Natural Language Processing For Text Analysis With spaCy

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Minerva Singh

2:42:28

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1 - Introduction To The Course

1 - Welcome to the Course.mp4

05:05

2 - Data and Code.html

3 - Python Installation.mp4

05:44

4 - Start With Google Colaboratory Environment.mp4

07:13

5 - Google Colabs and GPU.mp4

05:50

6 - Installing Packages In Google Colab.mp4

04:27

2 - Get Started with Natural Language Processing NLP With SpaCy

7 - What Is spaCy.mp4

03:07

8 - What Is a Doc Object.mp4

03:30

9 - Extracting Information From Unstructured Text Data.mp4

04:33

10 - Splitting and Cleaning Text.mp4

03:05

11 - SpaCy Language Models.mp4

03:06

12 - Stop Words.mp4

03:34

13 - Lemmitization.mp4

02:16

14 - Putting it all together in pipelines.mp4

03:55

15 - Adding Components to Pipelines.mp4

04:35

3 - RulesBased Matching For Information Extraction

16 - Token Matcher.mp4

03:07

17 - Phrase Matcher.mp4

03:26

18 - Detect Entities With Entity Ruler.mp4

02:01

19 - Lets Locate the Phone Numbers.mp4

02:43

20 - Regex Matchers.mp4

05:28

21 - Similarity Matching.mp4

07:34

4 - Word Vectors for Linguistic Information

22 - What Is Semantic Similarity.mp4

04:22

23 - Work with word vectors in spaCy.mp4

03:54

24 - Semantic Similarity With Entities.mp4

04:28

25 - Similarity Comparison With a Keyword.mp4

01:34

26 - Using ThirdParty Word Vectors.mp4

02:03

5 - Textual Interlinkages

27 - Concept behind textual interlinkages.mp4

06:48

28 - Visualise the dependency between entities.mp4

09:27

29 - Looking for specific dependencies.mp4

03:05

6 - Practical Case Studies

30 - Mining Financial Information Using POS Tagging.mp4

07:33

31 - Visualise the Entities.mp4

02:26

32 - Extract Organisation Names.mp4

03:43

7 - Some Python Data Science Concepts to Bear In Mind

33 - What Is Pandas.mp4

12:06

34 - Basic Data Cleaning With Pandas.mp4

04:30

35 - Principles of Data Visualisation.mp4

09:33

36 - Principal Component Analysis PCATheory.mp4

02:37

Description

Learn step-by-step Natural Language Processing (NLP) in Python using spCY! Work on practical NLP Projects!

What You'll Learn?

Understand the basic concepts of natural language processing, including: part-of-speech, lemmatization, stemming, named entity recognition, and stop words
Implement text summarisation and keyword search
Understand more advanced concepts, such as: dependency parsing, tokenization, word and sentence similarity
Implement text summarisation and keyword search

Who is this for?

Data Scientists who want to increase their knowledge in natural language processing

Students of Artificial Intelligence (AI)

People interested in learning real-world NLP aplications

More details

Description
Natural Language Processing (NLP) is a subfield of Artificial Intelligence (AI) to enable computers to comprehend spoken and written human language. NLP has several applications, including text-to-voice and speech-to-text conversion, chatbots, automatic question-and-answer systems (Q&A), automatic image description creation, and video subtitles.Â With the introduction of ChatGPT, NLP will become more and more popular, potentially leading to increased employment opportunities in this branch of AI. The SpaCy framework is the workhorse of the Python NLP ecosystem owing to (a) its ability to process large text datasets, (b) information extraction, (c) pre-processing text for subsequent use in AI models, and (d) Developing production-level NLP applications.
IFÂ YOUÂ AREÂ AÂ NEWCOMERÂ TOÂ NLP, ENROLL IN MY LATEST COURSE ON HOW TO LEARN ALL ABOUT NATURALÂ LANGUAGEÂ PROCESSING (NLP) ANDÂ TO DEVELOPÂ NLPÂ MODELSÂ USINGÂ SPACY
The course is divided into three main parts:

Section 1-2: The course will introduce you to the primary Python concepts you need to build NLP models, including getting started with Google Colab (an online Jupyter implementation which will save the fuss of installing packages on your computers). Then the course will introduce the basic concepts underpinning NLP and the spaCy framework. By this end, you will gain familiarity with NLP theory and the spaCy architecture.

Section 3-5: These sections will focus on the most basic natural language processing concepts, such as: part-of-speech, lemmatization, stemming, named entity recognition, stop words, dependency parsing, word and sentence similarity and tokenization and their spaCy implementations.

Section 6: You will work through some practical projects to use spaCy for real-world applications
An extra section covers some Python data science basics to help you.
Why Should You Take My Course?
MY COURSE IS A HANDS-ON TRAINING WITH REAL PYTHONÂ SOCIALÂ MEDIAÂ MINING-Â You will learn to carry out text analysis and natural language processing (NLP) to gain insights from unstructured text data, including tweets.
My course provides a foundation to conductÂ PRACTICAL, real-life social media mining. By taking this course, you are taking a significant step forward in your data science journey to become anÂ expert in harnessing the power of text for deriving insights and identifying trends.
I have an MPhil (Geography and Environment) from the University of Oxford, UK. I also completed a data science intense PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience analyzing real-life data from different sources, including text sources, producingÂ publications for international peer-reviewed journals and undertaking data science consultancy work. In addition to all the above, youâ€™ll haveÂ MY CONTINUOUS SUPPORTÂ to ensure you get the most value out of your investment!
ENROLL NOW :)

Who this course is for:
Data Scientists who want to increase their knowledge in natural language processing
Students of Artificial Intelligence (AI)
People interested in learning real-world NLP aplications

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I completed a PhD (University of Cambridge, UK) in 2017 where I focussed on implementing data science techniques for quantifying the impact of forest loss on tropical ecosystems. I hold an MPhil (School of Geography and Environment) and an MSc (Department of Engineering) from Oxford University. I have more than 10 year's experience in conducting academic research (published in high level peer-reviewed international scientific journals such as PLOS One) and advising both non-governmental and industry stakeholders in data science, deep learning and earth observation (EO) related topics. I have a strong track record in implementing machine learning, data visualization, spatial data analysis, deep learning and natural language processing tasks using both R and Python. In addition to being educated at the best universities in the world, I have honed my statistical and data analysis skills through many MOOCs, including The Analytics Edge (R based statistics and machine learning course offered by EdX), Statistical Learning (R-based Machine Learning course offered by Stanford online) and the IBM Data Science Professional certificate Track. I specialise in a variety of topics ranging from deep learning (Tensorflow, Keras) to machine learning to spatial data analysis (including EO data processing), data visualizations, natural language processing, financial analysis among others. I have acted as a peer reviewer on highly regarded academic journals such as Remote Sensing and given guest lectures on prestigious forums such as Open Data Science Conference (ODSC).

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