exercises for Data Analysis (Python)](https://www.machinelearningplus.com/python/101-numpy-exercises-python/)
* [101 Pandas Exercises for Data Analysis](https://www.machinelearningplus.com/python/101-pandas-exercises-python/)
### Courses
* https://leanpub.com/universities/set/jhu/chromebook-data-science - free online educational to help anyone who can read, write, and use a computer to move into data science
### Visualization
* https://www.kdnuggets.com/2018/08/data-visualization-cheatsheet.html
* https://www.visualcinnamon.com/portfolio/
* https://bl.ocks.org/
### Books
* https://solutionsreview.com/business-intelligence/top-20-best-data-science-books-you-should-read/
* [https://github.com/wesm/pydata-book](Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media)
## Data Science Podcasts
* http://storybydata.com/humans-of-data-science-hods/
## Statistics
* https://seeing-theory.brown.edu/basic-probability/index.html - Interactive Visual Introduction to Probability and Statistics 
### Books
* [Start to learn Statistics with these 6 books](https://hackernoon.com/aspiring-data-scientists-start-to-learn-statistics-with-these-6-books-a33bbb55b8e9)
## Machine Learning
* https://www.analyticsvidhya.com/blog/ - blog
* [Machine Learning Mastery](https://machinelearningmastery.com/)
* https://sgfin.github.io/learning-resources/ - Machine Learning Resources
* [Microsoft Azure Machine Learning Studio](https://studio.azureml.net/)
* [Machine Learning Flashcards](https://machinelearningflashcards.com/) by [Chris Albon](https://chrisalbon.com/about/chris_albon/)
* [Data School](http://www.dataschool.io/) - Blog posts and video series for Data Science and Machine Learning
* [Introducing the Facebook Field Guide to Machine Learning video series](https://research.fb.com/the-facebook-field-guide-to-machine-learning-video-series/)
* https://pair-code.github.io/what-if-tool/ - inspect a machine learning model, with no coding required. 
* [Google AI PAIR initiative](https://ai.google/research/teams/brain/pair)
* [Machine Learning Glossary](https://developers.google.com/machine-learning/glossary/)  
* [Machine Learning tips and tricks cheatsheet by Stanford](https://stanford.edu/~shervine/teaching/cs-229/cheatsheet-machine-learning-tips-and-tricks)
### Blogs
* http://colah.github.io/
### Courses
* [Introduction to Machine Learning for Coders! by fast.ai](http://course.fast.ai/ml)
### Books
* http://themlbook.com/
* [Oracle - Demystifying Machine Learning](http://www.oracle.com/us/products/applications/machine-learning-v3-4442540.pdf?source=%3Aso%3Abl%3Aor%3A%3ABDBlog%20%3Aow%3Alp%3Acpo%3A%3A)
* Machine Learning: A Probabilistic Perspective by Murphy
* [Deep Learning by Goodfellow](https://www.deeplearningbook.org/)
* [Deep learning with Python](https://www.manning.com/books/deep-learning-with-python)
* [Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems](https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1491962291)
* [Confident Data Skills: Master the Fundamentals of Working with Data and Supercharge Your Career (Confident Series) by Kirill Eremenko](https://www.amazon.com/Confident-Data-Skills-Fundamentals-Supercharge-ebook/dp/B078HF5TKY)
* https://leonardoaraujosantos.gitbooks.io/artificial-inteligence/content/
## Deep Learning
* http://d2l.ai/index.html - An interactive deep learning book for students, engineers, and researchers.
* http://playground.tensorflow.org/ - an interactive visualization of neural networks
* http://www.fast.ai/ - Free courses for machine learning, deep learning and linear algebra
* [Practical Deep Learning For Coders by fast.ai Part 1](http://course.fast.ai/index.html) - free course
* [Practical Deep Learning For Coders by fast.ai Part 2](http://course.fast.ai/part2.html) - free course
* [Notes from Coursera Deep Learning courses by Andrew Ng](https://www.slideshare.net/TessFerrandez/notes-from-coursera-deep-learning-courses-by-andrew-ng)
* [Natural Language Processing with Deep Learning (Winter 2017) by Stanford University](https://www.youtube.com/playlist?list=PL3FW7Lu3i5Jsnh1rnUwq_TcylNr7EkRe6) - free video lectures
* https://deeplearning.mit.edu/
* https://distill.pub/2019/activation-atlas/
* https://stanford.edu/~shervine/teaching/cs-230/
### Books
* http://neuralnetworksanddeeplearning.com/index.html
## All Categories in One
* [Chris Albon's site](https://chrisalbon.com/) - Code Recepies for Python, Statistics, Data Science, Machine Learning, Deep Learning, Regular Expressions and more.
