Spark NLP for Data Scientists

Focused View

Ace Vo,David Talby,Jiri Dobes,Veysel Kocaman

12:50:09

49 View

1. Spark NLP Overview

1.1 John Snow Labs main website.html

1. Spark NLP for Data Scientists overview.mp4

03:53

2. Spark NLP Course Structure.mp4

04:00

2. Text Preprocessing - Text Normalization with Spell Checker

1.1 Blog post.html

1.2 Notebook.html

1. Context Spell Checker part 1.mp4

07:28

2.1 Blog post.html

2.2 Notebook.html

2. Context Spell Checker part 2.mp4

06:39

3.1 Blog post.html

3.2 Notebook.html

3. Context Spell Checker part 3.mp4

07:08

4.1 Blog post.html

4.2 Notebook.html

4. Context Spell Checker part 4.mp4

08:11

5.1 Blog Post.html

5.2 Notebook.html

5. NorvigSweeting Spellchecker.mp4

05:34

6.1 Blog Post.html

6.2 Notebook.html

6. SymmetricDelete Spellchecker.mp4

05:31

3. Text Processing - Extracting and Normalizing the Dates

1.1 Blog post.html

1.2 Notebook.html

1. Date Matcher.mp4

16:02

2.1 Blog Post.html

2.2 Notebook.html

2. MultiDateMatcher.mp4

16:01

4. Text Preprocessing - NGram Generation

1.1 Notebook.html

1. NGramGenerator.mp4

05:26

5. Text Preprocessing - Stemming and Lemmatizing

1.1 Notebook.html

1. Lemmatizer.mp4

07:50

2.1 Notebook.html

2. Stemmer.mp4

05:13

6. Text Preprocessing Models

1.1 Blog Post.html

1.2 Notebook.html

1. SentenceDetectorDL.mp4

09:38

2.1 Blog post.html

2.2 Notebook.html

2. Normalizer.mp4

07:45

3. StopWordsCleaner.mp4

08:44

7. Text cleaning with DocumentNormalizer

1.1 Blog Post.html

1.2 Notebook.html

1. DocumentNormalizer.mp4

10:05

8. Text Preprocessing - Text tokenization with Tokenizer

1.1 Notebook.html

1. Tokenizer.mp4

13:47

2.1 Extended examples of usage.html

2.2 Notebook.html

2.3 Regex Tokenizer Scala Docs.html

2.4 RegexTokenizer Documentation.html

2.5 RegexTokenizer Python Docs.html

2. RegexTokenizer.mp4

11:21

3.1 Notebook.html

3. ChunkTokenizer.mp4

12:40

4.1 Notebook.html

4. TokenAssembler.mp4

09:36

9. Information Extraction - Keyword Extraction

1.1 Academic Paper.html

1.2 Documentation.html

1.3 Extended examples of usage.html

1.4 Python Doc.html

1.5 Scala Doc.html

1. YAKE keyword extractor.mp4

11:46

10. Information Extraction with Regular Expression

1.1 Documentation.html

1.2 Extended examples of usage.html

1.3 Notebook.html

1.4 Python Doc.html

1.5 Scala Doc.html

1. RegexMatcher.mp4

13:24

11. Information Extraction with Text-based models

1.1 Blog Post.html

1.2 Notebook.html

1. TextMatcher and BigTextMatcher.mp4

12:44

12. Dependency Parsing and Coreference Resolution with DependecyParser

1.1 Blog Post.html

1.2 Dependency Parser.html

1.3 Documentation.html

1.4 Extended examples of usage.html

1.5 Notebook.html

1.6 Python Doc.html

1.7 Scala Doc.html

1. Dependency Parser.mp4

03:58

2.1 Blog Post.html

2.2 Notebook.html

2. POS Tagger.mp4

03:57

3.1 Notebook.html

3. Chunker.mp4

04:45

13. Information Extraction - Graph-based Information Extraction

1.1 Notebook.html

1. GraphExtraction.mp4

18:02

14. Coreference Resolution with SpanBertCoref

1.1 Blog Post.html

1.2 Notebook.html

1. SpanBertCoref.mp4

07:40

15. Text Representation with Embeddings and Vectorization - Word Embeddings

1.1 Blog Post.html

1.2 Notebook.html

1. Word2Vec.mp4

02:30

2.1 Blog Post.html

2.2 Notebook.html

2. WordEmbeddings part 1.mp4

13:40

3.1 Blog Post.html

3.2 Notebok.html

3. WordEmbeddings part 2.mp4

13:04

16. Text Representation with Embeddings and Vectorization - Sentence Embeddings

1.1 Doc2Vec Blogpost.html

1.2 doc2vec gigaword 300 model.html

1.3 doc2vec gigaword wiki 300 model.html

1.4 Notebook.html

1. Doc2Vec.mp4

13:40

2.1 Blog Post.html

2.2 Notebook.html

2. Chunk Embeddings.mp4

08:05

3.1 Blog Post.html

3.2 Notebook.html

3. SentenceEmbeddings.mp4

08:15

4.1 Notebook.html

4. UniversalSentenceEncoder.mp4

12:43

5.1 Another example of Word Embeddings.html

5.2 Notebook.html

5. Embeddings Finisher.mp4

06:12

17. Text Representation with Embeddings and Vectorization with Transformers

1.1 Extended example.html

1.2 Notebook.html

1.3 Python Documentation.html

1.4 Scala Documentation.html

1. Transformers-based Embeddings Part 1.mp4

13:56

2.1 Notebook.html

2. Transformers-based Embeddings Part 2.mp4

04:52

3. Embeddings Finisher.mp4

06:11

18. Sentiment Analysis

1.1 Blog Post.html

1.2 Notebook.html

1. ViveknSentiment.mp4

09:10

2.1 Blog Post.html

2.2 Notebook.html

2. SentimentDL.mp4

17:02

3.1 Blog Post.html

3.2 Notebook.html

3. SentimentDetector.mp4

08:11

