This repository contains examples of popular machine learning algorithms implemented in Python with mathematics behind them being explained.

User needs to request predictions by executing a Python script. Uses Flask and Gunicorn. Serving a recurrent neural network (RNN) through a HTTP webpage, complete with a web form, where users can input parameters and click a button to generate text based on the pre-trained RNN model. Uses Flask, Jinja, Keras/TensorFlow, WTForms. Implementing some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator, and making it better.

Python implementations of some of the fundamental Machine Learning models and algorithms from scratch.

This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.

This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries.

machine-learning deep-learning machine-learning-algorithms artificial-intelligence software-engineer ... 🤖 Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained

For each algorithm there will be a notebook test document and a clean python script. The algorithms implemented in this repository include: 1. Adaboost 2. Adaptive Projected Subgradient Method (APSM) 3. Convolutional Neural Network (CNN) 4. Compressed Sensing Matching Pursuit (CSMP) 5. Decision tree 6. Fuzzy C Means 7. Hierarchical and DBSCAN Clustering 8. Iterative Shrinkage/Thresholding (IST) algorithms 9.

Python codes for common Machine Learning Algorithms

Python Machine Learning Algorithms . Contribute to GeorgeSeif/Python-Machine-Learning development by creating an account on GitHub.

A collection of minimal and clean implementations of machine learning algorithms. This project is targeting people who want to learn internals of ml algorithms or implement them from scratch.

March 23, 2026 - scikit-learn: machine learning in Python. Contribute to scikit-learn/scikit-learn development by creating an account on GitHub.

The accompanying code for the book "Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits". A practical guide to implementing supervised and unsupervised machine learning algorithms in Python by Tarek Amr

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly. - lukasmasuch/best-of-ml-python

python machine-learning algorithm neural-network scikit-learn regression classification python-machine-learning support-vector-machines

Machine Learning algorithm implementations from scratch. - patrickloeber/MLfromscratch

1 week ago - >>> from river import compose >>> from river import linear_model >>> from river import metrics >>> from river import preprocessing >>> model = compose.Pipeline( ... preprocessing.StandardScaler(), ... linear_model.LogisticRegression() ... ) >>> metric = metrics.Accuracy() >>> for x, y in dataset: ... y_pred = model.predict_one(x) # make a prediction ... metric.update(y, y_pred) # update the metric ... model.learn_one(x, y) # make the model learn >>> metric Accuracy: 89.28% Of course, this is just a contrived example. We welcome you to check the introduction section of the documentation for a more thorough tutorial. River is intended to work with Python 3.11 and above.

There are no particular mathematical prerequisites; however, to fully understand all the algorithms, it's important to have a basic knowledge of linear algebra, probability theory, and calculus. Chapters 1 and 2 do not contain any code as they cover introductory theoretical concepts. All practical examples are written in Python and use the scikit-learn machine learning framework, Natural Language Toolkit (NLTK), Crab, langdetect, Spark, gensim, and TensorFlow (deep learning framework).

If you want to see introductory codes to flask, visit my repository: https://github.com/madhurimarawat/Machine-Learning-Projects-In-Python · Streamlit - This framework is used to create website using python without having to worry about frontend. I deployed my ML models that I made in this repository using streamlit: Visit Website from : ML Algorithms on Inbuilt and Kaggle Datasets To See codes: https://github.com/madhurimarawat/ML-Model-Datasets-Using-Streamlits Also if you want to see introductory codes to streamlit, visit my repository: https://github.com/madhurimarawat/Streamlit-Programs

Machine Learning algorithms implementation in Python from scratch.

This repository contains a collection of commonly used machine learning algorithms implemented in Python/Numpy.