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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

Last synced: about 21 hours ago
JSON representation

[Prof. Andrew Ng's _Machine Learning_ on Coursera](https://www.coursera.org/learn/machine-learning)
- Tips for this course
- What just happened?
  - 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)
- Tips for studying on a busy schedule
  - 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))
Supplement: Troubleshooting
Tools you'll need
- If you prefer local installation
  - Python
  - Jupyter Notebook
- Cloud-based options
  - Binder
  - Deepnote - time collaboration
  - Google Colab
  - markusschanta/awesome-jupyter, "Hosted Notebook Solutions"
  - ml-tooling/best-of-jupyter, "Notebook Environments"
Let's go!
- Cloud-based options
  - 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
  - ![I'll wait. - learn.org/stable/tutorial/basic/tutorial.html)
  - An introduction to machine learning with scikit-learn
- What just happened?
  - scikit-learn
A Visual Introduction to Machine Learning
- What just happened?
  - "A Visual Introduction to Machine Learning, Part 1"
  - ![A Visual Introduction to Machine Learning, Part 1 - intro-to-machine-learning-part-1/)
Jargon note
- What just happened?
Explore another notebook
- What just happened?
  - Dr. Randal Olson's Example Machine Learning notebook
  - Launch in Binder, no installation steps required
  - Jupyter's official Gallery of Interesting Jupyter Notebooks: Statistics, Machine Learning and Data Science - gallery-of-interesting-Jupyter-Notebooks/ae03c01ed25024aa06a4479ea600895d59b38bc4))
Skilling up
- Machine Learning and User Experience (UX)
  - 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.
  - 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
  - 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"
  - 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
  - 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.
  - for example, the "No Free Hunch" blog
  - a conversation - into-machine-learning/issues/11#issuecomment-154135498)
  - learning in public
  - Papers With Code
Other courses
- Take my tips with a grain of salt
Supplement: Learning Pandas well
- Some communities to know about!
  - Cookbook
  - 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
  - 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"
  - `dask` - like interface, but for larger-than-memory data and "under the hood" parallelism.
  - `dask` - like interface, but for larger-than-memory data and "under the hood" parallelism.
Getting Help: Questions, Answers, Chats
- Take my tips with a grain of salt
  - 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).
- Some communities to know about!
Deep Learning
- Easier sharing of deep learning models and demos
  - _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)
  - _Deep Learning_
  - "What are the best ways to pick up Deep Learning skills as an engineer?"
  - Distill.pub
Collaborate with Domain Experts
- Easier sharing of deep learning models and demos
  - subject-matter experts and domain experts
- Machine Learning and User Experience (UX)
  - "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)
More Data Science materials
- Aside: Bayesian Statistics and Machine Learning
  - 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!
  - _Time Series Forecasting with Bayesian Modeling by Michael Grogan_ - project series - paid but the first project is free.
More ways to "Dive into Machine Learning"
- Aside: Bayesian Statistics and Machine Learning
  - `josephmisiti/awesome-machine-learning`
  - Amazon AWS - learning/learn/))
  - `ujjwalkarn/Machine-Learning-Tutorials`

Programming Languages

Python 1

Ecosyste.ms: Awesome

dive-into-machine-learning

[Prof. Andrew Ng's _Machine Learning_ on Coursera](https://www.coursera.org/learn/machine-learning)

Tips for this course

What just happened?

Tips for studying on a busy schedule

Supplement: Troubleshooting

Risks - some starting points

Production, Deployment, [MLOps](https://ml-ops.org/)

Some communities to know about!

Peer review

Tools you'll need

If you prefer local installation

Cloud-based options

Let's go!

Cloud-based options

What just happened?

A Visual Introduction to Machine Learning

What just happened?

Jargon note

What just happened?

Explore another notebook

What just happened?

Skilling up

Machine Learning and User Experience (UX)

Other courses

Take my tips with a grain of salt

Supplement: Learning Pandas well

Some communities to know about!

Getting Help: Questions, Answers, Chats

Take my tips with a grain of salt

Some communities to know about!

Deep Learning

Collaborate with Domain Experts

Machine Learning and User Experience (UX)

More Data Science materials

Aside: Bayesian Statistics and Machine Learning

More ways to "Dive into Machine Learning"

Aside: Bayesian Statistics and Machine Learning

Ecosyste.ms: Awesome

dive-into-machine-learning

[Prof. Andrew Ng's _Machine Learning_ on Coursera](https://www.coursera.org/learn/machine-learning)

Tips for this course

What just happened?

Tips for studying on a busy schedule

Supplement: Troubleshooting

Risks - some starting points

Production, Deployment, [MLOps](https://ml-ops.org/)

Some communities to know about!

Peer review

Easier sharing of deep learning models and demos

Tools you'll need

If you prefer local installation

Cloud-based options

Let's go!

Cloud-based options

What just happened?

A Visual Introduction to Machine Learning

What just happened?

Jargon note

What just happened?

Explore another notebook

What just happened?

Skilling up

Machine Learning and User Experience (UX)

Other courses

Take my tips with a grain of salt

Supplement: Learning Pandas well

Some communities to know about!

Getting Help: Questions, Answers, Chats

Take my tips with a grain of salt

Some communities to know about!

Deep Learning

Easier sharing of deep learning models and demos

Collaborate with Domain Experts

Easier sharing of deep learning models and demos

Machine Learning and User Experience (UX)

More Data Science materials

Aside: Bayesian Statistics and Machine Learning

More ways to "Dive into Machine Learning"

Aside: Bayesian Statistics and Machine Learning