Practical Introduction to Natural Language Processing
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Introduction

01 Course and Instructor Introduction.mp4
Slack channel Invite
A note on subtitles for the course!
02 Course Curriculum.mp4
03 Introduction to NLP and its terms.mp4
Course Code and Resources Link
Module 1.1: Dataset creation

04 Methods for Dataset Collection.mp4
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
23 Evolution of word vectors Part 2- Contextual embeddings and Sentence Transformers.mp4
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

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

2 Lessons