This repository contains my weekly assignments and projects completed during the Student Internship Program at IIIT Hyderabad. The coursework is divided into several modules, each focusing on different aspects of Artificial Intelligence and Machine Learning.
- Module 0: Basics of Linear Algebra and Probability and Statistics
- Module 1: Data Preprocessing
- Module 2: Data Visualization and Exploratory Data Analysis
- Module 3: K-Nearest Neighbors (KNN) and Distance Metrics
- Module 4: Gradient Descent (Ongoing)
This module covers the foundational concepts of linear algebra and probability, including:
- Matrix operations
- Eigenvalues and eigenvectors
- Basic probability theory
- Statistical measures
In this module, I worked on various data preprocessing techniques such as:
- Data Augmentation
- Data Transformation
- Data Normalization
This module focused on data visualization techniques using matplotlib and advanced methods like:
- Principal Component Analysis (PCA)
- t-Distributed Stochastic Neighbor Embedding (t-SNE)
- Isometric Mapping (ISOMAP)
Project: An Exploratory Data Analysis project on heart.csv and star_nutri_expanded.csv.
The focus of this module was on understanding KNN and various distance metrics. Additionally, I explored text classification techniques.
Project: A Binary Classification project on diabetes data.
This module is currently ongoing, with discussions centered around the Gradient Descent optimization algorithm.
Feel free to explore the repository to see the detailed assignments and projects. Suggestions are welcome in the Issues Tab!