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dive-into-machine-learning/dive-into-machine-learning

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Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)
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Mar 27th

Assorted Tips and Resources

Peer review

  • Open Peer Review: We provide a configurable platform for peer review that generalizes over many subtle gradations of openness, allowing conference organizers, journals, and other "reviewing entities" to configure the specific policy of their choice. We intend to act as a testbed for different policies, to help scientific communities experiment with open scholarship while addressing legitimate concerns regarding confidentiality, attribution, and bias.
  • Open Publishing: Track submissions, coordinate the efforts of editors, reviewers and authors, and host… Sharded and distributed for speed and reliability.
  • Open Access: Free access to papers for all, free paper submissions. No fees.
  • Assorted Tips and Resources

    Easier sharing of deep learning models and demos

  • 🐣 Replicate "makes it easy to share a running machine learning model"
    • Easily try out deep learning models from your browser
    • The demos link to papers/code on GitHub, if you want to dig in and see how something works
    • The models run in containers built by cog, "containers for machine learning."
      • It's an open-source tool for putting models into reproducible Docker containers.
      • You can put models in containers with just Python and YAML.
    • There's an API for Replicate to run predictions for you
  • Mar 14th

    More ways to "Dive into Machine Learning"

  • Machine Learning for Software Engineers, by Nam Vustars26.2k. In their words, it's a "top-down and results-first approach designed for software engineers." Definitely bookmark and use it. It can answer many questions and connect you with great resources.
  • ujjwalkarn/Machine-Learning-Tutorials
  • josephmisiti/awesome-machine-learning
  • Mar 12th

    Related to machine learning

  • humanetech-community/awesome-humane-tech — "Promoting solutions that improve wellbeing, freedom and society"
  • Code against climate change

  • ProjectDrawdown/solutionsProject Drawdown — "Project Drawdown entered the climate conversation with the publication of the 2017 book. With The Drawdown Review in 2020, the project continues its mission to inspire and communicate solutions." Python and Jupyter Notebooks.
  • Mar 4th

    Related to machine learning

  • AlgorithmWatchnewsletter — "a non-profit research and advocacy organization that is committed to watch, unpack and analyze automated decision-making (ADM) systems and their impact on society."
  • daviddao/awful-ai — "Awful AI is a curated list to track current scary usages of AI — hoping to raise awareness"
  • Code against climate change

  • philsturgeon/awesome-earth
  • daviddao/code-against-climate-change
  • protontypes/open-sustainable-technology
  • Other courses

  • See also microsoft/ML-For-Beginners
  • Explore another notebook

  • Series of notebooks:
  • More ways to "Dive into Machine Learning"

  • Courses by cloud vendors. These are usually high quality content but steer you heavily to use vendor-specific tools/services. To avoid getting locked into vendor specifics, you can make sure you're learning from other resources as well.
  • Feb 26th

    Explore another notebook

  • Various topical notebooks:
  • Getting Help: Questions, Answers, Chats

    Some communities to know about!

  • /r/LearnMachineLearning
  • /r/MachineLearning
  • /r/DataIsBeautiful
  • /r/DataScience
  • Cross-Validated: stats.stackexchange.com
  • ossu/data-science has a Discord server and newsletter
  • Skilling up

  • Ask a question. Start exploring some data. The "most important thing in data science is the question" (Dr. Jeff T. Leek). So start with a question. Then, find real datastars51.1k. Analyze it. Then ...
  • Communicate results. When you think you have a novel finding, ask for review. When you're still learning, ask in informal communities (some are linked below).
  • Learn from feedback. Consider learning in public, it works great for some folks. (Don't pressure yourself yet though! Everybody is different, and it's good to know your learning style.)
  • More ways to "Dive into Machine Learning"

  • 2022: Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili
  • Feb 2nd

    More Data Science materials

  • r0f1/datascience — "A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks."
  • Supplement: Troubleshooting

  • birdseye, snoop
  • pandas-log
  • Jan 23rd

    Dive into Machine Learning

  • You know Python or you'restars776 learning itstars142.2k 🐍stars124.1k
  • Jan 16th

    Other courses

  • microsoft/Data-Science-For-Beginnersadded in 2021 — "10-week, 20-lesson curriculum all about Data Science. Each lesson includes pre-lesson and post-lesson quizzes, written instructions to complete the lesson, a solution, and an assignment. Our project-based pedagogy allows you to learn while building, a proven way for new skills to 'stick'."
  • Jan 2nd

    Deep Learning

  • Dive into Deep Learning - An interactive book about deep learning (view on GitHubstars15k)
  • Dec 31st, 2021

    Supplement: Learning Pandas well

  • Bookmarks for scaling pandas and alternatives
    • dask: A Pandas-like interface, but for larger-than-memory data and "under the hood" parallelism.
    • vaex: "Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualize and explore big tabular data at a billion rows per second"
  • Dec 27th, 2021

    Prof. Andrew Ng's Machine Learning on Coursera

    Tips for this course

  • Study tips for Prof. Andrew Ng's course, by Ray Li
  • If you're wondering, Is it still a relevant course? or trying to figure out if it fits for you personally, check out these reviews:
  • Prof. Andrew Ng's Machine Learning on Coursera

