📔 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.
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.
Exploratory Data Analysis (EDA) on the Iris dataset using Python, focusing on data visualization and statistical insights.
A foundational AI/ML data preprocessing pipeline in Python to simulate, filter, and log sensor data.
A highly efficient data preprocessing pipeline using Python and Pandas to clean, filter (via boolean indexing), and format raw sensor data for ML models.
NumPy, pandas & probability exercises on real-world datasets (Titanic, diamonds, heart failure)
Daily Machine Learning & Deep Learning practice using Python
Data Cleaning Project using Python and Pandas | Employee Dataset | Removing Duplicates, Missing Values, and Data Formatting
A simple rule-based chatbot built using Python and NLTK that demonstrates fundamental NLP techniques such as tokenization, lemmatization, cosine similarity, and response generation.
My blogs and code for machine learning. http://cnblogs.com/pinard
Data-driven analysis of IPL 2016 player and team performances using R.
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.
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.
🚀 30 Days. Zero to FAANG. Master Python with baby-level analogies, visual memory tricks, and real FAANG interview questions. No boring tutorials—only pure, addictive learning. 🔥🐍
A 10-line control surface for 135 million learned statistical relationships (and some accidental Simlish).
Beginner-friendly Python project to perform statistical analysis (mean, median, outliers, correlation, and data scaling) using NumPy.
Machine learning implementations from scratch.
Hands-on NumPy fundamentals for Data Analyst & Machine Learning roles
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