19. Text Classification with Transformers

1.1 Documentation.html

1.2 Extended Example.html

1.3 Notebook.html

1.4 Python Documentation.html

1.5 Scala Documentation.html

1. Bert for Sequence Classification Part 1.mp4

09:53

2.1 Notebook.html

2. Bert for Sequence Classification Part 2.mp4

10:00

3.1 Notebook.html

3. Sentence Embeddings with Transformers Part 1.mp4

14:05

4.1 Notebook.html

4. Sentence Embeddings with Transformers Part 2.mp4

12:24

20. Text Classification - Training Text Classification models with ClassifierDL

1.1 Blog Post.html

1.2 Notebook.html

1. ClassifierDLApproach.mp4

19:17

2.1 Blog Post.html

2.2 Notebook.html

2. ClassifierDLModel.mp4

07:14

3.1 Blog Post.html

3.2 Notebook.html

3. MultiClassifierDL.mp4

16:32

21. Named Entity Recognition (NER)- Rule-based NER with EntityRuler

1.1 Blog Post.html

1.2 Notebook.html

1. EntityRuler.mp4

20:44

22. Named Entity Recognition (NER) - NER with BiLSTM-CNN-Char architecture

1.1 Blog Post.html

1.2 Notebook.html

1. NerDLModel and NerConverter.mp4

11:21

2.1 Blog Post.html

2.2 Notebook.html

2. NerOverwriter.mp4

05:36

23. Named Entity Recognition (NER) - Visualizing the Named Entities

1.1 Blog Post.html

1.2 Notebook.html

1. NerVisualizer.mp4

06:23

24. Named Entity Recognition (NER) - Training custom NER models with NerDLApproach

1.1 Blog Post.html

1.2 Notebook.html

1. NerDLApproach.mp4

22:22

2.1 Blog Post.html

2.2 Notebook.html

2. TFNerDLGraphBuilder.mp4

04:35

3.1 Blog Post.html

3.2 Notebook.html

3. CoNLL Preparation for NER.mp4

04:49

25. Named Entity Recognition (NER) - Conditional Random Fields for NER with NerCrf

1.1 Blog Post.html

1.2 Notebook.html

1. NerCrf.mp4

09:25

26. Named Entity Recognition (NER) - NER with Bert for Token Classification

1.1 Bert for Token Classification Medium Post.html

1.2 Bert for Token Classification Notebook.html

1.3 Documentation.html

1.4 Extended Example.html

1.5 Python Documentation.html

1.6 Scala Documentation.html

1. Bert for Token Classification Part 1.mp4

17:22

2.1 Bert for Token Classification Notebook.html

2. Bert for Token Classification Part 2.mp4

04:23

27. Question Answering from Text with Transformers

1.1 Documentation.html

1.2 Extended examples of usage.html

1.3 Notebook.html

1.4 Sclala Doc.html

1. Question Answering with Transformers.mp4

08:31

2.1 Blog Post.html

2.2 Notebook.html

2. MultiDocumentAssembler.mp4

18:10

28. Question Answering from Tables with TAPAS

1. Tapas For Question Answering.mp4

13:01

29. Multilingual NLP - Word segmentation with WordSegmenter

1.1 Blog Post.html

1.2 Notebook.html

1. WordSegmenter.mp4

13:01

30. Multilingual NLP - Multilingual machine translation with MarianTransformer

1.1 Notebook.html

1. MarianTransformer.mp4

16:43

31. Multilingual NLP - Language Detection with LanguageDetectorDL

1.1 Blog Post.html

1.2 Notebook.html

1. LanguageDetectorDL.mp4

05:33

32. Advanced Topics - Image Classifiation with VIT

1.1 Documentation.html

1.2 Extended Examples of Usage.html

1.3 Notebook.html

1.4 Python Doc.html

1.5 Scala Doc.html

1. ImageAssembler.mp4

05:41

2.1 Notebook.html

2. ViTForImageClassification.mp4

07:50

33. Advanced Topics- Leveraging the T5 model for NLP (T5Transformer)

1.1 Notebook.html

1. T5Transformer.mp4

16:36

34. Advanced Topics - Speech-to-Text Conversion

1. Wav2VecForCTC Part 1.mp4

09:36

2. Wave2VecForCTC Part 2.mp4

07:22

35. Utility Tools & Annotators

1.1 Notebook.html

1. LightPipeline.mp4

08:13

2. Token2Chunk.mp4

02:28

3.1 Notebook.html

3. PretrainedPipeline.mp4

05:24

4.1 Notebook.html

4. DocumentAssembler.mp4

10:22

5.1 Notebook.html

5. Finisher.mp4

07:37

6.1 Notebook.html

6. Doc2Chunk.mp4

03:18

7.1 Notebook.html

7. Chunk2Doc.mp4

03:52

8.1 Notebook.html

8. GPT2Transformer.mp4

14:07

Description

Unlock your NLP power with Spark NLP, the most popular NLP library in enterprises

What You'll Learn?

Utilize 20,000+ State-of-the-Art NLP models in 200+ languages
Train & tune your own NLP models by leveraging the Spark NLP's pre-defined classifier architecture on your own datasets
Perform popular NLU tasks in one line of code - like generate texts, summarize texts, answer questions
Deploy models as API's with NLP Server, a Docker container that contains all Spark NLPs capabilities

Who is this for?

Data scientists who are looking to use Natural Language Processing at Scale

Data scientists looking to build custom natural language understanding applications

Data Analysts who want to apply about Natural Language Processing

