Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.

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.

Data Science Feature Engineering and Selection Tutorials

Notebook image and notebook for feature reduction talk

Interactive ML Toolset

A set of notebooks that leverage classical ML algorithms and DL neural nets using TF, Keras and Theano to address a series of issues in the field of conservation and biology.

Best for beginners | Well explained ML algorithms | organized Notebooks | Case Studies

This repository contains notebooks in which I have implemented ML Kaggle Exercises for academic and self-learning purposes. In my notebooks, I have implemented some basic processes involved in ML Data Processing like How to take care of Missing Values, Handling Categorical Variables, and operations like mapping, 'Grouping', 'Sorting', 'Renaming …

Extensive Collection of Jupyter Notebooks focused on Machine Learning covering different techniques includes Feature Engineering, Feature Selection, Feature Extraction, Model Training & Testing.

Data Science Python notebooks

This repository provides a collection of Jupyter Notebook examples demonstrating various feature selection techniques using Python.

Sameer Girolkar's AIML practice Notebooks

Explore a collection of Jupyter notebooks that guide you through various stages of the machine learning pipeline. From data analysis and feature engineering to model training and deployment, these notebooks provide practical insights for both beginners and experienced data enthusiasts. Let's dive into the world of data-driven decision-making! 📊🚀"

Relationship prediction between nodes using Neo4J and Jupiter Notebook

Information gain of a car dataset was calculated in this notebook

Notebooks in this repository focus on code related to machine learning topics

This particular notebook consist of all the Feature Engineering technique and Feature Transformation technique

"A set of Jupyter Notebooks on feature selection methods in Python for machine learning. It covers techniques like constant feature removal, correlation analysis, information gain, chi-square testing, univariate selection, and feature importance, with datasets included for practical application.

This is the curated pile of notebooks/small projects which contains linear and non-linear regression models.