Natural Language Processing with spaCy & Python - Course for Beginners
ฝัง
- เผยแพร่เมื่อ 23 เม.ย. 2024
- In this spaCy tutorial, you will learn all about natural language processing and how to apply it to real-world problems using the Python spaCy library.
💻 Course website with code: spacy.pythonhumanities.com/
✏️ Course developed by Dr. William Mattingly. Check out his channel: / pythontutorialsfordigi...
⭐️ Course Contents ⭐️
⌨️ (0:00:00) Course Introduction
⌨️ (0:03:56) Intro to NLP
⌨️ (0:11:53) How to Install spaCy
⌨️ (0:17:33) SpaCy Containers
⌨️ (0:21:36) Linguistic Annotations
⌨️ (0:45:03) Named Entity Recognition
⌨️ (0:50:08) Word Vectors
⌨️ (1:05:22) Pipelines
⌨️ (1:16:44) EntityRuler
⌨️ (1:35:44) Matcher
⌨️ (2:09:38) Custom Components
⌨️ (2:16:46) RegEx (Basics)
⌨️ (2:19:59) RegEx (Multi-Word Tokens)
⌨️ (2:38:23) Applied SpaCy Financial NER
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👾 Nick Raker
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i found this to be an excellent tutorial - very clear, great examples and thorough. thank you for sharing this and i look forward to seeing you continue with another covering machine learning in spacy.
I’ve come back to this video several times. The ONLY tutorial I’ve seen which walks through the whole process . The Python Tutorials for the digital humanities videos are also great. I am focused on biomedical text, but text is text when you are trying to get started.
Best Helpline for those who really want to learn NLP with ease and for free , can't wait for part 2
Awesome content there Dr. William. I was really hyped during the series and every aspects of spaCy you've described perfectly. Now I'm interested on ML aspect of spaCY and It'd be great if you come with ML aspect of spaCy.
Waiting for the second part ! This tutorial is perfect , thank you so much !
Thank you so much. The best course on SpaCy I have founded. Please make Part Two! We are waiting for it!
Yes! Please, definitely make a second part. I teach in the Humanities (college literature and creative writing classes), and I'm actively searching for tools I can use for creative experiments with texts.
Check out Microsft Power Automate AI Builder
@@NickWindham hey is the second part out yet?
hey, I`m not getting the expected output at 1:57:26. It`s showing KeyError: 0. Can you help me with that?
Where do you teach?
Thank you very much for making this video. I want to create my own corpus to analyze data. But as a newbie to Python, I found it really hard to start without a clear direction. Looking forward to Part 2!
This is a great NLP tutorial. I have checked out a few others but this one here takes the cake. Thanks for the excellent resource!
Excellent tutorial, thank you :) Looking forward to the second part!
This video lesson was great. Looking forward to see the second part.
Nice to see the connection of the real world and code because of NLP. Great to see real life implementations that are beneficial to humanity.
Definitely interested in part2 of this course
Bangers one after another. This channel is a treasure.
Your tutorials and your TH-cam channel are great. Thanks so much for sharing your knoledge online. So helpful and well made.
50 minutes in and it is already the best practical explanation of how spaCy works.
Thank you Dr William for taking me through such wonderful journey on NLP - it was my first learning on this area of python application and i found it quite useful and excited to do some more. Looking forward to having your part 2 soon!
where can I find text book
Thank you Dr. William. Looking forward for a part two.
Excellent tutorial. Straight into the subject. Hats off to you !!
Thank you such amazing content. This was so easy to follow and understand. Please do a 2nd part to the tutorial!! 🙏
I was searching for Spacy tutorials yesterday, and FCC uploaded it, thank you 💝. Interested in part 2.
Hi, can you tell where can i find the repository for the data?
I'm definitely interested in the ML aspects of spaCy) Thank you very much for the video!
This video is fantastic! I would really appreciate part 2
Outstanding overview of Spacy, can't wait for part 2! Thank you so much.
is part 2 out?
Great video. Please make the second part ASAP. Keep up the good work.
