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

Supplement: Troubleshooting / Production, Deployment, MLOps

• eugeneyan/applied-ml (⭐22k)

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

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

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

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."

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.

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."

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?"

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)

Other courses / Take my tips with a grain of salt

• Stanford CS229: Machine Learning (⭐14k)

• Advanced Statistical Computing (Vanderbilt BIOS8366). Interactive.

• 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."

• 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)

Supplement: Troubleshooting / Risks - some starting points

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

Supplement: Troubleshooting / Risks - some starting points

• Overfitting vs. Underfitting: A Conceptual Explanation

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.

Tools you'll need / Cloud-based options

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

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)

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

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

• visenger/awesome-MLOps (⭐8.7k)

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

• Python Data Science Handbook, as Jupyter Notebooks

Tools you'll need / Cloud-based options

• Deepnote allows for real-time collaboration

• Google Colab provides "free" GPUs

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

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

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

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

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)."

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

• Essential: 10 Minutes to Pandas