Track Dive Into Machine Learning Updates Daily

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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[ Daily / Weekly / Overview ]

Jun 17, 2022

Supplement: Troubleshooting / 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 27, 2022

Supplement: Troubleshooting / Production, Deployment, MLOps

• eugeneyan/applied-ml (⭐22k)

Mar 14, 2022

More ways to "Dive into Machine Learning" / Aside: Bayesian Statistics and 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.
  • microsoft/ML-For-Beginners, microsoft/Data-Science-For-Beginners
  • Machine Learning Crash Course from Google (more of their options)
  • Amazon AWS (more of their options)

• ujjwalkarn/Machine-Learning-Tutorials

• Machine Learning for Software Engineers, by Nam Vu (⭐26k). 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.

• josephmisiti/awesome-machine-learning

Mar 04, 2022

Explore another notebook / What just happened?

• 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

• Dr. Randal Olson's Example Machine Learning notebook (⭐5.4k): "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

• Various topical notebooks:
  • trekhleb/machine-learning-experiments (⭐1.3k)
  • trekhleb/homemade-machine-learning (⭐20k)

Prof. Andrew Ng's Machine Learning on Coursera / Tips for this course

• 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:
  • Review: Andrew Ng's Machine Learning Course
  • The user reviews on Coursera

Feb 26, 2022

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

Deep Learning / Easier sharing of deep learning models and demos

• fastai/fastbook by Jeremy Howard and Sylvain Gugger — "an introduction to deep learning, fastai and PyTorch."

• explosion/thinc is an interesting library that wraps PyTorch, TensorFlow and MXNet models.
  • "Concise functional-programming approach to model definition, using composition rather than inheritance."
  • "Integrated config system to describe trees of objects and hyperparameters."

Skilling up / Machine Learning and User Experience (UX)

• "Advice on building a machine learning career and reading research papers by Prof. Andrew Ng"

• Some links for finding/following interesting papers/code:
  • Papers With Code is a popular site to follow, and it can lead you to other resources. github.com/paperswithcode
  • MIT: Papers + Code — "Peer-review is the lifeblood of scientific validation and a guardrail against runaway hype in AI. Our commitment to publishing in the top venues reflects our grounding in what is real, reproducible, and truly innovative."
  • papers.labml.ai/papers/weekly, monthly

• Pull requests welcome!

More ways to "Dive into Machine Learning" / Aside: Bayesian Statistics and Machine Learning

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

Feb 02, 2022

Supplement: Learning Pandas well / Some communities to know about!

• 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"

Supplement: Troubleshooting / Some communities to know about!

• birdseye, snoop

• pandas-log

Deep Learning / Easier sharing of deep learning models and demos

• Distill.pub publishes explorable explanations, definitely worth exploring and following!

More Data Science materials / Machine Learning and User Experience (UX)

• 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."

Jan 17, 2022

Deep Learning / Easier sharing of deep learning models and demos

• paperswithcode.com — "The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code, datasets, methods and evaluation tables."

• labmlai/annotated_deep_learning_paper_implementations — "Implementations/tutorials of deep learning papers with side-by-side notes." 50+ of them! Really nicely annotated and explained.

Jan 16, 2022

Other courses / Take my tips with a grain of salt

• 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'."

• Prof. Pedro Domingos's introductory video series. Prof. Pedro Domingos wrote the paper "A Few Useful Things to Know About Machine Learning", which you may remember from earlier in the guide.

• ossu/data-science (see also ossu/computer-science)

• Prof. Mark A. Girolami's Machine Learning Module (GitHub Mirror). (⭐440) "Good for people with a strong mathematics background."

Jan 15, 2022

Deep Learning / Easier sharing of deep learning models and demos

• Deep Learning, a free book published MIT Press. By Ian Goodfellow, Yoshua Bengio and Aaron Courville.
  • A notable testimonial for it is here: "What are the best ways to pick up Deep Learning skills as an engineer?"

Jan 02, 2022

Other courses / Take my tips with a grain of salt

• Kevin Markham's video series, Intro to Machine Learning with scikit-learn, starts with what we've already covered, then continues on at a comfortable place.