Thanks for sharing this enlighting stuff about the spaCy framework! Is there a date when you're gonna publish part 2? Can't wait to learn about the machine learning applications of spaCy.
You're a wizard, W.J.B. Mattingly! Sincerely yours, a stan
Thank you for the explanations. They are very clear and relevant. Excellent video.
Very much interested in the machine learning aspect of SpaCy. Thank you, this course was informative and handy.
I’ve been watching Dr. Mattingly’s other videos and they’re great.
This is super awesome tutorial. Just what I need. Thanks!
Really fascinating and accessible. Thank you.
Very very helpful stuff! 31 minutes in the video and I'm already using spacy for my own analyses! Thank you so much!
just finished 1/3 and I have to say very good introduction. thanks a lot on the sharing
Hi and thank you very much for your tutorial. I really enjoyed it and looking forward to the second part of the tutorial
Thank you! interested in part 2.
I can't believe such good content is for free, thank you.
That was a very nice explanation and an awesome tutorial. Waiting for the machine learning part.
Thanks for your awesome introduction :). Would love to have your next course on using spaCy for ML.
This video is very useful for me. Thanks for always bringing the great video. Mad respect from me
Awesome Video! Can't wait for part 2
crossing my fingers 🤞🤞🤞
Thanks for this incredible class and textbook, it was very helpful. Greetings from Brazil
Superb, Waiting for part 2 with thanks🙏👍
Great work! Really a good video to learn using spaCy.
Thank you 🙏 , Interested in part 2
Great Tutorial. Learnt a lot about SpaCy fundamentals.
This is ready awesome teaching video. I feel highly interesting in the part two video.
Excelent!!! The best of the best!!!! Please do the second showing how to train the model.
Love the work you are doing. Many thanks from India
This is very helpful, thank you!
Please make the second video about machine learning! this was so helpful
Yes please for a part 2 on Machine Learning with Spacy!
very simple and easy to understand thank you for this
Thanks for the depth with this library sir
Thank you, very clear and forward tutorial. What is the name of the editor?
Thanks a lot, and please make the second part.
Greate tutorial, please make a second part!
just perfect! Thank you very much :)
Thank you so much! Such a wonderful video.
Enjoyed This video waiting for part2.
Definitely important to dig into the .similarity() output before using it in one's own work. One of its flaws is that it cares too much about the number of words in the spans being compared. For example:
print(nlp2("fries").similarity(nlp2("burgers"))) = .65
print(nlp2("fries").similarity(nlp2("hamburgers"))) = .58
print(nlp2("fries").similarity(nlp2("ham burgers"))) = .70
print(nlp2("french fries").similarity(nlp2("hamburgers"))) = .46
print(nlp2("french fries").similarity(nlp2("ham burgers"))) = .64
Also, I find that the small model correctly identifies West Chestertenfieldville as a GPE without modification, and I find that nlp.add_pipe("entity_ruler") does not add of the pipeline-description we see via nlp.analyze_pipes(). Rather, it seems that element of this description is in alphabetical order, and every nested sub-element is also in alphabetical order. I suspect this does not say anything about the true order of the pipeline.
Best Helpline for those who really want to learn
Such a nice video, 2nd part please!!
Everyone seemed to be asking for part 2, but this coverage is good enough - so good that I don't think it deserved a part 2, otherwise a large part is going to be lots of repetition. I will keep exploring deeper based on this video itself.
There are also a lot of other resources available (and free), if u have time to go through: course.spacy.io/en/
@@tthtlc thank you so much! do you have other resources like that?
Thanks Great course and I love how easy and smooth the explanation is. Moreover I like how explaining each step before diving into it is really making the understanding easier for us to follow thanks a lot. BTW I've spent some time looking for the github account and repo related to this video here is it if anyone needs it to begin following the video, ENJOY...
where?`
Thank you so Much. It is very helpful
eagerly waiting for the second part..please upload it soon...
Hi, can you please make a tutorial on spacy-llm? by considering few use cases which can be addressed with the spacy-llm, thanks.
Fantastic tutorial. Did part two ever get published?
great video we are looking for the second part please
Please create Part 2!!!!! Part one was 🔥🔥🔥🔥🔥🔥🔥
Eagerly waiting for the second part.
