12 weeks, 25 lessons, 50 quizzes, classic Machine Learning for all
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Updated
Sep 10, 2021 - Jupyter Notebook
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12 weeks, 25 lessons, 50 quizzes, classic Machine Learning for all
The motive behind Creating this repo is to feel the fear of mathematics and do what ever you want to do in Machine Learning , Deep Learning and other fields of AI
Plant Disease Detector Web Application
Machine Learning Concepts with Concepts
Tutorial Series (60 hour course): Essentials of computer vision
This is a repository built by the community for the community.
another yet custom data science template via cookiecutter
I will update this repository to learn Machine learning with python with statistics content and materials
`mllint` is a command-line utility to evaluate the technical quality of Python Machine Learning (ML) projects by means of static analysis of the project's repository.
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
Stress classifier with AutoML
Indian license plate detection and character extraction using deep learning and raspberry pi.
Python code source for features selection
Flutter App Build for the machine Learning model which shows sentiments of instagram user by analysing their captions
Python codes from tutorials on the Data Professor YouTube channel
A simple implementation of a portion of GCN (Kipf & Welling) that can handle graph classification.
An efficient open-source AutoML system for automating machine learning lifecycle, including feature engineering, neural architecture search, and hyper-parameter tuning.
Discord bot for coronavirus (COVID-19) , With Ai [Machine learning algorithms] integrated into it
ByteHub: making feature stores simple
Machine Learning (Easy to Hard step by step)
Social Distancing and Face Mask Detection using TensorFlow. Install all required Libraries and GPU drivers as well. Refer to README.md or REPORT for know to installation requirement
ECN 5090- Machine Learning in Economics and Finance (Python)
Draw in the air with your favorite pointer
Modeled the credit risk associated with consumer loans. Performed exploratory data analysis (EDA), preprocessing of continuous and discrete variables using various techniques depending on the feature. Checked for missing values and cleaned the data. Built the probability of default model using Logistic Regression. Visualized all the results. Computed Weight of Evidence and price elasticities.
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Currently, no fit predict is supported. Behaviour is desirable and probable would need to use Parametric UMAP base or other means as workaround.