一些常用的机器学习算法实现

This repository contains all the Lab and Assignments from Andrew NG Machine Learning Specialization Course on Coursera.

Data Mining Algorithms with C# using LINQ

𝗙𝗶𝗿𝗲 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻🔥using Machine Learning Algorithm with python🐍, GoogleColab & database taken from 𝗨𝗖𝗜 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆

Leveraging the power of Machine Learning as a tool, we delve into the realm of app permissions to discern the true nature of applications, whether they harbor malicious or benign intent. By analyzing and predicting based on these permissions, we unlock valuable insights to safeguard users in the digital landscape.

A Collection of Most Famous Machine Learning Models Implementation

Explore ML mini-projects with Jupyter notebooks. Discover predictive analysis for commercial sales, leveraging regression models such as linear regression, decision trees, random forests, lasso, ridge, and extra-trees regressor.

Nations Flags Classification & Clustering project. 🎏

Efficient Android Malware detection using Random - Protype of BA's final project (Efficient Android Malware Detection using RL) - Amit Moshe (@Amit223) & Inbar Roth (@Inbaroth) & Liad Bercovich (@liadber)

This code build up a predicting model use the Machine learning algorithms such as Decision Tree, k-Nearest Neighbors etc. on the Vehicle to predict the departure action

Text Classification that works on identifying different authors writing styles in Gutenberg Digital Books | NLP.

coding lines for Deep learning 0.1 Desicion Tree

I created this notebook to training my datascience skills, ssing different automatic learning models

Deployment of the Omdena Algeria Chapter

Uji coba model menggunakan 3 algorima machine learning untuk klasifikasi

Decision Support System Application with Machine Learning Approach in Diagnosis of Diabetes

The data is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).

我也不知道我在寫什麼，大學的一個小專案，發想是買西瓜的時候長輩都會拍打西瓜，因為糖分影響密度，使得有些西瓜聲音比較低沉(咚咚咚)，有些西瓜聲音比較高(啪啪啪)。以此概念，藉由ftt再透過機器學習的決策樹，因為西瓜太貴了，我們樣本數嚴重不足(5顆)，即便拍打原西瓜，每次輸出結果還是不一樣，實驗以失敗告終。