Machine Learning notebooks for refreshing concepts.

The full collection of Jupyter Notebook labs from Andrew Ng's Machine Learning Specialization.

Some notebooks

Notebooks from the Machine Learning Specialization

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

Colab notebooks part of the documentation of Stable Baselines reinforcement learning library

AI projects in python, mostly Jupyter notebooks.

Stable-Baselines tutorial for Journées Nationales de la Recherche en Robotique 2019

Notebooks for the Practicals at the Deep Learning Indaba 2022.

Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.

A collection of reference Jupyter notebooks and demo AI/ML applications for enterprise use cases: marketing, pricing, supply chain, smart manufacturing, and more.

Reinforcement Learning and Decision Making tutorials explained at an intuitive level and with Jupyter Notebooks

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

A collection of #MachineLearning #Python Notebooks 🤖 🐍📚 that can be launched to the ☁️ for use and experimentation. No setup needed, just launch it 🚀

Colab notebooks for the Robotic Vision Summer School

This is a repository that goes hand by hand with my Medium Series under the same name. Here, each article will be further developed, with fully working coding solutions.

Walkthrough notebooks for Deep Learning, Machine Learning, Reinforcement Learning, Spark, Statistics, Algorithms, Scala, Python

Practical notebooks for Khipu 2019, held in Universidad de la República in Montevideo.

Machine Learning Engineer Nanodegree portfolio, which includes projects and their notebooks/reports.

Succinct Machine Learning algorithm implementations from scratch in Python, solving real-world problems (Notebooks and Book). Examples of Logistic Regression, Linear Regression, Decision Trees, K-means clustering, Sentiment Analysis, Recommender Systems, Neural Networks and Reinforcement Learning.