abhie7 / advanced-python-nlp

To-Do Topics:

Part 1: Advanced Python Fundamentals

Python Basics Revision

• Data types
• Control flow (if-else, loops)
• Functions
• Modules and packages

Advanced Python Concepts

• OOPS
• Multithreading
  • Sychronizing threads
  • Semaphores
  • Events
  • Daemon
• Map functions
• Itertools
• Lambda functions
• Try/Except (Error Handling)
• Decorators
• Collections
• Generators
• Magic Methods
• Regular Expressions (Regex)
• Multiprocessing
• Data Classes
• Understanding the working of Python

Part 2: Introduction to Machine Learning and NLP

Machine Learning Fundamentals

• What is Machine Learning?
• Supervised, Unsupervised, and Reinforcement Learning
• Common Machine Learning Algorithms • PDF
  • Linear Regression
  • Logistic Regression
  • Decision Trees
  • Random Forests
  • Support Vector Machines (SVMs)
  • K-Nearest Neighbors
  • Naive Bayes
  • K-Means Clustering
  • Principal Component Analysis (PCA)
  • Gradient Boosting Machines

Transformers and Large Language Models (LLMs)

• Overview of NLP • Huggingface Documentation
• What is LLM and understanding the working of an LLM • Video
• Transformer Architecture • Short Video • Detailed Video • Huggingface Documentation
• Pre-training
• Fine-tuning

Natural Language Processing (NLP) Fundamentals

• What is NLP? • Video
• Text Preprocessing
  • Text Normalization
  • Text Tokenization
  • Stopword Removal
• Attention Mechanism • Huggingface Documentation
• Stemming • IBM Documentation
• Lemmatization • IBM Documentation
• Bag-of-Words and TF-IDF
• Word Embeddings
  • Word2Vec
  • GloVe

Part 3: NLP Applications and Tools

NLP Tools, Libraries and Frameworks

• PyTorch
• Hugging Face Transformers
• LlamaIndex
• Langchain
• Tokenization
• Vectors and Embeddings
• Faiss
• Pinecone
• NLTK
• spaCy

Advanced NLP Concepts

• Tokenization Techniques
  • Byte Encoder
  • Cross Encoder
• Retrieval Augmented Generation (RAG) • Short Video

NLP Applications

• Text Classification
• Named Entity Recognition
• Text Generation
• Sentiment Analysis
• Text Summarization
• Fine Tuning

Part 4: Deep Learning

Deep Learning Basics

• Neural Networks
  • Feedforward
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
• Activation Functions
• Backpropagation
• Optimization Techniques