Recommendation Systems This is a workshop on using Machine Learning and Deep Learning Techniques to build Recommendation Systesm Theory: ML & DL Formulation, Prediction vs. Ranking, Similiarity, Biased vs. Unbiased Paradigms: Content-based, Collaborative filtering, Knowledge-based, Hybrid and Ensembles Data: Tabular, Images, Text (Sequences) Models: (Deep) Matrix Factorisation, Auto-Encoders, Wide & Deep, Rank-Learning, Sequence Modelling Methods: Explicit vs. implicit feedback, User-Item matrix, Embeddings, Convolution, Recurrent, Domain Signals: location, time, context, social, Process: Setup, Encode & Embed, Design, Train & Select, Serve & Scale, Measure, Test & Improve Tools: python-data-stack: numpy, pandas, scikit-learn, keras, spacy, implicit, lightfm Notes & Slides Basics: Deep Learning AI Conference 2019: WhiteBoard Notes | In-Class Notebooks Notebooks Movies - Movielens 01-Acquire 02-Augment 03-Refine 04-Transform 05-Evaluation 06-Model-Baseline 07-Feature-extractor 08-Model-Matrix-Factorization 09-Model-Matrix-Factorization-with-Bias 10-Model-MF-NNMF 11-Model-Deep-Matrix-Factorization 12-Model-Neural-Collaborative-Filtering 13-Model-Implicit-Matrix-Factorization 14-Features-Image 15-Features-NLP Ecommerce - YooChoose 01-Data-Preparation 02-Models News - Hackernews Product - Groceries Python Libraries Deep Recommender Libraries Tensorrec - Built on Tensorflow Spotlight - Built on PyTorch TFranking - Built on TensorFlow (Learning to Rank) Matrix Factorisation Based Libraries Implicit - Implicit Matrix Factorisation QMF - Implicit Matrix Factorisation Lightfm - For Hybrid Recommedations Surprise - Scikit-learn type api for traditional alogrithms Similarity Search Libraries Annoy - Approximate Nearest Neighbour NMSLib - kNN methods FAISS - Similarity search and clustering Learning Resources Reference Slides Deep Learning in RecSys by Balázs Hidasi Lessons from Industry RecSys by Xavier Amatriain Architecting Recommendation Systems by James Kirk Recommendation Systems Overview by Raimon and Basilico Benchmarks MovieLens Benchmarks for Traditional Setup Microsoft Tutorial on Recommendation System at KDD 2019 Algorithms & Approaches Collaborative Filtering for Implicit Feedback Datasets Bayesian Personalised Ranking for Implicit Data Logistic Matrix Factorisation Neural Network Matrix Factorisation Neural Collaborative Filtering Variational Autoencoders for Collaborative Filtering Evaluations Evaluating Recommendation Systems

This repository documents my complete journey of learning and implementing Large Language Models (LLMs) using Python. The project starts from Python basics and gradually moves towards Machine Learning, Deep Learning, NLP, Transformers, and real-world LLM applications. The goal of this project is to build a strong foundation in AI/LLMs a

The ultimate objective of Natural Language Processing (NLP) is to let machines understand, and make sense of the human languages for valuable business applications. One field where NLP has excelled is Digital Marketing where it has shown interesting results especially with Recommender Systems. In this workshop, we will begin by understanding the basics of NLP then through case studies, we will dive into training different NLP models in order to make relevant recommendations for perfume products in a chatbot interface. This application is based on an open source dataset and will result in helping future customers choose from the top-5 extracted products.

A complete introduction to foundational NLP techniques using probabilistic models and Python libraries. The project explores text classification with Naive Bayes, sequence modeling with Hidden Markov Models, and Markov Chain-based predictions.

Text analysis with NLP Tool kit basics and Preprocessing the text using Tensorflow built-in models

Hands-on implementation of NLP fundamentals and Hugging Face models covering Word2Vec, ANN basics, and fine-tuning transformer models using real notebooks.

Basics of NLP, Text Classification, Sentiment Analysis, NER, Text Similarity and Topic Modeling using LDA, Guided LDA, HDP and NMF models

Includes a challenge for each python basics, pandas, matplotlib visualization and NLP model. Bootcamp was offered by Dphi a data science community.

This Repository gives basics info about NLP:Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) and linguistics that focuses on the interaction between computers and human language. It involves developing algorithms and models to enable computers to understand, interpret, and generate human language.

