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

PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).

Generative AI Client for SAP HANA Cloud is an extension of the existing HANA ML Python client library, mainly focusing on GenAI and related use cases. It includes many leading-edge GenAI related open source libraries and provides seamless integration with HANA ML, HANA vector engine, and other SAP GenAI Hub SDK.

High-performance Python librarys for connecting AI/ML frameworks with OSS storage.

Nada-AI is a Python library designed for ML/AI on top of Nada DSL and Nillion Network.

An rclpy-compatible Python library — designed to solve the friction of using ROS 2 with Python ML/AI libraries.

🛠️ PyLibPro: A streamlined Python library installer for ML/AI development. Interactive CLI for managing TensorFlow, PyTorch, scikit-learn & more. Install by category or individually. Perfect for data science setup & teaching environments.

Part of the sai3 project that delivers multi-protocol storage access for AI/ML workflows, supporting Pytorch, Tensorflow and Jax. This project provides a CLI, along with Rust and Python libraries for AI/ML storage workflows. Supporting S3, File, Azure Blob and GCS using the latest Rust SDKs.

Chatter: a Python library for applying information theory and AI/ML models to animal communication

TraverseCraft is a versatile Python library for creating static, animated, and interactive visualizations, enabling robust simulations for AI, RL, and ML. Ideal for students, educators, and researchers, it allows real-time observation of algorithm behaviors with an intuitive interface.

AI-Aquatica is an open-source Python library for intelligent water quality analysis. It offers tools for data cleaning, imputation, ion balance verification, statistical analysis, ML modeling, visualization, and automatic reporting.

Created a Smart traffic light system to fight congestion by identifying the 2 lanes problems in India using Python and it's libraries like OpenCV, NumPy, collections etc. and put step forward in AI & ML Technology and showcased with help of IOT components like LED's and Arduino UNO.

A Snowflake-centric Enterprise AI/ML framework with tight integration of popular Python data science libraries, e.g., Pandas, Scikit-Learn, Tensorflow, Pytorch, MLFlow, etc. This project simplifies the process of integrating your company's Snowflake data with those popular libraries, making it easier to develop and deploy machine learning models.

OpenCITE (**C**ataloging **I**ntelligent **T**ools in the **E**nterprise) is a Python library and application designed to facilitate the discovery and management of AI/ML Assets (including tools, models, and infrastructure) across multiple cloud platforms and protocols.

# Python Developer Roadmap Folks, Here are 10 important things to deep-dive into Python Developer Role! Also, the items are listed in no particular order. You don't need to learn everything listed here, however knowing what you don't know is as important as knowing things. ## **1. Learn the basics** * Basic syntax * Variable and data types * Conditionals * List, Tuples, Sets, Dictionaries * Type Casting, Exception Handling * Functions, Buitlin functions ## **2. Advanced Core Python** * Object Oriented Programming(OOP) * Data Structures and Algorithms * Regular Expressions * Decorators * Lambdas * Modules * Iterators ## **3. Version Control Systems** * Basic Git Usage * Repo Hosting Services(GitHub, GitLab, BitBucket) ## **4. Package Managers** * PyPI * PIP ## **5. Learn Framework(Web Development)** - Synchronous Framework - Django, Flask, Pyramid - Asynchrnous Framework - Tornado, Sanic, aiohttp, gevent ## **6. Desktop Applications** * Tkinter * PyQT * Kivy ## **7. Scraping** - Web scraping is an idea that alludes to the way toward gathering and handling huge information from the web utilizing programming or calculation. Absolutely, scratching information from the web is a significant ability to have in case you’re an information researcher, developer, or somebody who examinations tremendous amounts of information. - Python is a successful web scrapping programming language. Essentially, you don’t have to learn muddled codes in case you’re a Python master who can do numerous information creeping or web-scratching undertakings. Notwithstanding, the three most notable and usually utilized Python systems are Requests, Scrappy, and BeautifulSoup. ## **8. Scripting** - Python is a prearranged language since it utilizes a mediator to interpret and run its code. Also, a Python content can be an order that runs in Rhino, or it very well may be an assortment of capacities that you can import as a library of capacities in different contents. - In web applications, specialists use Python as a “prearranging language.” Because it can computerize a particular arrangement of assignments and further develop execution. Accordingly, designers lean toward Python for building programming applications, internet browser destinations, working framework shells, and a few games. **Python Scripting Tools You Can Implement Easily:** - DevOps: Docker, Kubernetes, Gradle, and so on - Framework Admin ## 9. Artificial Intelligence / Data Science - Shrewd engineers consistently lean toward Python for AI because of its countless advantages. Python’s creative libraries are one of the primary motivations to pick Python for ML or profound learning. Additionally, Python’s information taking care of limits is extraordinary not with standing its speed. - Being exceptionally strong in ML and AI, Python is presently getting more foothold from different enterprises like travel, Fintech, transportation, and medical services. Tools You Can Use For Python Machine Learning: Tensorflow PyTorch Keras Scikit-learn Numpy Pandas ## 10. Ethical Hacking With Python - Ethical hacking is the way toward utilizing complex instruments and strategies to recognize potential dangers and weaknesses in a PC organization. Python, quite possibly the most well-known programming dialect because of its huge number of instruments and libraries, is additionally utilized for moral hacking. - It is so generally utilized by programmers that there are plenty of various assault vectors to consider. Additionally, it just takes little coding information, simplifying it to compose content. - Tools For Python Hacking SQL infusion Meeting seizing Man in the Middle Systems administration IP Adress Double-dealing ###### Python is a programming language that has acquired prominence and is sought after. Additionally, Python developer’s interest has soar today, requiring information science with Python preparation. Thus, on the off chance that you have the chance to participate in element-related graphs and appreciate experience altogether, this work makes you fortunate in this field of programming. ###### To close this Python developer roadmap empowers an develoepr to prevail in Python programming on the off chance that you achieve the information and an essential comprehension of the field.