What You Need to Know?

Hands-on understanding of Python is needed

Recommended: basic understanding of machine learning and natural language processing

Nice to have: basic understanding of Apache Spark

More details

Description
Welcome to the Spark NLPÂ forÂ Data Scientist course!
This course will walk you through building state-of-the-art natural language processing (NLP) solutions using John Snow Labsâ€™ open-source Spark NLP library. Our library consists of more than 20,000 pretrained models with 250 plus languages. This is a course for data scientists that will enable you to write and run live Python notebooks that cover the majority of the open-source libraryâ€™s functionality. This includes reusing, training, and combining models for NLP tasks like named entity recognition, text classification, spelling & grammar correction, question answering, knowledge extraction, sentiment analysis and more.
The course is divided into 11 sections:Â Text Processing, InformationÂ Extraction, Dependency Parsing, Text Representation withÂ Embeddings, Sentiment Analysis, TextÂ Classification, Named Entity Recognition, Question Answering, MultilingualÂ NLP, Advanced Topics such as Speech to text recognition, and Utility Tools &Annotators. In addition to video recordings with real code walkthroughs, we also provide sample notebooks to view and experiment. At the end of the cost, you will have an opportunity to take a certification, at no cost to you.

The course is also updated periodically to reflect the changes in our models.

Looking forward to seeing you in the class, from all of us in John Snow Labs.
Who this course is for:
Data scientists who are looking to use Natural Language Processing at Scale
Data scientists looking to build custom natural language understanding applications
Data Analysts who want to apply about Natural Language Processing

User Reviews

Rating

average 0

Total votes0

Focused display

We here at John Snow Labs are working to support natural language processing (NLP) tasks in the healthcare, legal, and finance industry. We provide the state-of-the-art algorithms for our clients so they can augment their work with the richness of text data. We offer NLP Summit every quarter, where we also host trainings on how to use our product and library packages. We hope to provide more accessible learning experience for everyone who are interested in NLP.

David Talby

Instructor's Courses

David Talby is the Chief Technology Officer at John Snow Labs, helping companies apply artificial intelligence to solve real-world problems in healthcare and life science. David is the creator of Spark NLP – the world’s most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK. David holds a Ph.D. in Computer Science and Master’s degrees in both Computer Science and Business Administration. He was named USA CTO of the Year by the Global 100 Awards and GameChangers Awards in 2022.

Jiri Dobes

Instructor's Courses

Jiri Dobes is the Head of Solutions at John Snow Labs. He has been leading the development of machine learning solutions in healthcare and other domains for the past five years. Jiri is a PMP-certified project manager.His previous experience includes delivering large projects in the power generation sector and consulting for the Boston Consulting Group and large pharma. Jiri holds a Ph.D. in mathematical modeling.

Veysel Kocaman

Instructor's Courses

Lead Data Scientist and ML Engineer having a decade long industry experience. Currently having my PhD in CS at Leiden University (NL) and holding an MS degree in Operations Research from Penn State University (USA).I have worked as CTO, Head of AI, Principal Data Scientist and various other titles so far and I have also provided hands-on consulting services in Machine Learning and AI, statistics, data science and operations research to the several start-ups and companies around the globe. In Leiden University, I give lectures in Big Data Architecture, Distributed Data Processing and Automated ML. Besides, I'm the instructor and course planner for "Intro to Python and Machine Learning Toolkit" and "NLP with Python: From Zero to Hero" at several online venues.I also speak at Data Science & AI events, conferences and workshops. So far, I have delivered more than a hundred talks at International as well as National Conferences, Meetups. Feel free to drop me a line if you want to invite me.

Udemy

View courses Udemy

Students take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.