Welcome to My Portfolio

In pursuing my physics minor, I took several classes focused on modern optics. Throughout these courses, I gained hands-on knowledge of Köhler Illumination, Abbe Theory of Image Formation, Fourier Optics, as well as contrast methods such as Darkfield, Phase Contrast, and Fluorescence Microscopy.

During my time in the microscopy lab, I had the opportunity to construct an optical system specifically designed for imaging small samples. This practical experience allowed me to explore various imaging techniques, including brightfield, darkfield, phase contrast, and fluorescence microscopy, and provided me with valuable insights into designing and setting up optical systems applicable to diverse fields.

Completing my minor in physics has provided me with a solid foundation in optics, along with practical skills in building and operating laboratory equipment. The knowledge and experience gained through these courses have not only deepened my understanding of electromagnetism, but also proved valuable in both physics and electrical engineering domains.

In the course "Introduction to Machine Learning and Pattern Recognition for Big Data Analytics," I delved into the fundamental concepts, theories, approaches, algorithms, and applications of machine learning in the context of big data analytics. This course provided a comprehensive understanding of the field, covering both supervised and unsupervised learning techniques and exploring learning theory.

Throughout the course, I gained practical experience in implementing machine learning algorithms using Python. I extensively utilized popular libraries such as matplotlib, pandas, numpy, and scikit-learn, which enabled me to effectively analyze and visualize data, perform data preprocessing, and apply various machine learning models.

Among the key topics covered in the course were K-Nearest Neighbors (KNN), decision trees, and ensemble methods. I learned how to construct classification and regression models using these techniques, understanding their strengths, weaknesses, and optimal usage scenarios. Additionally, the course introduced me to the field of clustering, where I explored different clustering algorithms to identify natural groupings within datasets.

Furthermore, the course touched upon Natural Language Processing (NLP), a fascinating subfield of machine learning that focuses on analyzing and understanding human language. Through NLP, I gained insights into processing and extracting meaningful information from textual data, enabling me to develop applications such as sentiment analysis, text classification, and language generation.

Lastly, the course explored the exciting realm of neural networks, where I learned about the architecture, training, and application of artificial neural networks for tasks such as image recognition, sequence processing, and prediction.

By completing this course, I have acquired a solid foundation in machine learning, enabling me to apply the learned concepts and techniques to real-world data analysis and predictive modeling challenges. The knowledge gained in Python programming, along with the practical experience of working with popular machine learning libraries, positions me well in the growing fields of Machine Learing and Artifical Intelligence.