Practical Introduction to Natural Language Processing

Course
57 Lessons

Transform yourself from a Python Developer to an NLP Data Scientist with practical projects.

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Who is this course for?

New Grads

Recent graduates or about to graduate students, who want to start their career as an NLP Data Scientist.

Experienced Working Professionals

Seasoned working professionals who want to transition into NLP or solidify their existing NLP knowledge and keep up with the latest NLP trends.

AI Enthusiasts

AI enthusiasts who want to dabble with NLP and explore the potential for themselves.

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Course Roadmap

From TF-IDF -> Sentence Tranformers -> GPT-3 -> deployment, you will learn it all and use it in practical projects.

Course Curriculum

Introduction

01 Course and Instructor Introduction.mp4

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Slack channel Invite

A note on subtitles for the course!

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02 Course Curriculum.mp4

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03 Introduction to NLP and its terms.mp4

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Course Code and Resources Link

Module 1.1: Dataset creation

04 Methods for Dataset Collection.mp4

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05 Parse Wikipedia movie titles and links using Beautifulsoup Library.mp4

06 Parse movie plot from a movie's Wiki page.mp4

07 Combine and collect all American movie plots.mp4

08 Collect Dataset with no-code tools - Parsehub.mp4

09 Collect novel Datasets with GPT-3.mp4

10 Install Github Desktop and create Github Repository.mp4

11 Deploy Dataset Visualizer on Streamlit Cloud for free.mp4

12 Understanding the Streamlit Code for Dataset Visualization.mp4

Module 1.2: TF-IDF algorithm and applications

13 Text to Vector and TF-IDF Introduction.mp4

14 Code - Tokenization of text.mp4

15 Code - Get term frequency of words in a movie plot.mp4

16 Code - Get document frequency and calculate TF-IDF of a movie plot.mp4

17 Code - Calculate TF-IDF vector using Sklearn Library.mp4

18 Code - TF-IDF Applications.mp4

19 - Add TF-IDF to the moviepro.ai Streamlit App.mp4

Project 1: Use N-grams to find the most diverse paraphrase sentence

20 Project 1 Problem - Sort paraphrases by their diversity using N-grams.mp4

21 Project 1 Solution - Sort paraphrases by their diversity using N-grams.mp4

Module 2: Data Visualization, Word Vectors and Sentence Transformers

22 Evolution of word vectors Part 1- TFIDF and Word2vec.mp4

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23 Evolution of word vectors Part 2- Contextual embeddings and Sentence Transformers.mp4

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24 Theory - Localization using NER and Word Vectors.mp4

25 Code - Localization using NER and Word Vectors.mp4

26 Theory - Data Visualization and Dimensionality Reduction.mp4

27 Code - Data Visualization and Dimensionality Reduction.mp4

Module 3: Keyword extraction, Similarity Search and Topic Modeling

28 - Theory - Keyword extraction with Sentence Transformers and diversity with MMR and Max Sum Similarity.mp4

29 - Code - Keyword extraction with Sentence Transformers and diversity with MMR and Max Sum Similarity.mp4

30 - Adding Sentence Transformers to Streamlit App.mp4

31 Theory - Topic Modeling using Sentence Transformers.mp4

32 Code - Topic Modeling using Sentence Transformers.mp4

Module 4: GPT-3, Production API Deployment and Full-stack App

33 Build an AI SaaS with GPT-3.mp4

34 Introduction to GPT-3 - Theory.mp4

35 - Introduction to GPT-3 Playground.mp4

36 - Understanding GPT-3 Parameters.mp4

37 - Create new paraphrase pairs dataset with GPT-3.mp4

38 - Build a paraphraser GPT-3 playground.mp4

39 - Sentence paraphraser using GPT-3 in code.mp4

40 - Paraphrase multiple sentences in parallel using GPT-3.mp4

41 - Introduction to ML Deployment.mp4

42 - Install AWS CLI and AWS SAM CLI.mp4

43 - Create Sentence Paraphraser API on AWS.mp4

44 - Setup text Paraphraser for AWS Lambda container deployment.mp4

45 - Deploy text paraphraser API on AWS Lambda Container Image.mp4

46 - Deploy Question Answering with Provisioned concurrency on Lambda.mp4

47 - Limitations of Streamlit and need for Bubble.io.mp4

48 - Nocode tool capabilties.mp4

49 - Introduction to Bubble Editor.mp4

50 - Input Output Textboxes and Buttons with Bubble.io.mp4

51 - API connector using Bubble.io.mp4

52 - Add Login and Signup Functionality using Bubble.io.mp4

53- Make database changes and implement fixed runs with Bubble.io.mp4

54 - A Guide to JSON output with LLM prompts.mp4

Testimonials

A few words from the course takers!

"Ramsri inspires with his NLP courses to go above and beyond what is taught in the course. I got the confidence to build full-scale products along with production deployment with his NLP courses."

Lavanya Gupta

Grad @ CMU

"This is a perfect course if you are looking for inspiration and a skillet to build a state-of-the-art NLP product, something I was looking out for and it fit me just right! Thanks to Ramsri for the infectious BIG thoughts he dared the class to think and execute."

Divya Venkatraman

Senior Data Scientist, Phd

"Ramsri teaches in a very practical way, taking time to explain the NLP concepts. After taking his course, I was confident to go ahead and build my own project because his teachings inspired new ideas."

Olaifa Julius 'Tunde

Data Science Learner

"The depth and pace of the course were perfect. Also thanks for your patience all along for a beginner like me! And got to interact with a great bunch.. 10/10 👍🏼"

Vatsal Parikh

Director, Flow XVA

Meet your instructor

Hi, I am Ramsri 👋

Welcome to LearnNLP academy!

I started learnlp.academy with a vision to teach practical NLP and foster a real-world application mindset among NLP learners.

About me:
I am Ramsri, based out of Hyderabad, India.
I am currently building two revenue-making NLP SaaS apps Questgen.ai and Supermeme.ai.

Questgen is an edtech startup to generate school quizzes like MCQs etc from any text using AI. Supermeme is a meme marketing tool for individuals and brands to generate original memes with AI.

Prior to this, I have 10+ yrs of work and startup experience across the USA, Singapore, and India. I am also an avid content creator and have built an audience of 50k+ across various social media platforms.

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FAQs

What are the pre-requisites for the course?

Good working knowledge of Python and data structures like lists and dictionaries is expected.

What is the refund policy?

If you are unhappy with the course for any reason, a request for a refund in the first 14 days will be honored.

Is the course only for beginners?

No. There is something for everyone.
Whether you are a beginner or an experienced NLP developer, you will have something new to learn from the course.