hangtwenty/dive-into-machine-learning

Learn 2 months ago 11k

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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View on Github

Mar 21st - Mar 27th, 2022

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.

Mar 14th - Mar 20th, 2022

More ways to "Dive into Machine Learning"

Machine Learning for Software Engineers, by Nam Vu stars25.9k. 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 7th - Mar 13th, 2022

Related to machine learning

humanetech-community/awesome-humane-tech — "Promoting solutions that improve wellbeing, freedom and society"

Code against climate change

ProjectDrawdown/solutions — Project 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.

Feb 28th - Mar 6th, 2022

Related to machine learning

AlgorithmWatch — newsletter — "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

Explore another notebook

Series of notebooks:

2022: rasbt/machine-learning-book — notebooks from Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili

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 21st - Feb 27th, 2022

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 data stars49k. 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

Jan 31st - Feb 6th, 2022

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 17th - Jan 23rd, 2022

Dive into Machine Learning

You know Python or you're stars734 learning it stars128.5k 🐍stars114.2k

Jan 10th - Jan 16th, 2022

Other courses

microsoft/Data-Science-For-Beginners — added 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'."

Dec 27th - Jan 2nd, 2021

Deep Learning

Dive into Deep Learning - An interactive book about deep learning (view on GitHub stars13.5k)

Quickstart:
- Run this book locally, using Jupyter Notebooks
- Run this book in your browser, using Google Colab
"The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code."
"You can modify the code and tune hyperparameters to get instant feedback to accumulate practical experiences in deep learning."

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"

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:

Barbara Oakley's book A Mind for Numbers: How to Excel at Math and Science (reviews) — "We all have what it takes to excel in areas that don't seem to come naturally to us at first"
Make It Stick: the Science of Successful Learning (reviews)

Dec 20th - Dec 26th, 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 13th - Dec 19th, 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

Nov 29th - Dec 5th, 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."

"MLOps" overlaps with Data Eng, and there's an introductory MLOps section later in this guide.

Assorted Tips and Resources

Risks - some starting points

Awesome Production Machine Learning stars11.6k, "a curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning." It includes a section about privacy-preserving ML stars11.6k, 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)stars56

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 PyMC stars6.6k. It's available in print too!

Like learning by playing? Me too. Try 19 Questions stars15, "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 Python stars2.4k. Uses PyMC stars6.6k as well.

Nov 15th - Nov 21st, 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

Tools you'll need

Cloud-based options

Binder is Jupyter Notebook's official choice to try JupyterLab

Oct 18th - Oct 24th, 2021

Other courses

Data science courses as Jupyter Notebooks:

Dive into Machine Learning

You care about the ethics of ML stars784

You're new to Machine Learning

You learn by doing

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."

Launch in Binder, no installation steps required

Jul 17th - Jul 23rd, 2017

Supplement: Learning Pandas well

Another helpful tutorial: Real World Data Cleanup with Python and Pandas

Oct 17th - Oct 23rd, 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)."

Jan 11th - Jan 17th, 2016

Supplement: Learning Pandas well

Essential: 10 Minutes to Pandas

Here are some docs I found especially helpful as I continued learning:

Last Checked At: 2022-05-28T16:10:50.090Z

lucasviola/awesome-tech-videos

watson/awesome-computer-history

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Computer Science

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Higher Education

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Miscellaneous

Unmaintained

Top 50 Awesome List

Recently Updated

Platforms

Programming Languages

Front-End Development

Back-End Development

Computer Science

Big Data

Theory

Books

Editors

Gaming

Development Environment

Entertainment

Databases

Media

Learn

Security

Content Management Systems

Hardware

Business

Work

Networking

Decentralized Systems

Higher Education

Events

Testing

Miscellaneous

Unmaintained

hangtwenty/dive-into-machine-learning

Assorted Tips and Resources

Peer review

Assorted Tips and Resources

Easier sharing of deep learning models and demos

Mar 14th - Mar 20th, 2022

More ways to "Dive into Machine Learning"

Mar 7th - Mar 13th, 2022

Related to machine learning

Code against climate change

Feb 28th - Mar 6th, 2022

Related to machine learning

Code against climate change

Other courses

Explore another notebook

More ways to "Dive into Machine Learning"

Feb 21st - Feb 27th, 2022

Explore another notebook

Getting Help: Questions, Answers, Chats

Some communities to know about!