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dive-into-machine-learning
Free ways to dive into machine learning with Python and Jupyter Notebook. Notebooks, courses, and other links. (First posted in 2016.)
https://github.com/dive-into-machine-learning/dive-into-machine-learning
- Python
- Jupyter Notebook
- Anaconda Python distribution - guide-to-virtualenvs/))
- Binder
- Deepnote - time collaboration
- Google Colab
- markusschanta/awesome-jupyter, "Hosted Notebook Solutions"
- ml-tooling/best-of-jupyter, "Notebook Environments"
- Learn how to use Jupyter Notebook - 10 minutes).** (You can [learn by screencast](https://www.youtube.com/watch?v=qb7FT68tcA8) instead.)
- An introduction to machine learning with scikit-learn
- 
- Dr. Randal Olson's Example Machine Learning notebook
- Launch in Binder, no installation steps required
- trekhleb/machine-learning-experiments
- trekhleb/homemade-machine-learning
- Jupyter's official Gallery of Interesting Jupyter Notebooks: Statistics, Machine Learning and Data Science - gallery-of-interesting-Jupyter-Notebooks/ae03c01ed25024aa06a4479ea600895d59b38bc4))
- Prof. Andrew Ng's - learning) is a popular and esteemed free online course. I've seen it [recommended](https://www.quora.com/How-do-I-learn-machine-learning-1/answer/Cory-Hicks-1) [often.](https://www.quora.com/How-do-I-learn-machine-learning-1/answer/Xavier-Amatriain) [And emphatically.](https://www.forbes.com/sites/anthonykosner/2013/12/29/why-is-machine-learning-cs-229-the-most-popular-course-at-stanford/)**
- Understanding Machine Learning
- Elements of Statistical Learning - into-machine-learning/issues/29)
- Awesome Public Datasets - change-data`](https://github.com/KKulma/climate-change-data#open-data)
- Study tips for Prof. Andrew Ng's course, by Ray Li
- Review: Andrew Ng's Machine Learning Course
- The user reviews on Coursera
- "Learning How to Learn" by Barbara Oakley
- Barbara Oakley's book _A Mind for Numbers: How to Excel at Math and Science_ - a-mind-for-numbers)) — "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_ - make-it-stick))
- Practical Data Science
- Python Data Science Handbook, as Jupyter Notebooks
- `microsoft/Data-Science-For-Beginners` - data-science-for-beginners-curriculum-on-github-1hme) — "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'."
- `microsoft/ML-For-Beginners` - Science-For-Beginners`](https://github.com/microsoft/Data-Science-For-Beginners)
- Data Science Specialization
- Prof. Pedro Domingos's introductory video series
- `ossu/data-science` - science`](https://github.com/ossu/computer-science))
- Stanford CS229: Machine Learning
- Harvard CS109: Data Science
- Advanced Statistical Computing (Vanderbilt BIOS8366)
- Intro to Machine Learning with scikit-learn
- UC Berkeley's Data 8: The Foundations of Data Science
- Machine Learning Module (GitHub Mirror).
- An epic Quora thread: How can I become a data scientist?
- `ujjwalkarn/Machine-Learning-Tutorials`
- datascience.stackexchange.com - learning_.](https://stats.stackexchange.com/questions/tagged/machine-learning) There are some subreddits, like [/r/LearningMachineLearning](https://www.reddit.com/r/learningmachinelearning) and [/r/MachineLearning](https://www.reddit.com/r/machinelearning).
- /r/LearnMachineLearning
- /r/MachineLearning
- /r/DataIsBeautiful
- /r/DataScience
- Cross-Validated: stats.stackexchange.com
- `ossu/data-science` has a Discord server and newsletter
- Things in Pandas I Wish I'd Had Known Earlier
- 10 Minutes to Pandas
- Real World Data Cleanup with Python and Pandas
- Video series from Data School, about Pandas
- Cookbook
- Data Structures - docs/stable/dsintro.html#dataframe) section
- Reshaping by pivoting DataFrames
- Computational tools - is-covariance-in-plain-language)
- Group By (split, apply, and combine DataFrames)
- Visualizing your DataFrames
- `dask` - like interface, but for larger-than-memory data and "under the hood" parallelism.
- `vaex` - of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualize and explore big tabular data at a billion rows per second"
- `birdseye`
- `snoop`
- `pandas-log`
- "A Few Useful Things to Know about Machine Learning."
- Machine Learning: The High-Interest Credit Card of Technical Debt
- listen to a podcast episode interviewing one of the authors of this paper
- Awesome Production Machine Learning - preserving ML](https://github.com/EthicalML/awesome-production-machine-learning#privacy-preserving-machine-learning), by the way!