A step-by-step guide to mastering machine learning, from Python basics to advanced deep learning, NLP, and MLOps. Learn key tools like NumPy, Pandas, Scikit-Learn, TensorFlow, and AWS to build, deploy, and maintain ML models effectively.

Welcome to the NLP Repository! This repository is dedicated to showcasing various projects, models, and resources related to Natural Language Processing (NLP). Whether you're a beginner looking to understand the basics or an advanced user aiming to explore state-of-the-art techniques, this repository has something for everyone.

This repo contains all my Deep Learning semester work, including implementations of FNNs, CNNs, autoencoders, CBOW, and transfer learning. I explored TensorFlow, Keras, PyTorch, and Theano while practicing image classification, anomaly detection, NLP basics, and model evaluation.

Explore sentiment analysis on the IMDB movie reviews dataset using Python. This Jupyter Notebook showcases text preprocessing, TF-IDF feature extraction, and model training (Multinomial Naive Bayes, Random Forest) for sentiment classification. Ideal for understanding NLP basics and applying ML to textual data.

A simple Hugging Face Space that demonstrates prompt engineering basics using a user-friendly interface. Built to test and explore prompt design strategies with a generative AI model.My projects focus on:✅Foundational LLMs, Prompt Engineering, NLP, Text Generation, Structured Output Evaluation, and Chain-of-Thought Reasoning

A large amount of data that is generated today is unstructured, which requires processing to generate insights. Some examples of unstructured data are news articles, posts on social media, and search history. The process of analyzing natural language and making sense out of it falls under the field of Natural Language Processing (NLP). Sentiment analysis is a common NLP task, which involves classifying texts or parts of texts into a pre-defined sentiment. You will use the Natural Language Toolkit (NLTK), a commonly used NLP library in Python, to analyze textual data. In this tutorial, you will prepare a dataset of sample tweets from the NLTK package for NLP with different data cleaning methods. Once the dataset is ready for processing, you will train a model on pre-classified tweets and use the model to classify the sample tweets into negative and positives sentiments. This article assumes that you are familiar with the basics of Python (see our How To Code in Python 3 series), primarily the use of data structures, classes, and methods. The tutorial assumes that you have no background in NLP and nltk, although some knowledge on it is an added advantage.

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Sentiment analysis models using NLP and other important basics of NLP and subwords and a song lyric generator!

My journey of exploring NLP basics, models, and real-world text processing projects.

basics of nlp including data cleaning, feature engineering and model training

This repository covers all the LLM models overview and their implementation code to cover the basics of NLP, LLM and Generative AI

This is a NLP project where I learned the basics of LDA and applying LDA for Topic Modeling

This repository contains my projects and learning materials from the Workshop on NLP Basics. It documents my journey from Python fundamentals to text preprocessing, data analysis, and introductory ML models for NLP, serving as the foundation of my NLP and ML portfolio.

This repository contain my learnings on Generative AI references and its implementations right from the basics that is, from basic NLP to the models available.

🗣️ Natural Language Processing (NLP) – Basics to Mid-Level This repository contains Python code and examples for learning and implementing Natural Language Processing (NLP) concepts, from text preprocessing to building machine learning models for text data.

NLP_Basics_Tutorial_Examples is a dynamic tutorial folder housing 23 notebooks covering a spectrum of NLP fundamentals, from tokenization and sentiment analysis to advanced topics like transformers and sequence modeling.

This repository includes all the basics to advanced level of details related to NLP models, Deep Learning models, Fine Tuning of LLMs, GenAI apps in AWS and Deploying end-to-end projects

Complete LLM (Large Language Model) Learning Roadmap with Examples and Slides – in Telugu. Includes NLP basics, Transformer architecture, Hugging Face, LangChain, and project ideas.

Machine Learning, Deep Learning & NLP portfolio showcasing Python programming, data analysis, ML/DL models, NLP techniques, and end-to-end projects with deployment on AWS. Includes Python basics, advanced ML algorithms, deep learning architectures, NLP pipelines, and real-world projects demonstrating full-stack data science skills.

This repository documents my journey in learning Natural Language Processing (NLP) basics, focusing on essential concepts such as tokenization, word embeddings, Word2Vec, and Skipgram models. It serves as a beginner-friendly introduction to fundamental techniques in NLP.