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.

Visualization

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

8 mins
161 MB

Preview

Slack channel Invite

A note on subtitles for the course!

Preview

02 Course Curriculum.mp4

15 mins
279 MB

Preview

03 Introduction to NLP and its terms.mp4

26 mins
527 MB

Preview

Course Code and Resources Link

Module 1.1: Dataset creation

04 Methods for Dataset Collection.mp4

10 mins
248 MB

Preview

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

25 mins
730 MB

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

10 mins
287 MB

07 Combine and collect all American movie plots.mp4

9 mins
173 MB

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

13 mins
306 MB

09 Collect novel Datasets with GPT-3.mp4

8 mins
108 MB

10 Install Github Desktop and create Github Repository.mp4

7 mins
75.7 MB

11 Deploy Dataset Visualizer on Streamlit Cloud for free.mp4

11 mins
212 MB

12 Understanding the Streamlit Code for Dataset Visualization.mp4

8 mins
155 MB

Module 1.2: TF-IDF algorithm and applications

13 Text to Vector and TF-IDF Introduction.mp4

32 mins
576 MB

14 Code - Tokenization of text.mp4

14 mins
296 MB

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

19 mins
393 MB

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

15 mins
316 MB

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

12 mins
291 MB

18 Code - TF-IDF Applications.mp4

21 mins
567 MB

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

9 mins
247 MB

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

18 mins
425 MB

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

8 mins
174 MB

Module 2: Data Visualization, Word Vectors and Sentence Transformers

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

22 mins
374 MB

Preview

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

18 mins
386 MB

Preview

24 Theory - Localization using NER and Word Vectors.mp4

12 mins
245 MB

25 Code - Localization using NER and Word Vectors.mp4

18 mins
408 MB

26 Theory - Data Visualization and Dimensionality Reduction.mp4

16 mins
266 MB

27 Code - Data Visualization and Dimensionality Reduction.mp4

24 mins
577 MB

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

28 mins
832 MB

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

18 mins
418 MB

30 - Adding Sentence Transformers to Streamlit App.mp4

9 mins
245 MB

31 Theory - Topic Modeling using Sentence Transformers.mp4

28 mins
631 MB

32 Code - Topic Modeling using Sentence Transformers.mp4

21 mins
517 MB

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

33 Build an AI SaaS with GPT-3.mp4

2 mins
10.6 MB

34 Introduction to GPT-3 - Theory.mp4

7 mins
150 MB

35 - Introduction to GPT-3 Playground.mp4

5 mins
52.3 MB

36 - Understanding GPT-3 Parameters.mp4

21 mins
217 MB

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

23 mins
392 MB

38 - Build a paraphraser GPT-3 playground.mp4

6 mins
82 MB

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

7 mins
110 MB

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

8 mins
217 MB

41 - Introduction to ML Deployment.mp4

13 mins
192 MB

42 - Install AWS CLI and AWS SAM CLI.mp4

3 mins
37.2 MB

43 - Create Sentence Paraphraser API on AWS.mp4

24 mins
440 MB

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

10 mins
189 MB

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

10 mins
291 MB

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

27 mins
604 MB

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

8 mins
80.8 MB

48 - Nocode tool capabilties.mp4

6 mins
91.4 MB

49 - Introduction to Bubble Editor.mp4

8 mins
89.2 MB

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

13 mins
129 MB

51 - API connector using Bubble.io.mp4

12 mins
152 MB

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

26 mins
314 MB

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

11 mins
160 MB

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

22 mins
427 MB

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.