Real World ML: From Theory to Production

Learn machine learning by building it from scratch, then applying it to solve real-world problems

Overview

This repository provides comprehensive machine learning implementations built from first principles, combined with production-ready examples for real-world deployment.

Learn by Implementation: Every algorithm is built from scratch using minimal dependencies, helping you understand the mathematics and intuition behind ML/DL techniques.

Production-Ready Examples: Bridge the gap between academic understanding and real-world deployment with complete end-to-end pipelines.

Comprehensive Coverage: From classical ML to cutting-edge deep learning, covering supervised/unsupervised learning, NLP, computer vision, and reinforcement learning.

Quick Start

# Clone the repository
git clone https://github.com/Asad-Ismail/Real_World_ML.git
cd Real_World_ML

# Install dependencies
pip install -r requirements.txt

# Try a quick example
python learn/Supervised/LogisticRegression/logisticregression.py

Repository Structure

/learn/ - Algorithm Implementations

Supervised Learning /learn/Supervised/

• Decision Trees - From scratch tree building with various splitting criteria
• Ensemble Methods - Gradient boosting, random forests with custom implementations
• Linear Models - Linear/logistic regression with regularization
• Support Vector Machines - SVM implementation with different kernels
• Naive Bayes - Probabilistic classification
• k-NN - Instance-based learning

Deep Learning

• CNNs - Convolutional neural networks from scratch
• LLMs from Scratch - Transformer architecture, attention mechanisms, BPE tokenization
• Generative Models - GANs, VAEs, diffusion models, NeRF implementations

Unsupervised Learning /learn/Unsupervised/

• PCA - Principal component analysis
• t-SNE - Dimensionality reduction and visualization
• K-Means - Clustering algorithms
• Autoencoders - Neural network-based dimensionality reduction

Natural Language Processing /learn/NLP/

• BERT Implementation - Transformer-based language model
• Word2Vec - Skip-gram and CBOW implementations
• Tokenizers - Text preprocessing and tokenization

Reinforcement Learning /learn/Reinforcement_Learning/

• Q-Learning & SARSA - Temporal difference methods
• Policy Gradient - REINFORCE, Actor-Critic, A2C, SAC
• Custom Environments - Grid world and other RL environments

Specialized Topics

• Graph Neural Networks - DGL-based fraud detection pipeline
• Active Learning - Smart data labeling strategies
• Explainable AI - GradCAM, saliency maps, interpretability tools
• Time Series - Forecasting and temporal data analysis
• Probability Theory - Bayesian methods, Kalman filtering, sensor fusion

/Use_Cases/ - Production Examples

Real-Time Data Processing

• Complete Kafka + Spark streaming pipeline
• ML model inference on streaming data
• Scalable architecture for production deployment

AWS SageMaker End-to-End

• Complete fraud detection pipeline
• Model training, deployment, and monitoring
• Lambda functions for real-time inference

Spark Image Processing

• Distributed image processing with PySpark
• Scalable computer vision workflows

Learning with Less Data

• Comprehensive guide to data-efficient learning
• Transfer learning, semi-supervised, and active learning strategies
• Performance comparisons and best practices

Learning Paths

Beginner Path: Start with Fundamentals

1. Linear Regression → Logistic Regression
2. Decision Trees → Random Forest
3. K-Means → PCA

Intermediate Path: Deep Learning & NLP

1. CNNs → Generative Models
2. Word2Vec → BERT
3. LLM Components → Transformer Architecture

Advanced Path: Production & Specialized Topics

1. Real-Time Processing → AWS SageMaker Pipeline
2. Graph Neural Networks → Active Learning
3. Reinforcement Learning → Explainable AI

Technical Requirements

Core Dependencies:

• Python 3.7+
• NumPy, Matplotlib, Scikit-learn
• PyTorch (for deep learning examples)
• Additional dependencies listed in requirements.txt

For Production Examples:

• Apache Kafka (Real-time processing)
• Apache Spark/PySpark (Distributed processing)
• AWS CLI (SageMaker examples)
• Docker (Containerized deployments)

Key Features

• Educational Focus: Every implementation includes detailed comments explaining the mathematics
• From Scratch Implementation: Minimal external dependencies - understand every line of code
• Comprehensive Testing: Most implementations include test cases and validation examples
• Production Ready: Complete pipelines from data ingestion to model deployment
• Real-World Applications: Tackle fraud detection, image processing, NLP, and time series forecasting

Contributing

We welcome contributions including:

• Bug fixes and performance improvements
• Enhanced documentation and examples
• New algorithm implementations
• Additional production use cases

Please feel free to open issues and pull requests.

Additional Resources

• Detailed Explanations: Check the learning_with_less directory for comprehensive guides
• Research References: Most implementations include links to original papers and theoretical foundations
• Best Practices: Production examples demonstrate industry-standard practices and deployment patterns

Contact

For questions, suggestions, or discussions about machine learning concepts, please open an issue in this repository.