← Generative AI & Large Language Models

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The complete curriculum

01 Introduction to Python-Data Types & Data Structures 7 lessons ▶

1. 1.1Machine Learning Intuition
2. 1.2Python Data Types
3. 1.3Data Types Practical
4. 1.4List in Python
5. 1.5Tuples in Python
6. 1.6Dictionary in Python
7. 1.7Sets in Python

02 Fundamentals of Python Programming 5 lessons ▶

1. 2.1Loops in Python
2. 2.2Comprehension in Python
3. 2.3Functions in Python
4. 2.4Lambda Functions in Python
5. 2.5Exception Handling in Python

03 Python Libraries-Numpy,Pandas,Matplotlib 8 lessons ▶

1. 3.1Numpy Array Creation
2. 3.2Numpy Operations
3. 3.3Numpy Bonus Operations
4. 3.4Pandas in Python
5. 3.5Working with CSV Files
6. 3.6Pandas Operations
7. 3.7Pandas Bonus Operations
8. 3.8Matplotlib in Python

04 Regular Expressions in Python 1 lesson ▶

1. 4.1Regular Expressions in Python

05 Introduction to NLP-Challenges & Applications 3 lessons ▶

1. 5.1NLP-Introduction, Challenges & Applications
2. 5.2Text Classification & Translation Pipeline
3. 5.3Text Clustering & Classification

06 Text Cleaning & Preprocessing using NLTK 10 lessons ▶

1. 6.1Introduction to NLTK
2. 6.2Tokenization
3. 6.3Stop Word Removal
4. 6.4Stemming and Lemmatization
5. 6.5Named Entity Recognition
6. 6.6Introduction to POS Tagging
7. 6.7POS Tagging Architecture
8. 6.8POS Tagging Approach & Applications
9. 6.9Rare Word Removal
10. 6.10Correcting Words using NLTK

07 Text Cleaning and Preprocessing using SpaCy 7 lessons ▶

1. 7.1Tokenization using SpaCy
2. 7.2POS Tagging using Spacy
3. 7.3Dependency Parsing using SpaCy
4. 7.4Lemmatization using SpaCy
5. 7.5NER using SpaCy
6. 7.6Vector Similarity in spaCy
7. 7.7NLTK vs spaCy

08 Machine Learning Crash Course 6 lessons ▶

1. 8.1Linear Regression
2. 8.2Logistic Regression
3. 8.3Support Vector Machine
4. 8.4K Nearest Neighbors Algorithm
5. 8.5Decision Trees
6. 8.6Random Forest

09 Text Representation Techniques in NLP 4 lessons ▶

1. 9.1Bag of Words Technique
2. 9.2TF-IDF
3. 9.3Word Embeddings
4. 9.4Sentence Embeddings

10 Working with Text Files 1 lesson ▶

1. 10.1Working with Text Files

11 Sentiment Analysis in NLP 9 lessons ▶

1. 11.1What is Sentiment Analysis
2. 11.2Challenges, Applications & Takeaways
3. 11.3How sentiment is detected
4. 11.4Sentiment Analysis Implementation
5. 11.5Text Preprocessing for Sentiment Analysis
6. 11.6Text Cleaning Code
7. 11.7Sentiment Analysis with machine Learning
8. 11.8ML for Sentiment Analysis Code
9. 11.9Training & Hyperparameter Tuning for Sentiment Analysis

12 ML Model Deployment using Streamlit 3 lessons ▶

1. 12.1What is Model Deployment
2. 12.2Introduction to streamlit
3. 12.3Model Deployment using Streamlit

13 Multi Label Text Classification 2 lessons ▶

1. 13.1Multi Label Classification
2. 13.2Multi Label Binarization

14 Sentiment Analysis using WordVec Embeddings 3 lessons ▶

1. 14.1Word2Vec Embeddings
2. 14.2Data Preparation for Sentiment Analysis using Word2Vec Embeddings
3. 14.3Data preprocessing for sentiment analysis using word2vec embeddings

15 Emotion Recognition in Text using GloVe 1 lesson ▶

1. 15.1Emotion Recognition in Text using GloVe

16 CV Parsing in NLP 2 lessons ▶

1. 16.1What is CV Parsing in NLP
2. 16.2Implementation of CV Parsing using Spacy3

17 Sentiment Analysis using Deep Learning 7 lessons ▶

1. 17.1What is Deep Learning
2. 17.2Machine Learning vs. Deep Learning
3. 17.3Sentiment Analysis using Deep Learning
4. 17.4Data Preparation for Sentiment Analysis using Deep Learning
5. 17.5Train Test Split for Sentiment Analysis Using Deep Learning
6. 17.6Artificial Neural Networks
7. 17.7Convolutional Neural Networks

18 Hate Speech Classification 4 lessons ▶

1. 18.1Introduction to Hate Speech Classification
2. 18.2Workflow of Hate Speech Classification
3. 18.3Implementation of Hate Speech Classification
4. 18.4Testing in Hate Speech Classification

19 Poetry Generation in NLP 3 lessons ▶

1. 19.1Understanding RNN and its types
2. 19.2Challenges of RNNs
3. 19.3Poetry Generation in NLP

20 Transformers 4 lessons ▶

1. 20.1Introduction to Transformers
2. 20.2Encoder Decoder Achitecture
3. 20.3Applications of Transformers
4. 20.4Pretrained Language Models - BERT & GPT

21 Prompt Engineering 1 lesson ▶

1. 21.1Prompt Engineering - Techniques & Examples

22 Multi-modal NLP 1 lesson ▶

1. 22.1Multi-modal NLP & Applications

23 Intro to Gen AI 1 lesson ▶

1. 23.1Introduction to GEN AI

24 Capstone Project 1 lesson ▶

1. 24.1Capstone Project Hands-On

Tools you'll use

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