Natural Language Processing with spaCy & Python - Course for Beginners

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 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

🎉 Thanks to our Champion and Sponsor supporters:
👾 Wong Voon jinq
👾 hexploitation
👾 Katia Moran
👾 BlckPhantom
👾 Nick Raker
👾 Otis Morgan
👾 DeezMaster
👾 AppWrite

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[rublev]

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.

[wdonno]

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.

[mohammadyusuf]

I was searching for Spacy tutorials yesterday, and FCC uploaded it, thank you 💝. Interested in part 2.

[yugioh]

Best Helpline for those who really want to learn NLP with ease and for free, can't wait for part 2

[firdovsihasanzada]

50 minutes in and it is already the best practical explanation of how spaCy works.

[soumensarkar]

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!

[rakshittherakki]

Definitely interested in part2 of this course <3

[pabloneirotti]

Thank you so much. The best course on SpaCy I have founded. Please make Part Two! We are waiting for it!

[TechTessellator]

Bangers one after another. This channel is a treasure.

[laurentius]

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.

[TT-cfxl]

You're a wizard, W.J.B. Mattingly! Sincerely yours, a stan

[varunnayyar]

this is absurd, opened yt for NLP videos and it was uploaded 1 sec ago.

[qutluq]

This video lesson was great. Looking forward to see the second part.

[tthtlc]

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.

[NickWindham]

Your tutorials and your YouTube channel are great. Thanks so much for sharing your knoledge online. So helpful and well made.

[roman]

I'm definitely interested in the ML aspects of spaCy) Thank you very much for the video!

[lixx]

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!

[thesupermachak]

Clicked this video by accident but got hypnotised by the shirt and now I'm learning Python.

[asmitadhikari]

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.

[stephenbradley]

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:

= .65
= .58
burgers"))) = .70
print(nlp2("french = .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.