• UC Berkeley's Data 8: The Foundations of Data Science course and the textbook Computational and Inferential Thinking teaches critical concepts in Data Science.

Deep Learning / Easier sharing of deep learning models and demos

• Dive into Deep Learning - An interactive book about deep learning (view on GitHub (⭐15k))
  • 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."

Jan 01, 2022

Deep Learning / Easier sharing of deep learning models and demos

• Prof. Andrew Ng's courses on Deep Learning! There five courses, as part of the Deep Learning Specialization on Coursera. These courses are part of his new venture, deeplearning.ai
  • Some course notes about it: ashishpatel26/Andrew-NG-Notes (⭐1.4k)

Dec 29, 2021

Other courses / Take my tips with a grain of salt

• Stanford CS229: Machine Learning (⭐14k)

• Advanced Statistical Computing (Vanderbilt BIOS8366). Interactive.

Dec 27, 2021

Prof. Andrew Ng's Machine Learning on Coursera / Tips for this course

• Study tips for Prof. Andrew Ng's course, by Ray Li

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 23, 2021

Supplement: Troubleshooting / Risks - some starting points

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

Dec 18, 2021

Supplement: Troubleshooting / Risks - some starting points

• Overfitting vs. Underfitting: A Conceptual Explanation

Dec 02, 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

Other courses / Take my tips with a grain of salt

• Data science courses as Jupyter Notebooks:
  • Practical Data Science
  • Python Data Science Handbook, as Jupyter Notebooks

• Coursera's Data Science Specialization

• Harvard CS109: Data Science

• An epic Quora thread: How can I become a data scientist?

Supplement: Learning Pandas well / Some communities to know about!

• Here are some docs I found especially helpful as I continued learning:
  • Cookbook
  • Data Structures, esp. DataFrame section
  • Reshaping by pivoting DataFrames
  • Computational tools and StackExchange thread: "What is covariance in plain language?"
  • Group By (split, apply, and combine DataFrames)
  • Visualizing your DataFrames

Supplement: Troubleshooting / Risks - some starting points

• Awesome Production Machine Learning (⭐12k), "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 (⭐12k), 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) (⭐58)

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 (⭐7.1k). It's available in print too!

• Like learning by playing? Me too. Try 19 Questions (⭐15), "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 (⭐2.4k). Uses PyMC (⭐7.1k) as well.

Nov 20, 2021

Explore another notebook / What just happened?

• Jupyter's official Gallery of Interesting Jupyter Notebooks: Statistics, Machine Learning and Data Science (⭐14k) (permalink (⭐14k))

Other courses / Take my tips with a grain of salt

• There are more alternatives linked at the bottom of this guide

Supplement: Learning Pandas well / Some communities to know about!

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

Supplement: Troubleshooting / 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

• EthicalML/awesome-artificial-intelligence-guidelines (⭐880)

• visenger/awesome-MLOps (⭐8.7k)

More Data Science materials / Machine Learning and User Experience (UX)

• Python Data Science Handbook, as Jupyter Notebooks

Nov 17, 2021

Supplement: Troubleshooting / Production, Deployment, MLOps

• EthicalML/awesome-production-machine-learning (⭐12k)

• visenger/awesome-ml-model-governance (⭐52)

Nov 16, 2021

Tools you'll need / Cloud-based options

• Binder is Jupyter Notebook's official choice to try JupyterLab

Oct 20, 2021

Tools you'll need / Cloud-based options

• markusschanta/awesome-jupyter, "Hosted Notebook Solutions" (⭐2.9k)

• ml-tooling/best-of-jupyter, "Notebook Environments" (⭐538)

Oct 18, 2021

Tools you'll need / Cloud-based options

• Deepnote allows for real-time collaboration

• Google Colab provides "free" GPUs

Jul 18, 2017

Supplement: Learning Pandas well / Some communities to know about!

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

Oct 19, 2016

Supplement: Learning Pandas well / Some communities to know about!

• Video series from Data School, about Pandas. "Reference guide to 30 common pandas tasks (plus 6 hours of supporting video)."

Jan 13, 2016

Supplement: Learning Pandas well / Some communities to know about!

• Essential: 10 Minutes to Pandas