- "Rules of Machine Learning: Best Practices for [Reliable
- The High Cost of Maintaining Machine Learning Systems
- Overfitting vs. Underfitting: A Conceptual Explanation
- 11 Clever Methods of Overfitting and How to Avoid Them
- "So, you want to build an ethical algorithm?" An interactive tool to prompt discussions - ethics)
- OpenReview.net
- OpenReview organization on GitHub
- OpenReview.net
- OpenReview.net
- OpenReview Sponsors
- MLOps Stack Template
- Lessons on ML Platforms from Netflix, DoorDash, Spotify, and more
- MLOps Stack Canvas - ops.org](https://ml-ops.org/)
- EthicalML/awesome-artificial-intelligence-guidelines
- EthicalML/awesome-production-machine-learning
- visenger/awesome-ml-model-governance
- visenger/awesome-MLOps
- eugeneyan/applied-ml
- Replicate
- `cog`
- _Dive into Deep Learning_ - An interactive book about deep learning** ([view on GitHub](https://github.com/d2l-ai/d2l-en))
- Run this book locally, using Jupyter Notebooks
- Run this book in your browser, using Google Colab
- Prof. Andrew Ng's - learning)!** There five courses, as part of the [Deep Learning Specialization on Coursera](https://www.coursera.org/specializations/deep-learning). These courses are part of his new venture, [deeplearning.ai](https://www.deeplearning.ai)
- ashishpatel26/Andrew-NG-Notes
- _Deep Learning_
- "What are the best ways to pick up Deep Learning skills as an engineer?"
- `fastai/fastbook`
- `explosion/thinc`
- paperswithcode.com
- `labmlai/annotated_deep_learning_paper_implementations` - by-side notes." 50+ of them! Really nicely annotated and explained.
- Distill.pub
- subject-matter experts and domain experts
- "The UX of AI" by Josh Lovejoy - consensus/).** Suggested reading: [Martin Zinkevich's "Rules of ML Engineering", Rule #23: "You are not a typical end user"](https://developers.google.com/machine-learning/guides/rules-of-ml/#human_analysis_of_the_system)
- On Hacker News, user olympus commented to say you could use competitions to practice and evaluate yourself - research-code#results-leaderboards) or [here](https://towardsdatascience.com/12-data-science-ai-competitions-to-advance-your-skills-in-2021-32e3fcb95d8c).)
- for example, the "No Free Hunch" blog
- Machine Learning isn't just about Kaggle competitions
- "most important thing in data science is the question" - public-datasets). Analyze it. Then ...
- learning in public
- look for public datasets - is-plural), a newsletter of interesting datasets. When a question inspires you, try exploring it with the skills you're learning.
- a conversation - into-machine-learning/issues/11#issuecomment-154135498)
- here's a video about the scientific method in data science.
- "Advice on building a machine learning career and reading research papers by Prof. Andrew Ng"
- Papers With Code
- MIT: Papers + Code - 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
- Python Data Science Handbook, as Jupyter Notebooks
- `r0f1/datascience`
- the "Bayesian Machine Learning" overview on Metacademy
- Probabilistic Programming and Bayesian Methods for Hackers - first, mathematics-second point of view." Uses [PyMC](https://github.com/pymc-devs/pymc). It's available in print too!
- 19 Questions
- _Time Series Forecasting with Bayesian Modeling by Michael Grogan_ - project series - paid but the first project is free.
- Bayesian Modelling in Python - devs/pymc) as well.
- Machine Learning for Software Engineers, by Nam Vu - 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`
- `microsoft/ML-For-Beginners` - Science-For-Beginners`](https://github.com/microsoft/Data-Science-For-Beginners)
- Machine Learning Crash Course from Google - ai))
- Amazon AWS - learning/learn/))
- _Machine Learning with PyTorch and Scikit-Learn_ by Sebastian Raschka, Yuxi (Hayden) Liu, and Vahid Mirjalili
Programming Languages
Keywords
machine-learning
19
deep-learning
13
python
13
data-science
10
artificial-intelligence
5
ai
5
pytorch
5
awesome
5
awesome-list
4
neural-networks
4
jupyter-notebook
4
numpy
3
data-visualization
3
tensorflow
3
machine-learning-algorithms
3
scikit-learn
3
machinelearning
3
pandas
3
ml
3
neural-network
3
reinforcement-learning
3
jupyter
3
deep-learning-tutorial
2
data-analysis
2
data-mining
2
deeplearning
2
natural-language-processing
2
deep-neural-networks
2
mlops
2
jupyterhub
2
jupyterlab
2
jupyterlab-extension
2
data-protection
1
ethical-ai
1
ethics-frameworks
1
guidelines
1
institute-for-ethical-ai
1
collections
1
best-of-list
1
best-of
1
machine-learning-guidelines
1
visualization
1
principles
1
privacy
1
regulation
1
devops
1
engineering
1
federated-learning
1
software-engineering
1
applied-data-science
1