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Machine Learning Crash Course

developers.google.com

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Re: Machine Learning Crash Course

#31

Post by cjalmeida »

kmax12 wrote: post #13

Great to see they have a nice introductory section to feature engineering! Feature engineering is often the most impactful thing you can do to improve quality of models and a place where I often see beginners (and experts for that matter) get stuck. Google walks through how to work with json files and categorical variables https://developers.google.com/machine-learning/crash-course/... . If anyone is looking to get m…

OTOH, automating feature engineering is a thing. There are papers on using unsupervised methods to do that.

The 1st place in Kaggle's Porto Seguro competition trained an Autoencoder on raw data to extract features.

Re: Machine Learning Crash Course

#32

Post by Abundnce10 »

I have a new project at work: I need to take in a free form text of recipe ingredients (e.g. "1/2 cup diced onions", "two potatoes, cut into 1-inch cubes", etc.) and build a program that identifies the ingredient (e.g. onion, potato), as well as the quantity (e.g. 0.5 cup, 2.0 units). Would machine learning be an applicable approach to solving this? Right now I'm just planning on using an NLP library to parse out the various parts of the ingredient text.

Re: Machine Learning Crash Course

#33

Post by eanzenberg »

3pt14159 wrote: post #23
minimaxir wrote: post #12

Looking through the topics covered, the standard AI-course caveats ( https://news.ycombinator.com/item?id=16247629 ) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about…

Unless you work for a company obviously known for their ML the "expertise" out there right now is brutal. People are building recommendation engines without knowing the very, very, very basics like Jaccard indexes, ROC Curves, or topic drift. I've even had to explain type two error to someone working on one of these before. I agree with your general thrust, and you're right, messy data is often 95% of the problem, bu…

You probably can't communicate effectively. If you are describing "Type two error" of course you will get eyes glossing over. A huge problem with research fields is their terse banal labels. Confusion matrix anyone?

Re: Machine Learning Crash Course

#34

Post by eanzenberg »

foobiekr wrote: post #25
minimaxir wrote: post #12

Looking through the topics covered, the standard AI-course caveats ( https://news.ycombinator.com/item?id=16247629 ) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about…

Gate keeping is only obsolete when it ceases to have impact. The reality right now is that ML is extremely hard to enter even for a very knowledgeable and deeply experienced but non-credentialed (by degree) person. It will be interesting to see how the situation evolves but my own observations are that people trying to enter the space might be better off getting a quickie masters if they can afford the time or cost t…

Even people getting a quickie masters is hit/miss in my experience. At the end of the day, successful machine learning engineers require a whole suit of different skills, both technical, communicative, and even life skills that don't really exist for software devs. Not all those can be taught in 3 months, 2 years or even 6 years.

Re: Machine Learning Crash Course

#35

Post by eanzenberg »

solarkraft wrote: post #30
acconrad wrote: post #18

Earlier quoted context omitted.

> The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about the industry. So what are they hoping to achieve with this course? I'm genuinely asking because part of me wants to take the course, but another part of me feels like what's the point if, even through many additional courses to build up a skill set, Go…

I have a feeling that they're trying to get people familiar with TensorFlow and thus very compatible with their cloud computing services.

I dunno.. for this level of ml scikit/numpy is way more accessible than tensorflow.

Re: Machine Learning Crash Course

#36

Post by eorge_g »

austenallred wrote: post #26

Shameless plug: Lambda School (YC S17) is also putting on a free Machine Learning crash course (we call it a mini bootcamp), followed by an optional 6-12 month course that you pay for once you get a job in data science (it’s free until then, and always free if you don’t get a job in ML). https://lambdaschool.com/machine-learning-bootcamp/

is there a more fleshed out outline for what will be covered here? sounds interesting

Re: Machine Learning Crash Course

#37

Post by minimaxir »

eanzenberg wrote: post #33
3pt14159 wrote: post #23

Earlier quoted context omitted.

Unless you work for a company obviously known for their ML the "expertise" out there right now is brutal. People are building recommendation engines without knowing the very, very, very basics like Jaccard indexes, ROC Curves, or topic drift. I've even had to explain type two error to someone working on one of these before. I agree with your general thrust, and you're right, messy data is often 95% of the problem, bu…

You probably can't communicate effectively. If you are describing "Type two error" of course you will get eyes glossing over. A huge problem with research fields is their terse banal labels. Confusion matrix anyone?

Granted, Type II error and confusion matrices are covered in more basic statistical classes, and are indeed important for hypothesis testing.

Re: Machine Learning Crash Course

#39

Post by maffydub »

3pt14159 wrote: post #23
minimaxir wrote: post #12

Looking through the topics covered, the standard AI-course caveats ( https://news.ycombinator.com/item?id=16247629 ) apply. Yes, AI/ML MOOCs teach the corresponding tools well, and the creation of new tools like Keras make the field much more accessible. The obsolete gatekeeping by the AI/ML elites who say "you can't use AI/ML unless you have a PhD/5 years research experience" is one of the things I really hate about…

Unless you work for a company obviously known for their ML the "expertise" out there right now is brutal. People are building recommendation engines without knowing the very, very, very basics like Jaccard indexes, ROC Curves, or topic drift. I've even had to explain type two error to someone working on one of these before. I agree with your general thrust, and you're right, messy data is often 95% of the problem, bu…

For anyone else who wondered what the Jaccard index is, it's also referred to as Intersection over Union.

...and if you haven't come across that either, see https://en.wikipedia.org/wiki/Jaccard_index for details.

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