Where’s part 2!!! If there’s time in part 2, I would definitely be interested to know how to train ML to help with research and literature reviews as an example
Looking forward for Machine Learning aspects of Spacy.
Waiting for 2nd part sir 👌🙏
Also an historian looking for ways to extract info from old documents. very looking forward to the second part.
I am trying to have it out in early January.
@@python-programming really looking forward to the next video. One topic I have not seen you address is the question of tools for Annotation. When working in specialized language domains, extra training of models is a key step. As a newcomer, I have not yet found a process compatible with Spacy which is reasonably efficient. Prodigy?
super interesting , already quickly subscribe both of the channels (y)
Clicked this video by accident but got hypnotised by the shirt and now I'm learning Python.
Can't wait for Part 2
I would like the ML version too. So looking forward to seeing that
Thank you very much.
Question, sir: Why do we build an entity ruler if we can simply verify the presence of the company's name in a normal list of company names?
Is there a possibility to keep the nlp we built permanently stored and fill it with patterns consecutively over time? Or every time we call nlp.blank, the process must be repeated, and the names are fetched and stored?
Thank you ❤️
Let's do the second part of it 🙂
Is there anyway I can donate to your channel ? You are the reason I could excel in my projects and landed with the job. I would like to express gratitude.
(edit) : Okay, I found it out. My Bad.. I just did. Thanks again guys. Cheers.
Hello Sir, Great tutorial. Hows part 2 coming? Cheers
Thanks for the wonderful videos
this content is amazing
This is very helpful. I am very new to Machine Learning and NLP. I am in a situation where I have thousands of documents which don't always have correct spellings. I have to analyze these documents to look for trends related to parts failure especially if the failure has resulted in death or injury. Ideally like to learn from the data that can inform the future failures before there is a death or injury. Can SpaCy help with this?
Great tutorial! keep up the good work.
Can somebody explain tho why he's extracting the first sentence of the Alice JSON file at 1:57:40 by accessing [0][2][0]? I am not quite sure I get it :)
This tutorial is so freaking inspiring to me. NLP is so exciting and I'd love to integrate it with machine learning!!!!
I'd be 100% down to watch a tutorial with part 2!!!!!
Thanks! Good to know. I think I will start planning it this week.
@@python-programming Hi hi, any updates on part 2? I hope everything's ok :)
@@andrijor indeed! I am still working on it. Between the textbook and the video it takes a while to make. I am hoping to have it ready in early January.
@@python-programming Looking forward to it! 😊
Thank you so much dear
Please link the github repo, it is not linked here or in the book. Attempted to share but I think it was flagged as spam for having a link.
Great resource!
The interesting thing is, the models are still being improved by spacy, so some values or predictions come different or better at the time I am trying the notebooks out (06/05/2022).
Thank you for this fantastic video. Here's one question from vscode user. display.render(doc2, style='dep') has no reaction. I tried to find solutions on stack overflow but failed. Anyone can help? Many thanks.
Thank you for the great video!
When I run the most_similar method, copying the code on your notebook, I end up receiving a complete set of differemt words, some unrelated to the word and some in other languages. Example: country gave me ['country-0,467', 'nationâ\x80\x99s', 'countries-', 'continente', 'Carnations', 'pastille', 'бесплатно', 'Argents', 'Tywysogion', 'Teeters']
Can somebody help me understand why this is happening?
Same here. Curious but...maybe the transformers/models (not sure which) are retrained thus giving us a different set of words? Hopefully someone can answer this!
this video is so engaging...
Just getting started here. Have several months experience with Python and working with some team members who are implementing spaCy for our project. Question: my Python environment is VS rather than Jupyter (no criticism here, I just worked with VS a few decades ago). Can I proceed with this course in VS or must I install Jupyter. Windows 11 can get cranky if you ask it to do too much.
can anyone give me the link of the repo that is being used
Please do part2 I badly need this tutorial
Thanks for letting me know. I'll try it get it ready within the next few weeks.
@@python-programming thanks man .you are a life saver