🚀 AI EngVentures

Welcome to the AI EngVentures repository! This repo is a collection of my explorations, projects, and insights in artificial intelligence, computer vision, image processing, and machine learning. Through various projects and topics, I aim to deepen my understanding and share my learnings in these fields.

🗂️ Contents

1. 🤖 Machine Learning

Dive into feature engineering, supervised learning, and upcoming unsupervised learning models:

• Feature Engineering: Techniques for transforming and scaling data.
• Supervised Learning: Projects covering linear regression, logistic regression, and k-nearest neighbors algorithms.
• Unsupervised Learning Projects featuring K-Means clustering, PCA (Principal Component Analysis), and more.

2. 🛠️ Projects

Real-world applications of AI and machine learning techniques:

• 🩺 Cancer Classifier: A model to predict cancer based on diagnostic data.
• 🍯 Honey Production: Predicting honey production using regression techniques.
• 💳 Credit Card Fraud Detection: Identifying fraudulent transactions with logistic regression.
• 🎾 Tennis Ace: A project exploring data analysis in sports.
• 🚩 Find the Flag!: Predict the continent of a flag using decision trees and features like colors and shapes. Dataset
• 🧰 Transforming Data into Features: Transform customer review data by scaling, encoding categorical variables, and handling date-time features. Dataset from Kaggle.
• 📝 Wrapper Methods: Use logistic regression to predict obesity and explore feature selection with wrapper methods. Dataset: Obesity Data Set.
• ✍️ Handwriting Recognition: Use K-means clustering to recognize and group images of handwritten digits, leveraging scikit-learn for clustering analysis. Dataset from scikit-learn.
• 📧 Email Similarity Classification: A project that classifies emails into categories (hardware or hockey) using the Naive Bayes algorithm. This project utilizes the fetch_20newsgroups dataset and includes functionalities for predicting email categories based on user input.
• 🍷 Predict Wine Quality with Regularization: Classify wine quality (good/bad) with logistic regression, ridge, and lasso regularization. Wine Quality Dataset
• 🌲 Random Forests Project: Predict income >$50K from census data. Dataset
• 🔧 Building ML Pipelines: Pipeline for pediatric bone marrow transplants, including preprocessing and classifier selection to predict survival.

3. 🖼️ Computer Vision and Image Processing

Explore core concepts of image processing and computer vision, covering topics such as:

• Image Processing Fundamentals (Filtering, edge detection)
• Geometric Transformations
• Pixel Transformations
• Object Detection
• Logistic Regression for Vision Tasks
• Neural Networks for Vision Applications

4. 📐 Math for AI Engineering

Understand the mathematical foundations required for AI, including:

• Linear Algebra
• Probability
• Statistics

🤝 How to Contribute

If you'd like to contribute:

1. Fork this repository.
2. Create a new branch.
3. Make your changes and submit a pull request.

I'm open to contributions and feedback, so feel free to reach out with any ideas!