AI Engineering Recipes

A comprehensive collection of AI and Machine Learning implementations, from foundational concepts to real-world applications.

What You'll Find Here

This repository is your gateway to practical AI engineering. You'll explore foundational AI concepts and then dive deep into building real-world GenAI applications. From there, the book guides you into the realm of Agentic AI, detailing how to design intelligent agents capable of perception, reasoning, planning, decision-making, and dynamic collaboration.

Repository Structure

Core AI & Python Lab

• ai-lab-python/ - Your AI laboratory with Python notebooks, experiments, and utilities
• foundations-ai-ml/ - Symbolic AI, rule-based systems, expert systems, and reasoning

Machine Learning Fundamentals

• understanding-ml/ - Core ML concepts, supervised/unsupervised learning, overfitting/underfitting
• decision-trees-random-forests/ - Tree-based models and ensemble methods
• linear-logistic-regression/ - Linear and logistic regression implementations
• knn-svm-naive-bayes/ - KNN, SVM, and Naive Bayes with visualizations

Data Science & Engineering

• working-with-data/ - Data collection, preprocessing, EDA, feature engineering, and handling imbalanced datasets

Real-World Applications

• Credit Scoring & Fraud Detection - Anomaly detection systems
• E-commerce Recommendations - Collaborative filtering and similarity metrics
• Healthcare Diagnosis - Disease prediction models
• Spam Detection - NLP-based classification systems
• Currency Price Prediction - Foreign exchange rate forecasting using Random Forest
• Price Comparison Analysis - E-commerce price analysis and comparison systems

Advanced ML Projects

• ml-model-currency-price-prediction/ - Machine learning model for predicting USD to EUR exchange rates
• ml-model-price-comparison/ - Comprehensive price comparison and analysis system for e-commerce

Visual Learning Resources

• Flow Diagrams and Visualisations/ - Comprehensive diagrams explaining AI concepts, decision boundaries, and learning processes

Getting Started

Prerequisites

• Python 3.8+
• Basic understanding of Python programming
• Interest in AI and Machine Learning

Quick Start

1. Clone the repository

   git clone https://github.com/muhammadawaisshaikh/ai-engineering.git
   cd ai-engineering

2. Start with AI Lab Python

   cd ai-lab-python
   pip install -r requirements.txt
   python main.py

3. Explore Jupyter Notebooks

   jupyter notebook notebooks/intro_experiments.ipynb

4. Try Currency Prediction

   cd ml-model-currency-price-prediction
   python main.py

5. Run Price Comparison Analysis

   cd ml-model-price-comparison
   python main.py

Learning Path

Phase 1: Foundations

• Begin with ai-lab-python/ to set up your environment
• Explore foundations-ai-ml/ for symbolic AI concepts
• Understand basic ML concepts in understanding-ml/

Phase 2: Core ML

• Master decision-trees-random-forests/
• Implement linear-logistic-regression/
• Visualize knn-svm-naive-bayes/ decision boundaries

Phase 3: Data Mastery

• Learn data preprocessing in working-with-data/
• Master EDA and feature engineering
• Handle real-world data challenges

Phase 4: Applications

• Build fraud detection systems
• Create recommendation engines
• Develop healthcare diagnosis models
• Predict currency exchange rates
• Analyze e-commerce pricing strategies

Phase 5: Advanced Projects

• Implement time series forecasting for financial data
• Build comprehensive data analysis pipelines
• Create interactive visualizations and dashboards

Key Technologies & Libraries

• Core ML: scikit-learn, numpy, pandas
• Visualization: matplotlib, seaborn
• Data Processing: pandas, numpy
• Time Series: pandas datetime functionality
• Jupyter: Interactive notebooks for learning
• Python: Clean, readable implementations

What Makes This Special

• Hands-on Learning: Every concept has working code examples
• Real-world Focus: Applications you can actually use
• Financial Applications: Currency prediction and price analysis systems
• Visual Learning: Flow diagrams and decision boundary visualizations
• Progressive Complexity: Start simple, build up to advanced concepts
• Production Ready: Code that follows best practices

Featured Projects

Currency Price Prediction

• Predicts USD to EUR exchange rates using Random Forest
• Handles time series data with lag features
• Includes data preprocessing and visualization
• Perfect for learning financial time series modeling

Price Comparison Analysis

• Comprehensive e-commerce price analysis
• Data preprocessing and exploratory analysis
• Model building and evaluation
• Real-world business intelligence application

Credit Scoring & Fraud Detection

• Anomaly detection systems
• Real-time fraud monitoring
• Credit risk assessment models

Healthcare Diagnosis

• Disease prediction using ML
• Medical data analysis
• Healthcare AI applications

Contributing

We welcome contributions! Whether it's:

• Improving existing implementations
• Adding new algorithms
• Enhancing documentation
• Creating new visualizations
• Adding new real-world applications

Please feel free to open issues or submit pull requests.

Authors

• Muhammad Awais - Main Author & AI Engineer
• Robina Mirbahar - Co-Author & ML Specialist
• Dr. Mohammed Ziyad - Foreword Writer & AI Researcher

Publication

• First Published: September 2025
• Last Updated: Ongoing development
• License: Open source for educational use

Star This Repository

If you find this repository helpful for your AI learning journey, please give it a star! It helps others discover these valuable resources.

Connect With Us

• GitHub: muhammadawaisshaikh/ai-engineering
• LinkedIn: Muhammad Awais
• LinkedIn: Robina Mirbahar
• LinkedIn: Dr. Mohammed Ziyad

---

Happy Learning!

This repository is designed to be your comprehensive guide to AI engineering. Start anywhere, learn at your own pace, and build something amazing!