    Tips for studying on a busy schedule

  • "Learning How to Learn" by Barbara Oakley by Barbara Oakley, a free video course on Coursera.
  • Prefer book/audiobook? These are great options:
  • Dec 23rd, 2021

    Assorted Tips and Resources

    Risks - some starting points

  • "Rules of Machine Learning: Best Practices for [Reliable] ML Engineering," by Martin Zinkevich, regarding ML engineering practices.
  • Dec 18th, 2021

    Assorted Tips and Resources

    Peer review

  • Open Discussion: Hosting of accepted papers, with their reviews, comments. Continued discussion forum associated with the paper post acceptance. Publication venue chairs/editors can control structure of review/comment forms, read/write access, and its timing.
  • Open Directory: Collection of people, with conflict-of-interest information, including institutions and relations, such as co-authors, co-PIs, co-workers, advisors/advisees, and family connections.
  • Open Recommendations: Models of scientific topics and expertise. Directory of people includes scientific expertise. Reviewer-paper matching for conferences with thousands of submissions, incorporating expertise, bidding, constraints, and reviewer balancing of various sorts. Paper recommendation to users.
  • Open API: We provide a simple REST API [...]
  • Open Source: We are committed to open source. Many parts of OpenReview are already in the OpenReview organization on GitHub. Some further releases are pending a professional security review of the codebase.
  • Assorted Tips and Resources

    Risks - some starting points

  • Overfitting vs. Underfitting: A Conceptual Explanation
  • Dec 2nd, 2021

    Tools you'll need

    If you prefer local installation

  • Python. Python 3 is the best option.
  • Jupyter Notebook. (Formerly known as IPython Notebook.)
  • Some scientific computing packages:
    • numpy
    • pandas
    • scikit-learn
    • matplotlib
  • Jargon note

  • What is the difference between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data?
  • Another handy term: "Data Engineering."
  • Assorted Tips and Resources

    Risks - some starting points

  • Awesome Production Machine Learningstars12.3k, "a curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning." It includes a section about privacy-preserving MLstars12.3k, by the way!
  • The High Cost of Maintaining Machine Learning Systems
  • 11 Clever Methods of Overfitting and How to Avoid Them
  • "So, you want to build an ethical algorithm?" An interactive tool to prompt discussions (source)stars57
  • More Data Science materials

    Aside: Bayesian Statistics and Machine Learning

  • The free book Probabilistic Programming and Bayesian Methods for Hackers. Made with a "computation/understanding-first, mathematics-second point of view." Uses PyMCstars7k. It's available in print too!
  • Like learning by playing? Me too. Try 19 Questionsstars15, "a machine learning game which asks you questions and guesses an object you are thinking about," and explains which Bayesian statistics techniques it's using!
  • Time Series Forecasting with Bayesian Modeling by Michael Grogan, a 5-project series - paid but the first project is free.
  • Bayesian Modelling in Pythonstars2.4k. Uses PyMCstars7k as well.
  • Nov 20th, 2021

    Supplement: Learning Pandas well

  • Essential: Things in Pandas I Wish I'd Had Known Earlier (as a Jupyter Notebook)
  • More Data Science materials

  • Python Data Science Handbook, as Jupyter Notebooks
  • Assorted Tips and Resources

    Production, Deployment, MLOps

  • MLOps Stack Template by Henrik Skogström
  • Lessons on ML Platforms from Netflix, DoorDash, Spotify, and more by Ernest Chan in Towards Data Science
  • MLOps Stack Canvas at ml-ops.org
  • Nov 16th, 2021

    Tools you'll need

    Cloud-based options

  • Binder is Jupyter Notebook's official choice to try JupyterLab
  • Oct 23rd, 2021

    Other courses

  • Data science courses as Jupyter Notebooks:
  • Dive into Machine Learning

  • You care about the ethics of MLstars863
  • Oct 18th, 2021

    Tools you'll need

    Cloud-based options

  • Deepnote allows for real-time collaboration
  • Google Colab provides "free" GPUs
  • Explore another notebook

  • Dr. Randal Olson's Example Machine Learning notebook: "let's pretend we're working for a startup that just got funded to create a smartphone app that automatically identifies species of flowers from pictures taken on the smartphone. We've been tasked by our head of data science to create a demo machine learning model that takes four measurements from the flowers (sepal length, sepal width, petal length, and petal width) and identifies the species based on those measurements alone."
  • Dive into Machine Learning

  • You're new to Machine Learning
  • You learn by doing
  • Jul 18th, 2017

    Supplement: Learning Pandas well

  • Another helpful tutorial: Real World Data Cleanup with Python and Pandas
  • Oct 19th, 2016

    Supplement: Learning Pandas well

  • Video series from Data School, about Pandas. "Reference guide to 30 common pandas tasks (plus 6 hours of supporting video)."
  • Last Checked At: 2022-09-21T14:38:45.485Z
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