This repo contains DataScience-ML_projects mostly on unexplored python libraries for Data Science, ML, and AI.

A comprehensive Python AI/ML repository covering end-to-end workflows using TensorFlow, PyTorch, Keras, and Transformers for deep learning; NumPy and Pandas for data processing; Scikit-Learn for classical ML; XGBoost and LightGBM for high-performance tabular models; and spaCy for NLP pipelines.

a Python-based AI/ML model using advanced AI techniques and libraries to detect brain tumours from MRI images.

A Snowflake-centric Enterprise AI/ML framework with the tight integration of popular Python data science libraries

Learn DS ML and AI : Directories: 1. kaggle - kaggle learning experiences. 2. compilation - compilation of all learnings (python basics, specific libraries, stats or concepts)

Bowerbirds is a Python library that helps you organize messy AI/ML projects, making them easier to understand and reuse.

UltraClean is a fast and efficient Python library for cleaning and preprocessing text data for AI/ML tasks and data processing.

A mini Project based on AI and ML , that can summarise any article and provide sentiment analysis of it.This project uses Natural Language Processing libraries of python.

14yo coding enthusiast 💻 | Passionate about Python, AI & Data Science 🚀 | Learning Machine Learning & advanced Python libraries 🤖 | Building my first ML projects soon! Excited to share my journey & connect with like-minded developers. Follow for updates! 🌟

A comprehensive collection of toolkits for essential AI/ML and Data Science libraries in Python. This toolkit is designed to help you quickly get started with scientific computing, data analysis, and artificial intelligence, machine learning & data science.

Smart-Absensi (smart attendance) adalah program yang memudahkan sekolah untuk melakukan absensi menggunakan pengenalan wajah dari murid dan langsung terekam secara sistematis.Dibuat menggunakan Python dan beberapa library ML & AI terutama deepface

Web based AI-ML project that aims to recognise the text present in Doctor's prescription with high accuracy and also suggest remedies to user based on provided textual symptoms. Tech Stack : Python, Flask, HTML, CSS, JavaScript, AJAX. Key Libraries : Google Cloud Vision, OpenAI

A collection of hands-on Jupyter notebooks covering essential Python libraries for Data Science and Machine Learning — including NumPy, pandas, Matplotlib, Seaborn, Scikit-learn, PyTorch, and TensorFlow. Each notebook demonstrates core concepts, real-world examples, and practical use cases to build strong foundations for ML and AI projects.

A Python-based agent for generating synthetic preference-ranked datasets for RLHF or DPO training. Specialized in ML/Data coding tasks using libraries like pandas, numpy, sklearn, and PyTorch. Built with Claude Code for REPPO pod publishing. Verifiable, scalable, and designed to drive real fees in decentralized AI networks.

This repository showcases a model monitoring project using Evidently AI, an open-source Python library. The project focuses on evaluating, testing, and monitoring data and machine learning models. It works with tabular data and enables data scientists and ML engineers to gain insights into model performance and data drift over time.