📔 DHBW Lecture Notes "Artificial Intelligence and Machine Learning" 🤖
-
Updated
Apr 27, 2026
📔 DHBW Lecture Notes "Artificial Intelligence and Machine Learning" 🤖
Using models to understand relationships and make predictions.
Foundational Perceptron from scratch. Currently using these concepts to build an advanced, pure-Python Transformer network here: Hrishvi/ai8-transformer-from-scratch-python
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.
A highly efficient data preprocessing pipeline using Python and Pandas to clean, filter (via boolean indexing), and format raw sensor data for ML models.
Exploratory Data Analysis (EDA) on the Iris dataset using Python, focusing on data visualization and statistical insights.
Data-driven analysis of IPL 2016 player and team performances using R.
Data Cleaning Project using Python and Pandas | Employee Dataset | Removing Duplicates, Missing Values, and Data Formatting
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.
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
A foundational AI/ML data preprocessing pipeline in Python to simulate, filter, and log sensor data.
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.
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.
This repository contains all the basics library for machine learning.
Python code for Makoto Ito's "Textbooks of Machine Learning Learning with Python (Korean Edition)". '파이썬으로 배우는 머신러닝의 교과서' 책에 실린 파이썬 코드입니다.
In this repository, you'll find a set of Python exercises focused on fundamental machine learning concepts using scikit-learn library.
Add a description, image, and links to the machine-learning-basics topic page so that developers can more easily learn about it.
To associate your repository with the machine-learning-basics topic, visit your repo's landing page and select "manage topics."