ADVANCED PROGRAMMING
Complex Software Engineering Solutions
OVERVIEW
This repository contains a comprehensive collection of advanced programming projects that demonstrate my proficiency in complex algorithms, design patterns, machine learning implementations, and software architecture. Projects span multiple domains including AI, data science, distributed systems, and advanced software engineering concepts.
Collection of advanced programming projects with AI, ML, and design patterns
PROBLEM
Master advanced computer science concepts including machine learning algorithms, AI implementations, design patterns, data structures, distributed systems, and apply them to solve real-world problems effectively.
SOLUTION
Developed diverse projects showcasing AI/ML implementations using TensorFlow and Scikit-learn, implemented classic design patterns in Java, created data science solutions in Python, and built distributed systems with modern frameworks.
TECHNOLOGIES USED
KEY FEATURES
- Machine Learning models (Classification, Regression, Clustering)
- Neural Networks with TensorFlow
- Data preprocessing and analysis with Pandas
- Implementation of 23 Gang of Four design patterns
- Advanced algorithms (Graph, Dynamic Programming, Greedy)
- Distributed computing with Apache Spark
- Natural Language Processing projects
- Computer Vision implementations
- Data visualization dashboards
CHALLENGES
- Understanding complex ML algorithms from scratch
- Optimizing model performance and accuracy
- Handling large datasets efficiently
- Implementing design patterns correctly
- Debugging distributed systems
WHAT I LEARNED
- Machine Learning fundamentals and algorithms
- Deep Learning with neural networks
- Data science workflow and best practices
- Software design patterns and principles
- Advanced algorithms and data structures
- Distributed computing concepts
- Model evaluation and optimization
- Feature engineering techniques