Core ML models

Is anyone playing with CoreML models?

I'm interested in using text analysis. In the Claris examples they have a sentiment analysis model. I want to investigate nearest neighbours, misspelled words, and so on.

Not yet, but clearly a topic of interest.

I would seriously recommend you look at Python for ML. Using Pandas for data cleanup chores (a lot usually needed) and scikit-learn for ML leaves other approaches far behind.

Check out the book "Hands on machine learning...."., second edition.

Nearest neighbors is probably one of the very easiest (and often less useful) ML models to start with.

There are also free easy-to-use sentiment analysis libraries out there though they all have some issues. One of the models, for example, can't handle misspelled words. LOL.

Jump in!


really exciting topic, I lately did some research on the Apple develoment site, but all ML related items were on interpreting images which for me is of minor interest. What I'm looking for is more like clustering text by patterns or another one would be getting the OCR results from invoices put into tables... Things can be done by online services but I thought maybe the M1 architecture plus ML-libraries could bring this onsite. Maybe I am a bit early to this :slight_smile:


Check out the book I mentioned above on Amazon and see if what you want to do hasn't been around for quite a while... :slight_smile:

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thanks for the reference @OliverBarrett. I'll dig in and have a look. I just noticed that there is a bunch of stuff on GitHub associated with the book. GitHub - ageron/handson-ml2: A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.


You are so welcome! It's really an excellent book with TONS of resources, as you found. :slight_smile:
The book suggests setting up a virtual environment for each project so you keep libraries and such separate.
Regarding "Pandas"...this is such a powerful library (I use it for linear algebra - ML related stuff often), I can also recommend "Python for Data Analysis". This book has so many excellent examples.
Drop a note and let me know how it's going.


Machine Learning and “Apple, you have some ’splaining to do!”

A hard-hitting but insightful video interview by Joanna Stern from The Wall Street Journal of Apple’s software chief Craig Federighi regarding Apple’s iPhone child protection features and methods.

Link to WSJ video interview

Federighi tries, Stern actually delivers.