Code Repository for Machine Learning with PyTorch and Scikit-Learn

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

The projects I do in Machine Learning with PyTorch, keras, Tensorflow, scikit learn and Python.

A complete course on AI and machine learning, featuring Python-based tutorials, projects, and datasets covering algorithms, neural networks, and real-world applications. Designed for beginners to advanced learners, with hands-on exercises in Scikit-learn, TensorFlow, and PyTorch.

Machine learning recipes in Python with scikit-learn, OpenCV, PyTorch, and other libraries, including classical machine learning and neural networks, based on the book "Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning", Second Edition, by Kyle Gallatin and Chris Albon published by O'Reilly Media in 2023

Distributionally robust machine learning with Pytorch and Scikit-learn wrappers

Machine Learning Experiments with scikit-learn, Deep learning with Keras, TensorFlow and Pytorch. Data Science examples for various datasets and competitions from Kaggle and Analytics Vidhya.

This repository is used for synchronized storage of code records for the corresponding chapters of the book - Machine Learning with PyTorch and Scikit-Learn.

Jupyter Notebooks to accompany our book Machine Learning for Biomedical Applications with Scikit-Learn and PyTorch

My Notes from Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka

Tutorial for training PyTorch and Scikit-Learn machine learning models, converting them to onnx format, inferencing the converted model with onnxruntime and finally deploying them to Azure.

Here I will keep track of all the exercises and contents learned while reading the book "Hands-On Machine Learning with Scikit-Learn and PyTorch" by Aurélien Geron.

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.

# 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 repository showcases my practice with machine learning models from Machine Learning with PyTorch and Scikit-Learn by Sebastian Raschka. I updated outdated packages with ChatGPT and included detailed notes. It features various techniques, highlighting my skills and problem-solving abilities. Explore and reach out for feedback or discussion.

these are my worked notebooks from Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Study Notes & Experiments for Hands-On Machine Learning with Scikit-Learn & PyTorch by Aurélien Géron

A practical journey into machine learning and deep learning with hands-on examples using Scikit-Learn and PyTorch.

PyTorch implementations of neural networks from Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'.

A portfolio of machine learning projects demonstrating skills in Python, Scikit-learn, and data science, with a focus on expanding into deep learning with TensorFlow and PyTorch.

Machine learning and deep learning projects (Projects and course assignments implemented in Python with ML/DL libraries: Scikit-learn, Tensorflow, and Pytorch by Hao Zhao)

This IBM Professional Certificate covers machine and deep learning with Python, using SciPy, Scikit-Learn, Keras, PyTorch, and TensorFlow to solve real-world problems through labs and projects.

An end-to-end cybersecurity platform that leverages machine learning and deep learning to detect, classify, and respond to network threats in real time. Supports NSL-KDD, CICIDS2017/2018, UNSW-NB15, and Bot-IoT datasets. Built with Python, FastAPI, Streamlit, PyTorch, LightGBM, Scikit-Learn, and Docker for reproducible deployment.

Discover Azure AI—a portfolio of AI services designed for developers and data scientists. Take advantage of the decades of breakthrough research, responsible AI practices, and flexibility that Azure AI offers to build and deploy your own AI solutions. Access high-quality vision, speech, language, and decision-making AI models through simple API calls, and create your own machine learning models with tools like Jupyter Notebooks, Visual Studio Code, and open-source frameworks like TensorFlow and PyTorch.Only Azure empowers you with the most advanced machine learning capabilities. Quickly and easily build, train, and deploy your machine learning models using Azure Machine Learning and Azure Databricks. Use the latest tools like Jupyter and Visual Studio Code, alongside frameworks like PyTorch Enterprise, TensorFlow, and Scikit-Learn. Expand your data science teams and create models faster with low-code and no-code tools like automated machine learning and a drag-and-drop interface.

Machine Learning with PyTorch and Scikit-Learn

学习 《machine-learning-with-pytorch-and-scikit-learning》

Book: Machine Learning with PyTorch and Scikit-Learn

[Book] Machine Learning with PyTorch and Scikit-Learn

Various machine learning models implemented with Numpy, PyTorch and SciKit Learn.

My complete code and learning progression through the "Hands-On Machine Learning with Scikit-Learn and PyTorch" book.