hendramarcos's Stars
microsoft/Security-101
8 Lessons, Kick-start Your Cybersecurity Learning.
YikaiZhangskye/ML
Marcussena/introtodeeplearning
Lab Materials for MIT 6.S191: Introduction to Deep Learning
ritikrajput8660/Mentorness
dr-mushtaq/Deep-Learning
This repository is a related to all about Deep Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python)
chonyy/fpgrowth_py
🔨 Python implementation of FP Growth algorithm, new and simple!
Naveenpoliasetty/Building-Neural-nets-with-numpy
Building neural networks from scratch provides a unique perspective, akin to constructing a complex structure from its bare components.
Afrah333333/deep-learning
Master deep learning
Marcussena/ML-and-Ai-from-scratch
Python implementation of machine learning and Ai algorithms from scratch
Akashdawari/Articles_Blogs_Content
This repository contain jupyter notebooks regarding to the Articles published in blogs.
hemanth5666/100_Days_Of_Data_Science_Challenge
nirdesh17/movie-recommender-system
A movie recommendation system, is an AI/ML-based approach to filtering or predicting the users’ film preferences based on their past choices and behavior. It’s an advanced filtration mechanism that predicts the possible movie choices of the concerned user and their preferences towards a domain-specific item, aka movie.
nirdesh17/House-Price-Prediction
This project demonstrates the application of machine learning techniques to predict house prices based on various features. By analyzing the dataset, preprocessing the data, and selecting an appropriate model, we were able to achieve a high level of accuracy in predicting house prices. The trained model can be further refined and deployed.
GeostatsGuy/DataScienceInteractivePython
Python interactive dashboards for learning data science
Ankush511/Human-Activity-Recognition
microsoft/IoT-For-Beginners
12 Weeks, 24 Lessons, IoT for All!
microsoft/ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
labmlai/annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
cristianleoo/models-from-scratch-python
Repo where I recreate some popular machine learning models from scratch in Python
tosmartak/deep-learning-v2-pytorch
Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101
rasbt/stat451-machine-learning-fs21
yennhi95zz/machine-learning-cheatsheets
A comprehensive collection of Machine Learning cheatsheets for quick reference and learning.
alhabibii/Project-only-For-Train
andreaschandra/house-price
Notebook for house price competition from Kaggle
Abdullah-khan0/100-DaysOf-Code-dataScience
birol97/Dynamic-traffic-light-system-using-Google-Maps
dhakanavin5/Fuzzy-based-intelligent-traffic-light-control-system
Fuzzy-based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. The algorithm has three modes i.e. Fair Mode, Priority Mode, and Congestion Mode for smooth traffic. It automatically activates and switches to the best mode based on the real-traffic conditions.
dhakanavin5/Deep-Reinforcement-Learning-based-Traffic-Light-Control
A dynamic and Intelligent Traffic Light Control System(DITLCS) is proposed which takes real-time traffic information as an input and dynamically adjusts the traffic light duration. The proposed DITLCS runs in three modes namely Fair Mode(FM), Priority Mode(PM), and Emergency Mode(EM). A deep reinforcement learning model is used to switch the traffic light in different phases (Red, Green, and Yellow) and the fuzzy inference system selects one mode among three modes i.e. FM, PM, and EM according to the Traffic Information.
PacktPublishing/Python-Machine-Learning-By-Example-Third-Edition
Python Machine Learning By Example Third Edition, published by Packt
JustSimpleLucas/Recommendation-system