AI News, hangtwenty/dive-into-machine-learning

hangtwenty/dive-into-machine-learning

(You can learn by screencast instead.) Now, follow along with this brief exercise (10 minutes): An introduction to machine learning with scikit-learn.

encourage you to look at the scikit-learn homepage and spend about 5 minutes looking over the names of the strategies (Classification, Regression, etc.), and their applications.

The whole paper is packed with value, but I want to call out two points: When you work on a real Machine Learning problem, you should focus your efforts on your domain knowledge and data before optimizing your choice of algorithms.

It's helpful if you decide on a pet project to play around with, as you go, so you have a way to apply your knowledge.

(Machine Learning, Data Science, and related topics.) Start with the support forums and chats related to the course(s) you're taking.

(Please submit a Pull Request to add other useful cheat sheets.) I'm not repeating the materials mentioned above, but here are some other Data Science resources: From the 'Bayesian Machine Learning' overview on Metacademy: ...

Bayesian ideas have had a big impact in machine learning in the past 20 years or so because of the flexibility they provide in building structured models of real world phenomena.

Algorithmic advances and increasing computational resources have made it possible to fit rich, highly structured models which were previously considered intractable.

Here is the abstract of Machine Learning: The High-Interest Credit Card of Technical Debt: Machine learning offers a fantastically powerful toolkit for building complex systems quickly.

This paper argues that it is dangerous to think of these quick wins as coming for free.

Using the framework of technical debt, we note that it is remarkably easy to incur massive ongoing maintenance costs at the system level when applying machine learning.

The goal of this paper is highlight several machine learning specific risk factors and design patterns to be avoided or refactored where possible.

These include boundary erosion, entanglement, hidden feedback loops, undeclared consumers, data dependencies, changes in the external world, and a variety of system-level anti-patterns.

(Also please don't be evil.) This guide can't tell you how you'll know you've 'made it' into Machine Learning competence ...

Re-phrasing this, it fits with the scientific method: formulate a question (or problem statement), create a hypothesis, gather data, analyze the data, and communicate results.

(You should watch this video about the scientific method in data science, and/or read this article.) How can you come up with interesting questions?

This advice, to do practice studies and learn from peer review, is based on a conversation with Dr. Randal S.

think the best advice is to tell people to always present their methods clearly and to avoid over-interpreting their results.

Part of being an expert is knowing that there's rarely a clear answer, especially when you're working with real data.

As you repeat this process, your practice studies will become more scientific, interesting, and focused.

When I read the feedback on my Pull Requests, first I repeat to myself, 'I will not get defensive, I will not get defensive, I will not get defensive.'

Whenever you apply Machine Learning to solve a problem, you are going to be working in some specific problem domain.

If you aren’t aligned with a human need, you’re just going to build a very powerful system to address a very small—or perhaps nonexistent—problem.

Then, if you you know a coworker or friend who works in UX, take them out for coffee or lunch and pick their brain.

Pull requests welcome for all parts of this guide, including this section!) By no means will that make you an expert in UX, but maybe it'll help you know if/when to reach out for help (hint: almost always — UX is really tricky and you should work with experts whenever you can!).

If you want to explore this space more deeply, there is a lot of reading material in the below links: In early editions of this guide, there was no specific 'Deep Learning' section.

I also know that if you become an expert in traditional Machine Learning, you'll be capable of moving onto advanced subjects like Deep Learning, whether or not I've put that in this guide.

Even if 'buy' instead of 'build,' you may want to buy from vendors who use known good stacks.

If you are working with data-intensive applications at all, I'll recommend this book: Lastly, here are some other useful links regarding Big Data and ML.

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