* [DataCamp](https://www.datacamp.com/home) - Free and paid courses
* [DataCamp Community](https://www.datacamp.com/community) - Free tutorials, cheat sheets, blog and podcast
* [DataCamp 2 months subscription using the Visual Studio subscriptions](https://docs.microsoft.com/en-us/visualstudio/subscriptions/vs-datacamp)
* [DataCamp For The Classroom](https://www.datacamp.com/groups/education) - DataCamp is 100% free when you need it for your classroom. Even the premium content!
* [Microsoft Professional Program for Artificial Intelligence](https://academy.microsoft.com/en-us/professional-program/tracks/artificial-intelligence/)
* [Andrew Ng's profile at Coursera](https://www.coursera.org/instructor/andrewng) - free Machine Learning and Deep Learning courses
* https://skymind.ai/wiki/
## Maths
* [Essential Mathematics for Artificial Intelligence](https://cloudsociety.learning.microsoft.com/courses/course-v1:Microsoft+DAT256x+2018_T2/about)
### Books
* [Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares](http://vmls-book.stanford.edu/)
### Academies
* [Khan Academy](https://khanacademy.org) - Includes Algebra, Calculus, Statistics and probability, and many other subjects.
### Other
* [Linear Transformation Visualizer](https://shadanan.github.io/MatVis/)
## Tools and Libraries
* [Google AI tools](https://ai.google/tools/)
## Datasets Resources
* https://www.quora.com/Data/Where-can-I-find-large-datasets-open-to-the-public
* [Google Dataset Search Beta](https://toolbox.google.com/datasetsearch)
* [Kaggle](https://www.kaggle.com/datasets)
* https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research
* https://archive.ics.uci.edu/ml/datasets.html
* https://opendata.cityofnewyork.us/
* https://www.gapminder.org/data/
* https://data.cityofchicago.org
## Datasets
* https://github.com/googlecreativelab/quickdraw-dataset
* https://mmspg.epfl.ch/food-image-datasets - food 5k dataset
## Visualization
* https://serialmentor.com/dataviz/ - Free online book for Data Visualization
### Best Data Visualizations
* http://flowingdata.com/2015/12/22/10-best-data-visualization-projects-of-2015/
* http://flowingdata.com/2016/12/29/best-data-visualization-projects-of-2016/
* http://flowingdata.com/2017/12/28/10-best-data-visualization-projects-of-2017/
## Competitions
* https://www.drivendata.org/competitions/
## Papers
* https://paperswithcode.com/sota
## Other
* https://datamovesme.com/ - Read about topics in Data Science or prepare for your upcoming interview.
* https://www.datasciencelive.com/ - ongoing Series of Data Science Webinars
* [Deep Learning for NLP Best Practices](http://ruder.io/deep-learning-nlp-best-practices/)
* [Google's best practices in machine learning](https://developers.google.com/machine-learning/rules-of-ml/)
* [Top 10 Machine Learning Algorithms for Beginners](https://www.dataquest.io/blog/top-10-machine-learning-algorithms-for-beginners/)
* https://www.datacamp.com/community/blog/data-science-periodic-table - Everyting that you need to start to learn Data Science: courses, boot camps, conferences, data, projects, challenges, competitions, programming languages and distributions, data management, machine learning and stats, data visualization, collaboration, community and Q&A, news, newsletters, blogs, and podcasts.
* [Data Scientist Interviews - Vol 1, April 2014](https://www.datascienceweekly.org/articles/data-scientist-interviews-vol-1-april-2014)
* [Data Scientist Interviews - Vol 2, December 2014](https://www.datascienceweekly.org/articles/data-scientist-interviews-volume-2-december-2014)
* [Top 10 Machine Learning Projects for Beginners](https://www.dezyre.com/article/top-10-machine-learning-projects-for-beginners/397)
* [9 Must-have skills you need to become a Data Scientist](https://www.kdnuggets.com/2018/05/simplilearn-9-must-have-skills-data-scientist.html)
* https://ai.google/education/ - Learn from ML experts at Google
* [200+ Best Machine Learning, Natural Language Process, and Python Tutorials for Programmers - 2018](http://houseofbots.com/news-detail/3679-4-200-plus-best-machine-learning-natural-language-process-and-python-tutorials-for-programmers-2018)
* https://mybinder.org/ - Turn a GitHub repo into a collection of interactive notebooks
* [Top Data Science Online Courses in 2018](https://www.learndatasci.com/best-data-science-online-courses/)
* https://stanford.edu/~shervine/teaching/cs-229.html - Supervised Learning, Unsupervised Learning, Deep Learning, Tips and Tricks, Matlab, Probabilities and Statistics, Linear Algebrat and calculus
* https://www.packtpub.com/packt/offers/free-learning - Free ebooks
* http://www.claoudml.co/ - Free resources collected by Randy Lao

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