📔 DHBW Lecture Notes "Artificial Intelligence and Machine Learning" 🤖
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Updated
Apr 27, 2026
📔 DHBW Lecture Notes "Artificial Intelligence and Machine Learning" 🤖
Using models to understand relationships and make predictions.
A from-scratch Python perceptron project that trains and tests simple neural networks for logic gates such as AND, OR, and NOR. Includes separate training and testing scripts, along with saved trained models using pickle
This project focuses on analyzing the relationship between students’ study hours and their academic performance using basic data analysis techniques in Python. The goal is to understand how the number of hours studied affects the marks obtained by students and to visualize this relationship using graphs.
Exploratory Data Analysis (EDA) on the Iris dataset using Python, focusing on data visualization and statistical insights.
A simple rule-based chatbot built using Python and NLTK that demonstrates fundamental NLP techniques such as tokenization, lemmatization, cosine similarity, and response generation.
Daily Machine Learning & Deep Learning practice using Python
Data Cleaning Project using Python and Pandas | Employee Dataset | Removing Duplicates, Missing Values, and Data Formatting
Welcome to my Machine Learning repository! This collection is a comprehensive guide to key Machine Learning concepts, techniques, and practical implementations. I've organized the content into modules, each focusing on different aspects of Machine Learning, from foundational principles to advanced algorithms and projects.
This project is a Markov Chain-based text generator implemented in Python. It processes a given text file to build a probabilistic model of word sequences, allowing it to generate new, coherent text that mimics the style and structure of the input.
Data-driven analysis of IPL 2016 player and team performances using R.
My blogs and code for machine learning. http://cnblogs.com/pinard
Machine learning implementations from scratch.
Beginner-friendly Python project to perform statistical analysis (mean, median, outliers, correlation, and data scaling) using NumPy.
Python code for Makoto Ito's "Textbooks of Machine Learning Learning with Python (Korean Edition)". '파이썬으로 배우는 머신러닝의 교과서' 책에 실린 파이썬 코드입니다.
running knn on mnist dataset for numeric digit detection
A simple case study on sampling and confidence intervals using the Titanic dataset. The goal is to understand how well a sample can represent the whole population in a clear and easy way.
This repository contains all the basics library for machine learning.
A Python implementation of Gradient Descent for solving Multiple Linear Regression. This project demonstrates how the algorithm is used to minimize the Mean Squared Error (MSE) cost function and optimize the regression coefficients.
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