This repository contains a series of personal projects and exercises where I apply the concepts and techniques I've learned from the "Machine Learning and AI with Python" course by Harvard (offered on edX at https://learning.edx.org/course/course-v1:HarvardX+CS109xa+3T2023/home). These projects serve as hands-on practice to deepen my understanding and demonstrate my skills in Python programming, machine learning algorithms, and artificial intelligence.
Note: These projects are independent self-assigned challenges and are not part of the official course assessments. They showcase my ability to implement various machine learning models, AI techniques, and data analysis workflows using Python and related libraries.
- Machine Learning Algorithms: Applied supervised and unsupervised learning techniques, including regression, classification, clustering, and reinforcement learning.
- Python Programming: Utilized Python for data preprocessing, model building, and performance evaluation using libraries such as NumPy, pandas, scikit-learn.
- Data Analysis & Visualization: Worked with data wrangling and visualization tools to derive insights from complex datasets and present results effectively.
- Model Optimization: Tuned hyperparameters and implemented model selection strategies to improve the accuracy and efficiency of AI systems.
- Python (NumPy, pandas, scikit-learn)
- Jupyter Notebooks for experimentation and documentation
- Various datasets for training and testing machine learning models
Feel free to explore the individual project folders for detailed information on the approaches used, challenges faced, and solutions implemented.